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Genomic Data Science

As humans dig deeper into the genome, the analysis and interpretation of the genomic data collected are helping to better understand human health and disease, while also bringing up questions about privacy and ethics.

The Big Picture

• Genomic data science is a field of study that enables researchers to use powerful computational and statistical methods to decode the functional information hidden in DNA sequences.  
• Estimates predict that genomics research will generate between 2 and 40 exabytes of data within the next decade.  
• Our ability to sequence DNA has far outpaced our ability to decipher the information it contains, so genomic data science will be a vibrant field of research for many years to come.  
• Performing genomic data science carries with it a set of ethical responsibilities, as each person's sequence data are associated with issues related to privacy and identity.

How it affects you

As biomedical research projects and large-scale collaborations grow rapidly, the amount of genomic data being generated is also increasing, with roughly 2 to 40 billion gigabytes of data now generated each year. Researchers are working to extract valuable information from such complicated and large datasets so they can better understand human health and disease.

What is genomic data science?

Genomic data science is a field of study that enables researchers to use powerful computational and statistical methods to decode the functional information hidden in DNA sequence. Applied in the context of genomic medicine, these data science tools help researchers and clinicians uncover how differences in DNA affect human health and disease.

Genomic data science emerged as a field in the 1990s to bring together two laboratory activities:

• Experimentation: Generating genomic information from studying the genomes of living organisms.  
• Data analysis: Using statistical and computational tools to analyze and visualize genomic data, which includes processing and storing data and using algorithms and software to make predictions based on available genomic data.  

Both activities help researchers acquire and gain insights from the vast amounts of genomic data.

Why does genomics involve so much data?

Human genomics gained mainstream attention in the early 2000s when the Human Genome Project successfully generated the first sequence of the chemical bases (“letters”) —  As, Cs, Gs and Ts — in the human genome. Each of the trillions of cells in the human body contains a complete copy of the genome, i.e., our DNA blueprint). Most cells actually have two copies of the genome, which together reflect about 6 billion DNA letters.

Researchers are now generating more genomic data than ever before to understand how the genome functions and affects human health and disease. These data are coming from millions of people in various populations across the world. Data about a single human genome sequence alone would take up 200 gigabytes, or the space of about 200 copies of Jaws . We will need an estimated 40 exabytes to store the genome- sequence data generated worldwide by 2025. That’s almost one billion DVDs of Jaws ! In comparison, five exabytes could store all of the words ever spoken by human beings.

Because of the sizeable quantity of complex data associated with human genomes, genomics is now considered a "big data" field.

How do scientists study and use genomic data?

Researchers need special computational and analysis tools to find and interpret biological information hidden within the DNA of each person and also to manage the large volumes of data generated in genomics research projects.

Researchers use software tools called aligners to determine where individual pieces of DNA sequence lie on each part of a reference genome sequence.

Next, "variant callers" identify the places where a given human genome sequence differs from other human genome sequences. These genomic differences come in many sizes. The difference may be as small as one DNA letter (called a single-nucleotide polymorphism ), many letters long (called structural variants ) such as insertions or deletions , or substantially larger chromosomal abnormalities. These genomic differences may present no health risks, or they can directly cause inherited rare disorders, cancers or other more common diseases.

How do researchers manage and store such high volumes of genomic data?

Experts in both computer technologies and genomics manage and store genomic data by using various computer systems and software. Increasingly, data analysis and coordination centers are part of research networks and provide these services. 

Generating genomic data requires significant financial support from institutions such as the National Human Genome Research Institute (NHGRI), which provides over $125 million each year to support various genomic data science endeavors.

The generated data resources are often made available to the broader scientific community to facilitate further data analyses. They organize and provide many types of information about the human genome, such as the locations of genes and variants in the DNA.

Many private and commercial cloud platforms work in collaboration with governmental and public entities, such as at the National Institutes of Health (NIH) through the STRIDES initiative. These initiatives provide storage and computing infrastructure for hosting genomic data and to provide the necessary security and privacy protections for human genomic data in particular.

What are some of the ethical, legal and societal implications of genomic data sharing?

Performing genomic research carries with it a set of ethical responsibilities, as information about a person's genome sequence is associated with complex issues related to privacy and identity.  

• Informed Consent:  Researchers usually ask for consent from individuals whose genomes are sequenced. But researchers must provide clear information about how they will use and share the resulting genome-sequence data in the process of gaining such informed consent.  
• Privacy:  Powerful computational tools can take sequence data from de-identified genomes and, under special circumstances, connect them back to the person whose DNA was sequenced. Investigators can use such tools for useful purposes, such as identifying criminals who left behind DNA at a crime scene. But the societal benefits must outweigh the potential risks of using genomic data in this way.  
• Artificial Intelligence (AI):  AI tools increasingly help researchers process vast quantities of genome-sequence data to look for hidden patterns in DNA. However, because AI algorithms often lack transparency, biases can creep in undetected when such algorithms are applied to DNA data. This area of genomic data science will need extensive ethics research to navigate the unique differences between current methods in genomic data science (which rely on human intelligence for interpretation of the results) and newer AI methods. While AI methods offer many promising advantages, they also draw conclusions in completely different ways than humans do, and hence need to be subject to careful ethics oversight.

With all these considerations, data scientists and genomics researchers must be educated about the implications of their studies and work closely with ethics researchers.

How do researchers share human genomic data?

Researchers are expected to share human genomic data according to the consent provided by the research participants. Genomic data are typically shared with the scientific community through data resources, which can be accessed in three ways: 

Open-access or unrestricted access is the broadest form of data sharing. Data are available to the public for any research purpose. 

Registered access falls in between open-access and controlled-access. Researchers can obtain the data for any purpose; however, they must register their information, and their work with the data may need to be monitored.

Controlled-access data sharing requires researchers to describe their research purpose so that a special data access committee can evaluate the consistency of the research purpose with the participant’s consent. The researcher can only access the data after receiving the committee's approval.

What are some emerging topics in genomic data science?

Human genomes contain many genomic variants (DNA letters that differ in particular places among individuals). Healthcare systems and researchers are building tools that identify these DNA differences and link them to medically relevant information, such as a risk for disease or an indication for a specific medication among several options. Researchers also use artificial intelligence systems to interpret genomic data for clinical purposes, such as diagnosing diseases at early stages or predicting risk for different diseases using genomic information .

In the last decade, cloud computing has become necessary for genomic data storage and analyses. Cloud computing decreases the need to duplicate large datasets, increases security and provides researchers more accessibility to genomic data science. Data scientists are creating tools to make data upload easier and to ensure privacy.

What is NHGRI’s role in working towards a more diverse and equitable workforce?

Women and various minorities groups have been largely underrepresented in genomic data science. NHGRI believes that it is critical to expand and enhance the diversity of the genomic data science workforce. Recent analyses show a significant lack of ethnic and gender diversity among data scientists, trainees and genomics researchers across US institutions.

NHGRI is making changes to enhance the presence of women and underrepresented group in the genomic data science workforce. Through NHGRI-funded training programs and by bringing larger numbers of people in the field of genomic data science, the Institute hopes to foster a more inclusive data science workforce in genomics and beyond.

Additional Resources

To learn more about NHGRI's involvement in genomic data science and data science activities at NIH, see the following resources: 

Last updated: April 5, 2022

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Genomic data analysis: a beginner’s step-by-step guide with python and r examples.

In the current biotechnology and medicine era, genomic data analysis is the compass guiding researchers through our genetic code.

This comprehensive guide aims to explain genomic data analysis along with Python and R coding , breaking down each step into digestible portions.

Before diving into the nitty-gritty of genomic data analysis, let’s grasp the concept of genomic data, its formats and significance of genomic data analysis.

Explore more about role of 20 Essential Python Codes for Bioinformatics Beginners.

What is Genomic Data?

Genomic data is the code embedded in DNA sequences of living organisms, serving as the fundamental blueprint of life. It contains the entire set of genetic instructions that govern the development, functioning, and characteristics of an organism. This data includes the sequence of nucleotide bases – adenine (A), thymine (T), cytosine (C), and guanine (G) – arranged in a specific order, forming genes that act as the building blocks of our biological existence.

Genomic data is the molecular language that scientists decode to understand the inherited traits, the origins of diseases, and the evolutionary paths of diverse species

What are Common Genomic Data Formats?

Common genomic data formats include FASTQ, which holds raw sequence data, and BAM, a binary version of the Sequence Alignment/Map (SAM) format used for storing aligned sequence reads. Variant Call Format (VCF) is another vital format, specifically designed to represent genetic variations detected during genomic data analysis.

What is Genomic Data Analysis and Why it is Important?

Genomic data analysis is the systematic examination of the vast amount of genetic information contained within an organism’s DNA. It involves employing computational techniques and specialized tools to explore the genomic code.

The primary goal of genomic data analysis is to extract meaningful insights from the genetic data, understanding the functions of genes, identifying variations, and exploring the relationships between different elements within the genome. This process serves as a crucial bridge between the raw genetic information and actionable knowledge. It allows scientists, researchers, and healthcare professionals to comprehend the genetic basis of various phenomena, such as inherited traits, diseases, and evolutionary patterns. In essence, genomic data analysis provide a deeper understanding of life’s molecular intricacies and pave the way for advancements in medicine, biology, and genetics.

What Steps are Involved in Genomic Data Analysis?

Genomic data analysis involves a number of steps which are explained here in detail with python and R coding examples

Step 1: Gathering Genomic Data

Think of genomic data as the raw material for our genetic detective work. This data comes in various formats – FASTQ, BAM, or VCF. Imagine stepping into a vast library of genetic information, akin to NCBI’s Sequence Read Archive (SRA), where scientists worldwide deposit their genomic findings. Let’s say you’re interested in studying genetic variations in breast cancer. You’d download the relevant genomic data from SRA or collaborate with a renowned research institution specializing in cancer genomics.

Example: You access the SRA database and download genomic data from a study on breast cancer patients.

Step 2: Data Preprocessing

Now that we have our raw data, it’s time to clean it up – think of it as preparing a canvas before painting. This step, known as data preprocessing, involves removing noise, correcting errors, and ensuring the data’s overall quality.

Example: In our breast cancer study, you would eliminate low-quality reads, ensuring that the remaining data is reliable for subsequent analysis.

Step 3: Read Alignment

Reading the genomic sequence is the next step in genomic data analysis. Aligning the cleaned genomic data to a reference genome helps identify variations and understand the larger genomic landscape. It is like fitting puzzle pieces together to reveal the bigger picture.

Example: Aligning the genomic data of an individual with Asian ancestry to a reference genome based on individuals of European descent might unveil unique genetic variations specific to the Asian population.

Step 4: Variant Calling

Variant calling involves identifying genetic variations – the differences between the aligned reads and the reference genome. Such as spotting single nucleotide polymorphisms (SNPs) and insertions/deletions (indels), by comparing aligned reads to the reference genome are common examples in genomic data analysis

Example: Identifying a single nucleotide polymorphism (SNP) in the BRCA1 gene can provide crucial information regarding breast cancer susceptibility.

Know more about R Bioinformatics Cheat Sheet for Beginners here.

Step 5: Variant Annotation

Think of variant annotation as adding footnotes to the genomic text. It involves understanding the functional significance of identified variants and their potential impact on genes. It’s like deciphering the meaning behind the words in a book.

Example: Discovering a variant in a tumor suppressor gene may indicate a higher cancer risk, providing valuable insights for potential therapeutic strategies.

Step 6: Data Visualization

Now that we have our genomic data and insights, it’s time to visualize the findings. Visualization is the art of transforming raw data into meaningful stories. It is necessary step for genomic data analysis to gain insights and communicate findings effectively.

Example: Plotting the coverage across the genome could reveal regions with higher or lower sequencing depth, guiding further exploration.

Conclusion:

Genomic data analysis is a multifaceted process that involves acquiring, preprocessing, aligning, calling variants, annotating, and visualizing data. The seamless integration of Python and R in this guide provides a versatile toolkit for researchers and data scientists in the field. Mastering these steps and techniques not only enhance understanding of genomics but also contribute to advancements in the ever-evolving field of biological data science genomic research.

Click here to explore 11 Python Packages for Biological Data and Cheat Sheet.

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Data Analysis for Genomics

Drive your career forward.

This HarvardX professional certificate program gives learners the necessary skills and knowledge to tackle real-world data analysis challenges.

What You'll Learn

Advances in genomics have triggered fundamental changes in medicine and research. Genomic datasets are driving the next generation of discovery and treatment, and this series will enable you to analyze and interpret data generated by modern genomics technology.

Using open-source software, including R and Bioconductor, you will acquire skills to analyze and interpret genomic data. These courses are perfect for those who seek advanced training in high-throughput technology data. Problem sets will require coding in the R language to ensure mastery of key concepts. In the final course, you’ll investigate data analysis for several experimental protocols in genomics.

Enroll now to unlock the wealth of opportunities in modern genomics.

The course will be delivered via edX and connect learners around the world. After completing this series, you will understand how to:

• Bridge diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing
• Use advanced techniques to analyze genomic data.
• Structure, annotate, normalize, and interpret genome-scale assays.
• Analyze data from several experimental protocols, using open-source software, including R and Bioconductor.

Courses in this Program

2–4 hours per week, for 4 weeks The structure, annotation, normalization, and interpretation of genome scale assays.

2–4 hours per week, for 5 weeks Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

2–4 hours per week, for 4 weeks Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

Your Instructor

Rafael Irizarry

Professor of Biostatistics at Harvard University Read full bio.

Michael Love

Assistant Professor, Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health Read full bio.

Vincent Carey

Professor, Medicine at Harvard Medical School Read full bio.

Job Outlook

• R is listed as a required skill in 64% of data science job postings and was Glassdoor’s Best Job in America in 2016 and 2017. (source: Glassdoor)
• Companies are leveraging the power of data analysis to drive innovation. Google data analysts use R to track trends in ad pricing and illuminate patterns in search data. Pfizer created customized packages for R so scientists can manipulate their own data.
• 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. (source: Kaggle, 2017)
• Data Scientists are few in number and high in demand. (source: TechRepublic)

Ways to take this program

When you enroll in this program, you will register for a Verified Certificate for all 3 courses in the Professional Certificate Series. 

Alternatively, learners can Audit the individual course for free and have access to select course material, activities, tests, and forums. Please note that Auditing the courses does not offer course or program certificates for learners who earn a passing grade.

Genomics Data Analysis

Learn advanced techniques to analyze genomics data

Associated Schools

Harvard T.H. Chan School of Public Health

What you'll learn.

Advanced techniques to analyze genomic data

How to structure, annotate, normalize, and interpret genome-scale assays

How to bridge diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing

How to analyze data from several experimental protocols, using open source software, including R and Bioconductor

About this series

The Genomics Data Analysis XSeries is an advanced series that will enable students to analyze and interpret data generated by modern genomics technology.

Using open-source software, including R and Bioconductor, you will acquire skills to analyze and interpret genomic data.

This XSeries is perfect for those who seek advanced training in high-throughput technology data. Problem sets will require coding in the R language to ensure learners fully grasp and master key concepts. The final course investigates data analysis for several experimental protocols in genomics.

This series includes

Case Studies in Functional Genomics

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Introduction to Bioconductor

The structure, annotation, normalization, and interpretation of genome scale assays.

Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

Instructors

Rafael Irizarry

Michael Love

Vincent Carey

Join our list to learn more.

Genome Data Analysis

• © 2019
• Ju Han Kim 0

Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea (Republic of)

You can also search for this author in PubMed   Google Scholar

• Describes recent advances in genomics and bioinformatics
• Provides numerous examples of genome data analysis
• Meets the needs of life scientists, medical scientists, and others who are new to the field of bioinformatics

Part of the book series: Learning Materials in Biosciences (LMB)

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Table of contents (21 chapters)

Front matter, bioinformatics for life and personal genome interpretation, bioinformatics for life, next-generation sequencing technology and personal genome data analysis, personal genome data analysis, personal genome interpretation and disease risk prediction, advanced microarray data analysis, gene expression data analysis, gene ontology and biological pathway-based analysis, gene set approaches and prognostic subgroup prediction, microrna data analysis, network biology, sequence, pathway and ontology informatics, motif and regulatory sequence analysis, molecular pathways and gene ontology, biological network analysis, snps, gwas and cnvs, informatics for genome variants, snps, gwas, cnvs: informatics for human genome variations, snp data analysis.

• Genome data analysis
• Bioinformatics
• Practice in data science
• Statistics using R
• Clinical informatics

About this book

This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases.

The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.

Authors and Affiliations

About the author.

Professor. Ju Han Kim, Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul , South Korea.

Bibliographic Information

Book Title : Genome Data Analysis

Authors : Ju Han Kim

Series Title : Learning Materials in Biosciences

DOI : https://doi.org/10.1007/978-981-13-1942-6

Publisher : Springer Singapore

eBook Packages : Biomedical and Life Sciences , Biomedical and Life Sciences (R0)

Copyright Information : Springer Nature Singapore Pte Ltd. 2019

Softcover ISBN : 978-981-13-1941-9 Published: 10 May 2019

eBook ISBN : 978-981-13-1942-6 Published: 30 April 2019

Series ISSN : 2509-6125

Series E-ISSN : 2509-6133

Edition Number : 1

Number of Pages : XVI, 367

Number of Illustrations : 409 b/w illustrations, 236 illustrations in colour

Topics : Bioinformatics , Biomedicine general , Statistics for Life Sciences, Medicine, Health Sciences

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The value of genomic analysis

Genetic heritability is responsible for 30% of individual health outcomes , but is hardly used to guide disease prevention and care. Each individual carries 4-5 million genetic variants, each with varying influence on traits related to our health. The cost to sequence a genome has reduced drastically in recent years, and sequence data shows potential for ubiquitous use. However, the ability to read the sequence accurately and to meaningfully interpret it remain obstacles to broad adoption.

Improving the accuracy of genomic analysis

Sequencing genomes enables us to identify variants in a person’s DNA that indicate genetic disorders such as an elevated risk for breast cancer.

Highly accurate genomes with deep neural networks

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. As published in Nature Biotechnology , DeepVariant, an open-source variant caller that uses a deep neural network to call genetic variants from next-generation DNA sequencing data, significantly improves the accuracy in identifying variant locations, reducing the error rate by more than 50%. Learn more

Winner in PrecisionFDA V2 Truth Challenge

DeepVariant won awards for Best Overall accuracy in 3 of 4 instrument categories in the PrecisionFDA V2 Truth Challenge. Compared to previous state-of-the-art models, DeepVariant v1.0 significantly reduces the errors for widely-used sequencing data types, including Illumina and Pacific Biosciences. Read the article

Identifying disease-causing variants in cancer patients

Researchers wanted to understand if incorporating automated deep learning technology would improve the detection of disease-causing variants in patients with cancer. In a cross-sectional study published in JAMA of 2,367 prostate cancer and melanoma patients in the US and Europe, DeepVariant found disease-causing variants in 14% more individuals than prior state-of-the-art methods.

Building large-scale cohorts for genetic discovery research

Large cohorts of sequenced individuals are the foundations for discovery of novel genetic associations with disease. We developed best practices for generating cohorts that substantially improves over previous methods, which has been adopted by the UK Biobank for its large-scale sequencing efforts. Read the article

Improving genetic association discovery with machine learning

Discovering genetic variants associated with a trait of interest requires a large cohort of individuals with both genetic and trait information. As published in AJHG , we demonstrate that using a machine learning model to predict eye-disease-related traits from fundus images significantly improves discovery of genetic variants influencing those traits.

Our partners in genomics research

Because genomic data is highly personal, to the greatest extent possible we use datasets that are fully public or are broadly available to qualified researchers. We also partner with trusted organizations that contribute scientific and technology development to improve standards in genomic analysis and enhance the utility of sequencing data.

DeepVariant’s precisionFDA Truth Challenge V2 submission using PacBio HiFi reads achieved the highest single-technology accuracy, which has been featured on the PacBio blog and in a Nature Biotechnology retrospective . The collaboration also successfully launched DeepConsensus , which improves HiFi yield and read quality compared to existing consensus basecalling methods.

The Regeneron Genetics Center, one of the world’s largest human genomic research efforts, has adopted DeepVariant and re-trained specialized models for both internal projects and the delivery of 200,000 exomes to UKBiobank .

Benedict Paten ’s lab at UC Santa Cruz collaborated with Google on PEPPER-DeepVariant , which won best accuracy in the Oxford Nanopore Technologies category of the PrecisionFDA . The paper was also published in Nature Methods .

NVIDIA Clara Parabricks Pipelines software provides a suite of accelerated bioinformatic tools to support DNA and RNA applications, running on a GPU. Their implementation of DeepVariant processes a 30x whole human genome in less than 25 minutes from fastq to vcf using their latest A100 GPU.

GenapSys trained a custom DeepVariant model to provide a highly accurate variant caller for their new high accuracy, low cost, benchtop sequencing instrument.

ATGenomix builds a Spark framework which efficiently parallelizes DeepVariant , for their work with several clinical partners.

DNAnexus provides a secure and collaborative fit-for-purpose bioinformatics system that integrates cutting-edge tools like DeepVariant. They work with industry leaders like Google, the FDA, and UK Biobank to provide solutions to the scientific community.

DNAstack enables researchers to organize, share, and analyze genomics and biomedical data, using tools like DeepVariant, in an easy to use cloud environment. DNAstack's software products use open standards developed by the Global Alliance for Genomics & Health.

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Genome sequencing guide: An introductory toolbox to whole‐genome analysis methods

Alexis n. burian.

1 Department of Biology, Ithaca College, Ithaca New York, USA

2 Department of Biology, Davidson College, Davidson North Carolina, USA

Te‐Wen Lo

Deborah m. thurtle‐schmidt, associated data.

To fully appreciate genetics, one must understand the link between genotype (DNA sequence) and phenotype (observable characteristics). Advances in high‐throughput genomic sequencing technologies and applications, so‐called “‐omics,” have made genetic sequencing readily available across fields in biology from applications in non‐traditional study organisms to precision medicine. Thus, understanding these tools is critical for any biologist, especially those early in their career. This comprehensive review discusses the chronological development of different sequencing methods, the bioinformatics steps to analyzing this data, and social and ethical issues raised by these techniques that must be discussed and evaluated, including anticipatory guides and discussion questions for active engagement in the classroom. Additionally, the Supporting Information includes a case study to apply technical and ethical concepts from the text.

1. INTRODUCTION

Since DNA was established as the heritable material by Martha Chase and Alfred Hershey, scientists have sought to understand the structure and sequence of an organism's genome. 1 In 1953, the structure of DNA was determined, 2 , 3 , 4 yet it was not until 1996 that the first eukaryotic genome sequence—baking and brewing yeast Saccharomyces cerevisiae —was published. 5 Soon after, the first multicellular organism genome, C . elegans , 6 was completed, prompting the race to sequence the human genome, culminating in the draft human genome sequence in 2001. 7 Sequencing the human genome was a great achievement, but with the available technologies the effort was very labor and time‐intensive, prompting new advances in DNA sequencing. This second wave of sequencing technology—called next generation sequencing—drastically decreased sequencing costs, increasing the amount of genomic and genome‐scale information. 8 , 9 , 10 , 11 , 12 , 13

Genomic sequencing methods are now widely available, providing insight into basic molecular mechanisms from evolutionary analysis to personalized medicine. Additionally, genomic technologies can be applied to any methodology or organism in which nucleic acid can be extracted, making genomic methods widely accessible and “‐omic” techniques a staple across fields and organisms. Due to the ubiquity of these techniques, it is imperative for scientists early in their careers to understand both the power and the peril associated with genome sequencing techniques. This review introduces sequencing technologies, analysis methods, and socio‐ethical issues associated with genome sequencing to undergraduates. Through reading and engaging with the anticipatory guides and discussion questions with their peers and applying these concepts to the case study included in the Supporting Information , students should achieve the following learning outcomes:

• Explain the differences between Sanger and next‐generation sequencing methods.
• Compare and contrast chip‐based genotyping and whole‐genome sequencing methods.
• Identify advances in chemistry that enabled sequencing by synthesis.
• Outline the general pipeline for high‐throughput sequencing sample preparation and data analysis.
• Illustrate how various next‐generation sequencing techniques can be exploited to understand different aspects of gene expression.
• Discuss the social justice and ethical implications associated with genome sequencing techniques.

2. SEQUENCING AND WHOLE‐GENOME ANALYSIS METHODS

Nucleic acid sequencing techniques have evolved since their inception with each new technique building off of previous sequencing technology and addressing a prior shortcoming. In this section, we will review the development of various sequencing technologies.

2.1. Sanger sequencing

Anticipatory guides.

• What is necessary for DNA polymerase to add the next nucleotide?
• What is nucleic acid polarity and what implications does DNA polarity have on the double helix structure?
• How are DNA fragments separated during agarose gel electrophoresis?
• Review the steps of DNA amplification as in PCR .

Chain termination sequencing was the first nucleic acid sequencing method and revolutionized molecular biology, resulting in the 1980 Nobel Prize. Chain termination, also called Sanger sequencing as it was developed by Fred Sanger in 1977, uses the selective incorporation of dideoxynucleotides during an in vitro DNA replication reaction 14 (Figure  1 ). During DNA replication, DNA polymerase catalyzes the synthesis of DNA by forming a phosphodiester bond between the next complementary nucleotide and the hydroxyl group (─OH) of the 3′ end of the growing DNA strand. 15 Sanger sequencing exploits the requirement for an available 3′‐OH. The Sanger sequencing reaction contains both de oxynucleotides ( d NTPs) and dideoxynucleotides (ddNTPs). While dNTPs possess a 3′ carbon containing a hydroxyl group (Figure  1(b) ), ddNTPs lack the 3′‐OH (Figure  1(b) ) which prevents polymerase from adding the next base. Deoxynucleotides are present at high concentrations and will be incorporated by DNA polymerase in further synthesis most of the time. However, ddNTPs which are labeled with fluorescent dye are still incorporated albeit at a lower frequency, halting synthesis. This synthesis reaction results in numerous DNA fragments of varying lengths complementary to the sequenced template, each ending with the fluorescently labeled ddNTP (Figure  1(a) ). Each of the four different nucleotides are conjugated to a different dye, which emit a distinct wavelength when excited.

Chain termination sequencing. (a) Schematic of chain termination sequencing. DNA templates are amplified by DNA polymerase in a reaction containing a mixture of dNTPs and fluorescently labeled ddNTPs. Amplified fragments terminated at different lengths are separated by capillary gel electrophoresis followed by laser excitation and detection. Sequences are displayed in a chromatograph (as peaks) where each nucleotide is represented by a differently colored peak. The height of the peak indicates the confidence level at that nucleotide position. (b) Chemical structure of ddNTPs and dNTPs. The critical 3′ hydroxyl group in dNTPs is highlighted in red, which is not present in ddNTPs

The DNA molecule's sequence is determined by separating out all the newly synthesized DNA fragments by size, using capillary gel electrophoresis that separates DNA molecules by size with single base resolution, where smaller DNA molecules move faster through the capillary. At the end of the capillary, a laser excites the ddNTP at the end of the chain and the fluorescent dye color is detected, allowing the sequence to be recreated by the order of the laser excitations wavelengths observed (fluorescent dyes detected) (Figure  1(a) ).

2.2. Chip‐based detection methods

• Define heterozygous vs homozygous at a genetic locus .
• Write the complementary sequence for: 5 ′ ‐ATGCATCGTAT‐3 ′
• Describe the process of DNA denaturation and annealing (hybridization) during PCR .
• What is a single nucleotide polymorphism (SNP)? How are SNPs related to alleles?
• Why is determining more than one DNA sequence at a time beneficial?

Sanger sequencing can only sequence a single DNA fragment per reaction. Although powerful—Sanger sequencing was used to sequence the human genome 7 —sequencing a single fragment at a time has limitations. Chip‐based sequencing methods, called microarrays, sought to resolve this issue. 16 , 17 , 18 It is important to note that these methods do not actually sequence DNA but allow for the simultaneous detection of different DNA sequence variants and mRNAs at once. Generally, DNA microarray chips consist of a solid surface dotted with small wells that contain a collection of single‐stranded DNA specific to a gene, allele, or genomic region called the probe. Detection of different sequences is based on denatured, single‐stranded samples hybridizing (attaching through hydrogen bonding) to complementary probes on the surface of the chip. 19 Samples are prepared by extracting nucleic acid, fragmenting the nucleic acid into small pieces, denaturing the samples into single strands, and labeling the small fragments of nucleic acid with a fluorescent dye. These fragments are washed across the chip and hybridized to the DNA probes in wells that are complementary to the fluorescently labeled sample. The chip is then scanned and the quantity of each sample that anneals to each well is detected based on the amount of fluorescence present. The specific sequence and location on the chip for the DNA probe in each well is known, and the fluorescent signal correlates to the quantity of that sequence in the original sample.

To date, the main applications of DNA microarrays have been single nucleotide polymorphism (SNP) detection and relative mRNA quantification. For SNP detection, two adjacent wells contain probes specific to two common alleles in the population. The genotype of the sample is determined through detection of which wells the sample binds as the sample only binds to the well with the exact complementary sequence. These DNA microarray chips are still commonly used today to genotype people, as the human genome is costly to sequence. Additionally, microarrays can be used to determine relative gene expression through differential hybridization, correlating to gene expression, of differentially labeled control and experimental samples. Due to advances in sequencing by synthesis (see below), microarrays are not often used for RNA quantification anymore.

2.3. Sequencing by synthesis

• What challenges are there to sequencing an entire genome with Sanger sequencing?
• What types of questions could you address by sequencing all the DNA or RNA in an organism?

In the mid 1990s and early 2000s, two critical innovations brought on a fundamentally new sequencing methodology, still referred to as “next generation sequencing,” “second generation sequencing,” or more generally “sequencing by synthesis,” in which a single DNA molecule is continually sequenced. Continually sequencing the same molecule, as opposed to chain termination in Sanger sequencing, was made possible due to new chemistry termed “reversible terminator chemistry.” A nucleotide with a reversible terminator has a blocked 3′‐OH, similar to a ddNTP in Sanger sequencing, but after addition of another chemical solution the blocked 3′‐group is reversed to a 3′‐OH, again supporting sequencing (Figure  2 ). 13 , 20 Each of these modified dNTPs is labeled with a different fluorescent dye. After each base is added to the elongating DNA strand, synthesis is halted because of the blocked 3′‐OH, the dye is excited, and the color of the fluorescent nucleotide is recorded. Next, a chemical solution is added which both quenches the fluorescent dye (so that it no longer fluoresces) and reverses the blocked 3′‐OH, supporting the next round of sequencing. There are several reversible terminators used commercially. The most common of which are 3′ blocked reversible terminators with either a 3′‐ONH2, 3′‐O‐allyl, or 3′‐O‐azidomethyl. 20

Sequencing by synthesis. (a) Cycle of reversible terminator incorporation, identification of incorporated base by fluorescence imaging, followed by removal of the reversible terminator. (b) Chemical structure of a nucleotide with a reversible terminator attached. The 3′‐OH group is capped by a reversible terminator (black rectangle), with a fluorophore attached to the nitrogenous base (red circle). The fluorophore is then excited (red star), and the nucleotide is recorded. Finally, the fluorophore is cleaved from the nucleotide and the 3′‐OH (highlighted in red) is unblocked for the next round of sequencing

Another key innovation for sequencing by synthesis was simultaneous sequencing of multiple DNA sequences by attaching DNA strands to a flow cell, a two‐dimensional microfluidic device (which resembles a microscope slide)—very similar to a microarray used in chip‐based methods described above. First, DNA fragments are attached on one end to the flow cell. Next, each DNA molecule is amplified resulting in many copies of that DNA molecule in the same spot (or cluster) on the chip, amplifying the signal (sequence of the DNA molecule)—a step called “cluster generation”. 13 Spots on the chip have different initial DNA molecules and there can be millions of individual DNA molecules on each chip. After cluster generation, sequencing proceeds. For each base in the DNA strand, reversible terminator modified nucleotides are added and the attached fluorophores are excited and the chip imaged (Figure ​ (Figure2). 2 ). The colored images are translated into a DNA sequence, resulting in a single sequence for each cluster on the chip. Sequencing occurs for a defined number of rounds (usually between 50–300 bases, but can be as long as 500 bases), creating what is termed a “short read” of DNA sequence. Identification of the nucleotide incorporated for each DNA fragment relies on the amplified signal from the many copies of that DNA fragment in the cluster. At times one of the DNA fragments in a cluster gets “off phase” from all other DNA fragments in the cluster by accidently incorporating more than one nucleotide at a time, resulting in an incorrect signal of the nucleotide incorporated for that DNA fragment. The longer the sequencing, the more likely it is that some of the DNA molecules in a cluster get “off phase,” limiting the length of the sequencing reads. 21 In addition, sequencing reads should be long enough to unambiguously map to the genome thus setting the limits of read lengths for sequencing by synthesis.

2.4. Third generation sequencing

• What are the advantages of being able to sequence longer fragments of DNA?
• What are the consequences if sequencing is not accurate?
• What is a processive enzyme and provide an example of a processive enzyme that uses a nucleotide substrate?
• What is a protein pore in membrane bilayers?

Third generation sequencing technologies, called single molecule sequencing (SMRT) and nanopore sequencing, rely on sequencing single nucleotide molecules. 22 Like Sanger sequencing and sequencing by synthesis, SMRT sequencing, developed by PacBio, also relies on synthesizing a new DNA strand by DNA polymerase. 23 However, the DNA polymerase is immobilized at the bottom of a tiny well in the sequencing chip. Each well has a single piece of DNA to be sequenced and each dNTP is given a fluorescent label with an unique emission spectrum. The immobilized DNA polymerase begins to replicate the DNA strand and as each dNTP is added, the fluorophores are excited. The sequence of the DNA can then be easily determined based on the emission spectra observed, which belong to the incorporated nucleotides that were detected.

A complementary third generation sequencing technology was developed that, like SMRT sequencing, relies on direct detection of a single nucleotide molecule but does not rely on DNA synthesis. Instead, nanopore sequencing (Figure  3 ) uses a membrane protein complex. This protein complex consists of two proteins: (a) an unwinding enzyme and (b) a pore protein which allows molecules to pass through a lipid bilayer. The unwinding enzyme, such as polymerase or helicase, unwinds the double helix so that a single nucleic acid strand (DNA or RNA) passes through the pore protein. 24 This pore protein is inserted in a synthetic lipid bilayer. A commonly used pore protein is MspA, which is a transmembrane protein found in Mycobacteria used to transport nutrients across the bacterial membrane. 25 , 27 , 28 The lipid bilayer has variable voltage on either side. As nucleotides pass through the unwinding enzyme and the pore, the mass of the nucleotide creates a distinct change in current. From the specific current signature detected, the sequence of the nucleotide strand is determined.

Nanopore sequencing. DNA double helix is unwound by unwinding enzyme and a single strand is fed through the pore inserted in a membrane. As the DNA moves through the protein nanopore, the nucleotides (colored circles) are identified by the change in ion current (yellow) across the membrane. Graph shows the identification of nucleotides in the DNA sequence based on the current measured over time

Third generation sequencing is characterized by its ability to sequence much longer reads. Both SMRT and nanopore technologies have reported reads of at least 8000 bp as compared to sequencing by synthesis in which the longest reads are 500 bp. 29 However, longer reads come at the expense of sequencing accuracy—both third generation technologies have much higher error rates than second generation sequencing. 29 , 30 To improve sequencing accuracy, in nanopore sequencing, the two strands of DNA are ligated with a hairpin structure, thus when the DNA is denatured and passed through the pore as a single stranded molecule, both complementary strands are sequenced (Figure  3 ). This provides twice the sequence for one strand, helping to resolve unclear base calls. Similarly, prior to SMRT sequencing, hairpins are ligated to both ends of DNA, resulting in a circular single stranded DNA. This DNA molecule can be continually sequenced by the immobilized polymerase, resulting in better base calling due to the multiple sequencing rounds.

2.5. General discussion questions

• How does a dideoxynucleotide prevent elongation by DNA polymerase?
• What aspects of Sanger sequencing gave way to sequencing by synthesis?
• What aspects of DNA microarray chips gave way to sequencing by synthesis?
• Draw a picture of the results on a DNA microarray for a sample homozygous at a locus and for a sample heterozygous at the locus .
• Why would adding all four nucleotides at the same time in sequencing by synthesis reaction result in more accurate sequencing?
• Pose a research question appropriate for each of the technologies discussed above .
• Compare and contrast the different sequencing/genome detection methods .
• For each of the sequencing methods above , enumerate the significance and limitations .
• If sequencing a new genome , why would using a combination of sequencing by synthesis and third generation sequencing be advantageous?

3. SEQUENCING PIPELINE

The sequencing pipeline is a three‐step process: sample and library preparation, sequencing, followed by data analysis and bioinformatics. Above described the second step—sequencing. This section will describe the process of steps one and three.

3.1. Sample and library preparation

• What are challenges to sequencing many different fragments of DNA at once using sequencing by synthesis?
• What are primers and why are they necessary for DNA replication?
• What is cDNA? How does cDNA sequence differ from the genomic DNA?

Before sequencing, the nucleic acid sample is isolated using traditional molecular biology techniques. Applications where determining the sequence or the amount of different specific sequences in a sample are typical applications of second and third generation sequencing technologies (reviewed in Reuter et al. 26 ). After nucleic acid isolation, one of the challenges to genomic sequencing methods is the preparation of many millions of different sequences for sequencing at the same time (Figure  4 ). For sequencing by synthesis and SMRT sequencing methods (which rely on DNA polymerase) all the sequences must have at least some common sequence to which a primer can anneal. In second generation sequencing, DNA sequences are adhered to the chip by hybridizing to a complementary single‐stranded DNA oligonucleotide and a primer also binds to this sequence supporting cluster generation. 13 Thus, the necessity of a common DNA sequence on each DNA fragment for chip hybridization, cluster generation, and sequencing is at odds with the innovation that many different DNA pieces of unknown sequences are sequenced simultaneously.

Sequencing by synthesis pipeline. (a) Genomic DNA is first fragmented into smaller templates which undergo modification, including 5′‐phosphorylation and addition of 3′‐a for adaptor ligation. Following size selection and PCR amplification, the library is denatured and amplified into clonal clusters that undergo linearization, blocking, and hybridization, preparing the flow cell for sequencing, using reversible terminators. (b) DNA fragment converted into library with adaptor and primer sequenced indicated

To overcome this problem, the nucleic acid sample is prepared into a “library”—a collection of DNA fragments each with common sequences (adaptors) on either end 13 (Figure  4(b) ). First, if the sample is RNA, it is converted to cDNA (complementary DNA) using reverse transcriptase as DNA is much more stable than RNA and DNA polymerase requires a DNA template. Since sequencing by synthesis requires short pieces of DNA, the DNA is sheared to less than 500 bps in length. Since each fragment of DNA is unique, the same adaptors (pieces of DNA) must be added to the ends of each fragment to replicate and sequence each unique fragment simultaneously. The first step in adaptor attachment is to add a single “A” base to the 5′ ends of each sequence. This off‐hanging “A” base allows the adaptors to attach through ligating to the complementary “T” overhang on the 3′ end of the adaptor. The ends of the adaptor are complementary to the end of the primer sequence, which through PCR both amplifies the library so that there is enough material for sequencing and extends to add the primer sequence. After this PCR step, the DNA can attach to the flow cell, and primers support cluster generation and subsequent sequencing by synthesis. Samples prepared for nanopore sequencing have a very similar library preparation step, adding adaptors to each of the fragments, however fragmentation is not necessary since these technologies support sequencing longer pieces. Even though nanopore sequencing relies on direct detection and not sequencing by synthesis, the adaptors are necessary to feed the nucleic acid through the pore by the ratcheting enzyme. 29

3.2. Data analysis and bioinformatics

• From the steps in sequencing by synthesis described above , identify what determines the length of the sequence returned to the user .
• What is the risk of only sequencing each base one time?
• What information would be useful from the sequencing reaction for analyzing the accuracy of the base call?
• What are intron/exon boundaries?

In high‐throughput sequencing, millions of reads are sequenced. A read is the sequence of each DNA fragment and in second generation sequencing the length of the sequence is defined by the number of sequencing cycles (the number of times modified dNTPs were added and imaged), typically from 50 to 300 base pairs in 50‐base‐pair increments. Thus, since the DNA is typically sheared to 200–500 base pairs during the library preparation, the entire DNA fragment is not sequenced. In a typical sequencing reaction, termed single‐end sequencing, the fragment is sequenced from just one end of the DNA fragment. A paired‐end sequencing reaction sequences each fragment from both of the DNA ends, providing twice as much sequencing information of the same piece of DNA. The reads are returned to the user in a plain text file termed a FASTQ file (Figure  5 ). 31 The FASTQ file format is a repeating unit of four lines: (a) the name of the read, which begins with an “@” symbol; (b) the sequence of the read; (c) a separator, a single “+” (plus) sign, to make the file easier to read; (d) the quality score line. Each base pair in the sequence receives a quality score termed the Q‐score ranging from 1 to 40 with 1 indicating the least confidence that the base call is correct and 40 being the most confident. 13 For example, “I” is a score of 40 which translates to 99.99% accuracy for that base call. The symbol code, which is an ASCII based code, ensures that each numerical score only takes up a single character space so that it lines up with the appropriate base. This repeating four lines continues for the millions of reads sequenced. For second generation sequencing, a single sequencing sample can produce over 150 million reads.

Whole genome sequencing analysis. (a) Example four lines of each read in a FASTQ file. Components in the FASTQ file are labeled with a text box of the same color, which include the sequence ID, nucleotide sequence, and quality score. (b) Example reads mapped to a reference genome (black). An example of 1x coverage (left) and 5x coverage (right) is shown. Reads common to both the 1X and 5X examples are shown in light gray, and reads only in the 5X example are shown in dark gray

Bioinformatic analysis consists of quality control of the reads and then mapping the reads to the genome of interest. For quality control, the fourth line for each sequence is read in the FASTQ file, to determine if there is sufficient confidence in each base call. Based on quality control results, some trimming of low‐quality bases may be required to ensure that only high‐quality bases are included in the analysis. Another common pre‐processing step is to remove the general adaptor sequence so that the reads map more reliably to the genome, which is the next step in bioinformatic analysis. Most often, second and third generation sequencing is not used to sequence a new genome from scratch but rather for analyzing and quantifying the sequences of a nucleic acid sample of interest from an organism with a sequenced reference genome by mapping the reads to this reference genome (Figure  5 ). For nucleic acid samples from DNA, mapping is straight‐forward (although computationally intensive), and reads are compared to the entire known genome to find the place that matches the read. After all reads are mapped to the genome, the amount of coverage is determined by approximating how many times each nucleotide is represented in all of the sequencing reads (Figure  5 ). For RNA‐seq, which sequences the mRNA of a sample and identifies gene expression and alternative splicing, more sophisticated mapping algorithms are used to map reads that span exon‐intron boundaries, which result in part of the read mapping in a different location than the other end of the read as compared to the genomic sequence. Once mapped, the coverage of the gene in RNA‐seq samples is used to determine that gene's expression in a sample. This expression can be compared across samples to determine differentially regulated genes between different conditions. More recently, new RNA‐seq mapping algorithms significantly decrease processing time by skipping over the labor‐intensive mapping portion and directly quantitating transcript levels. 32 , 33 , 34

3.3. General discussion questions

• Explain the purpose of the adaptor and primer sequences in a genomic library .
• On the flow‐chart diagram in Figure  4 , draw the library preparation steps for fragments of DNA .
• How can you determine how many reads were sequenced from the number of lines in a FASTQ file?
• Outline the different steps needed to go from RNA‐seq FASTQ files to gene expression quantitation .
• Explain how RNA‐seq could be used to detect alternative splicing .
• In what applications would paired‐end sequencing be desirable over single‐end sequencing?
• Why is “high coverage” important when trying to identify mutations in a sample?

4. SOCIAL IMPLICATION OF GENOME SEQUENCING

Power lies within the genomic tools discussed above. This power can be enormously beneficial to millions of people and change lives, but researchers must consider the long‐term consequences and contemplate the social and ethical ramifications. Below we illuminate some of the ethical and social consequences when genetic sequencing is used for medical advancements and placed directly in the hands of consumers.

4.1. Knowledge is power

• How will genetic testing change medical treatments?
• Is the application of genetic testing limited to human diseases?

The ease in sequencing and decrease in cost has led to numerous discoveries linking genes to diseases. 35 Genome‐wide association studies (GWAS) facilitate linking complex genetics to differential phenotypes. GWAS identifies specific SNPs associated with diseases by comparing common sequence variants and/or genomes between unaffected individuals to those individuals with a phenotype or interest. 36 Knowing what SNPs are associated with particular diseases, is the foundation of precision medicine. 37 Precision medicine allows medical professionals to choose the most effective treatments based on an individual's genetic sequence.

Sequencing technologies have led to numerous direct‐to‐consumer sequencing companies that allow individuals to learn about their own genetics without professional medical assistance. 38 In most cases, consumers simply mail a saliva sample which is genotyped, using a DNA microarray as described above with probes to common variants in the human population. Consumers learn the sequence of their genomic loci known to be associated with different phenotypes such as lactose intolerance, heart disease, or caffeine sensitivity. As a consumer, a person must decide what they hope to learn about their genetic make‐up to select the appropriate direct‐to‐consumer sequencing service.

4.2. Does everyone have equal access to this “power?”

• How do social inequalities affect access to genetic testing and relevant treatments?
• Who should be responsible for educating people about genetic testing and its implications?

Access to genomic technologies has traditionally been through clinical genetic testing. Sequencing by synthesis has revolutionized clinical genetic tests by allowing interrogation of multiple different genes or even the entire genome of a patient sample at once, significantly speeding up genetic testing. Results from these tests can be used both for diagnosis and to identify targeted therapies. However since clinical genetic testing is administered in a healthcare setting, social inequities which are well‐documented in healthcare also plague genetic testing. 39 Access to clinical genetic testing is not equivalent across all racial and socioeconomic communities 40 due to factors such as differences in comprehensive health insurance among racial groups 41 and mistrust of medical testing by individuals from groups historically excluded from healthcare. 40 These disparities put racial minorities at increased disadvantage to reaping the benefits of clinical genetic testing.

Even though direct‐to‐consumer tests can be more affordable ($60–$200 depending on the comprehensiveness of the test) the costs are out‐of‐pocket and still represent a significant barrier to access. Additionally, the health and lifestyle genetic risk factors that direct‐to‐consumer report on are from studies with inequitable racial representation. The majority of genetic research databases and GWAS include mostly European‐descent genomes, indicating a serious gap in “who” is being solicited to participate in genetic research. 42 This lack of representation decreases the applicability of results across populations and understanding of genetic diseases in non‐European populations. Thus, even if an individual has the means to access these tests, it is not a given if those results are applicable to them.

4.3. Can this “power” end up in the wrong hands?

• Who might you want to keep your genetic results from?
• How secure (private) are genetic testing results?

While there may be great benefits of having a better understanding of your genetic make‐up, before signing up with a direct‐to‐consumer genetic test, the consumers must consider who owns and has access to their data and fully understand the companies' privacy policies prior to submitting their sample. To help protect an individual's privacy, legislation has been enacted to protect the consumer's privacy. The 21st Century Cures Act seeks to protect an individual's confidentiality when genetic information is donated to federal research purposes by removing all identifiers (i.e., donor's name and contact information). 43 Any information obtained from the research cannot be released to law enforcement or government agencies. In addition, under the Health Insurance Portability and Accountability Act (HIPPA) one's genetic information is protected from employers, schools, and the public if it becomes a part of one's health record. 44 The entities that can access this information are law enforcement and health insurance.

With the rise of genetic testing came concern about genetic discrimination if health insurance companies had access to genetic testing results; companies could discriminate against those who tested positive for differing genetic predispositions and alter their healthcare coverage. The Genetic Nondiscrimination Act (GINA) was passed in 2008 45 to prevent health insurance companies from denying coverage and changing rates based on genetic predispositions. However, it only protects individuals who are not showing any symptoms of the predisposition. If symptoms of the genetic difference are present, then insurance companies could alter coverage and rates. GINA also prohibits employers from changing the employment status based on genetic testing results. Direct‐to‐consumer testing companies have their own privacy policies that should be considered before using the service. These considerations highlight how a scientist should also understand how a scientific tool is being used in addition to the development of such technologies.

4.4. Discussion questions

• What human diseases are ideal for genetic testing and precision medicine?
• What are the limitations of genetic testing?
• Is it always beneficial for someone to know genotype(s)? Are there genotypes that one might not want to know?
• What direct‐to‐consumer service(s) do you think provides the most interesting (relevant) information?
• Who should be responsible for genetic testing costs?
• Should restrictions be placed on genetic testing? Why or why not? If yes , what are appropriate restrictions?
• What are potential concerns regarding genetic testing? Would these concerns stop you from getting your DNA tested? Why or why not?
• Who should have access to genetic testing results?
• Is additional legislation necessary to regulate genetic testing and results? What issues should that legislation address?

Supporting information

Appendix S1 : Supporting Information.

ACKNOWLEDGMENT

Funding support provided by Davidson College to D. T‐S.

Burian AN, Zhao W, Lo T‐W, Thurtle‐Schmidt DM. Genome sequencing guide: An introductory toolbox to whole‐genome analysis methods . Biochem Mol Biol Educ . 2021; 49 :815–825. 10.1002/bmb.21561 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Funding information Davidson College

The Genomic Data Analysis Network

The Genomic Data Analysis Network (GDAN) serves to help the cancer research community leverage the genomic data and resources produced by CCG and other NCI programs

About the Genomic Data Analysis Network

While large genomic datasets are invaluable to the cancer research community, translating genomic data into biological insights into the development and treatment of cancer is not a straightforward task. Over a decade of experience from The Cancer Genome Atlas (TCGA) program demonstrated the power and necessity of “team science”—that successful analyses of large-scale genomic datasets require the coordination of a large body of researchers with a wide range of expertise in computational genomics, tumor biology, and clinical oncology. 

CCG’s Genomic Data Analysis Network (GDAN) was formed from the need to harness TCGA data and a growing need at large for computational genomics. For TCGA, the network created standardized data formats and processing protocols, generated bioinformatics tools for the community, and performed a range of analyses on the data, notably generating clinically meaningful molecular subgroups of cancer and producing the PanCancer Atlas .  

In the post-TCGA era, the GDAN continues to conduct key large-scale studies and generate genomic resources to support the genomic research community. The GDAN’s overall goal is to help the cancer research community leverage the genomic data and resources produced by CCG and other NCI programs for the benefit of cancer patients, largely by: 

• developing and implementing new bioinformatic and computational tools to capture key biological insights about cancer (e.g., pathway analysis, data integration with visualization, and integrated cancer biology);   
• developing data processing and quality control methods for working with large-scale genomic characterization data;   
• processing and integrating a variety of analytical data types to generate disease-level findings and perform cross-disease analyses. 

The GDAN is comprised of individual Genome Data Analysis Centers (GDACs), each specializing in a unique set of computational analyses, molecular platforms, data integration, or visualization techniques. The GDACs are tasked to cooperatively perform molecular analyses on new and existing data from CCG programs and work with the other components of CCG’s Genome Characterization Pipeline. Areas of expertise and examples of their utility include: 

• DNA Mutations – Identifying mutations in coding and non-coding regions of the genome, classifying mutations as driver or passenger mutations, identifying chromosomal rearrangement events leading to fusion proteins, and determining potential enhancer or suppressor functionality of mutations. 
• Gene Expression – Identifying mRNA expression patterns and correlating with relevant clinical parameters, identifying translocation or rearrangement events. 
• Copy number and tumor purity - Clustering cases according to copy number alteration or loss-of-heterozygosity events, identifying candidate drivers of copy number alterations, estimating tumor purity of the samples. 
• miRNA analysis - Analyzing miRNA expression to correlate with patterns of mRNA expression and identify expression regulation networks, correlating miRNA data with relevant clinical parameters. 
• Long non coding RNA (lnRNA) – Analyzing lnRNA expression patterns and correlating with patterns of mRNA expression or expression regulation networks. 
• Batch effects and data integration – Identifying batch effects that might have been accrued during processing of samples, devising bioinformatics methods to correct such effects, determining biologically relevant groups that can subsequently be analyzed in the context of clinical data. 
• Methylation analysis – Identifying DNA methylation patterns of interest and correlating patterns with relevant clinical parameters, correlating patterns with mRNA expression data to propose gene regulation mechanisms. 
• Pathway analysis – Identifying biological pathways that have been altered, performing multi-omic data analyses to identify altered pathways and potential clinical relevance. 
• Single cell RNA sequencing – Identifying cell clusters according to gene expression patterns, extracting expression levels and correlating with relevant clinical parameters, identifying translocation/rearrangement events, and identifying cell clusters or subclones of interest. 
• Circulating cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) – Analyzing “liquid biopsies,” or blood samples for cfDNA or ctDNA to establish correlations between mutations in tumor tissue and ctDNA, developing methods to utilize the technology as a diagnostic and prognostic tool, and creating models of disease burden and progression in cancer development. 
• Long-read sequencing – Assembling genomes, identifying structural variants, sequencing through repetitive regions, phasing critical variants. 
• Spatial genomics – Analyzing gene expression data with spatial information, produced from different emerging spatial genomics platforms.  
• Digital Imaging – Mining histopathology images for elements that aid in diagnostic or prognostic efforts, applying machine learning to learn relevant features. 

New Molecular Profiling Platforms to Explore New Facets of Cancer 

CCG continues to expand and develop new genomic data and analysis resources for the cancer research community. Through the GDAN and other CCG programs, CCG is exploring new ways to mine the data and learn new things about cancer from the massive dataset.  

Additionally, as new molecular platforms become available, CCG explores utilizing these platforms to complement existing datasets. New platforms may be utilized in new structural genomics projects or in some cases to further characterize existing samples. Existing or new Genome Characterization Centers may be sought out to provide these capabilities. These newer platforms include: 

• Assay for transposase-accessible chromatin using sequencing (ATAC-seq) 
• Single-cell RNA 
• Single-cell DNA 
• Spatial genomics 

CCG considers how these new technologies may be applied to enhance what we can learn about cancer. For example, can single-cell or spatial technologies provide much needed insights into the tumor microenvironments of tumors that don’t respond to treatment? How can the technologies be used to further what we can learn from TCGA or other existing datasets? 

For example, GDAN researchers applied the ATAC-seq chromatin accessibility assay to 410 TCGA tumor samples, getting an unprecedented systematic look at gene dysregulation in cancer. With this low-cost assay, the researchers were able to discover new DNA regulatory elements and a new class of mutations falling within these elements that may play a key role in cancer.  

In addition to applying new molecular platforms to TCGA samples, CCG is also working to perform whole-genome sequencing for the complete set of TCGA samples. These rich datasets, along with analyses and methods developed by the GDAN, could help facilitate the discovery of new diagnostic and prognostic markers, new targets for pharmaceutical interventions, and new cancer prevention and treatment strategies.  

Current GDAN Centers 

The GDAN is comprised of individual Genome Data Analysis Centers (GDACs), each contributing distinct functions, capabilities, and analytical components. Each GDAC works collaboratively within the network and also with other components of CCG’s Genome Characterization Pipeline. The current GDACs and their area of expertise in computational genomics are described below. 

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• New AI-powered statistics method has potential to improve tissue and disease research

Research team hopeful that the method, called IRIS, can provide more detailed information for precision health treatment plans and health outcomes.

June 6, 2024

Researchers at the University of Michigan and Brown University have developed a new computational method, IRIS, to analyze complex tissue data which could transform our current understanding of diseases and how we treat them. 

Integrative and Reference-Informed tissue Segmentation (IRIS) is a novel machine learning and artificial intelligence method that gives biomedical researchers the ability to view more precise information about tissue development, disease pathology and tumor organization.

The findings were published today in the journal Nature Methods .

IRIS draws from data generated by spatially resolved transcriptomics (SRT) and uniquely leverages single-cell RNA sequencing data as the reference to examine multiple layers of tissue simultaneously and distinguish various regions with unprecedented accuracy and computational speed.

Unlike traditional techniques that yield averaged data from tissue samples, SRT provides a much more granular view, pinpointing thousands of locations within a single tissue section. However, the challenge has always been to interpret this vast and detailed dataset, says Xiang Zhou , professor of Biostatistics at the University of Michigan School of Public Health and senior author of the paper. He worked with Ying Ma , assistant professor of Biostatistics at the Brown University School of Public Health , to develop IRIS.

Interpreting large and complex datasets is where IRIS becomes a helpful tool—its algorithms sort through the data to identify and segment various functional domains, such as tumor regions, and provide insights into cell interactions and disease progression mechanisms.

“Different from existing methods, IRIS directly characterizes the cellular landscape of the tissue and identifies biologically interpretable spatial domains, thus facilitating the understanding of the cellular mechanism underlying tissue function,” said Ma, who earned her Ph.D. in Biostatistics from the University of Michigan School of Public Health in 2023. “We anticipate that IRIS will serve as a powerful tool for large-scale multi-sample spatial transcriptomics data analysis across a wide range of biological systems.”

In the study, the researchers applied IRIS to six SRT datasets and compared its performance to other commonly used spatial domain methods. IRIS significantly outperformed other methods in accuracy. Ultimately as SRT technology continues to grow in popularity and use, the researchers hope to see methods like IRIS help to potentially develop targets for clinical interventions or drug targets, improving personalized treatment plans and patient health outcomes.

“The computational approach of IRIS pioneers a novel avenue for biologists to delve into the intricate architecture of complex tissues, offering unparalleled opportunities to explore the dynamic processes shaping tissue structure during development and disease progression.  Through characterizing refined tissue structures and elucidating their alterations during disease states, IRIS holds the potential to unveil mechanistic insights crucial for understanding and combating various diseases.” said Zhou.

The study, “ Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics ,” was supported by grants from the National Institutes of Health.

Contact Destiny Cook Senior Public Relations Specialist University of Michigan School of Public Health [email protected] 734-647-8650

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Single-Cell Genomics and Regulatory Networks for 388 Human Brains

Yale researchers who conducted the largest human brain analysis from a single-cell perspective hope their findings lead to better prediction of medicine that will target certain cells.

The study was led by co-corresponding authors Matthew Girgenti, PhD, assistant professor of psychiatry, and Mark Gerstein, PhD, Albert L. Williams Professor of Biomedical Informatics and professor of molecular biophysics & biochemistry, of computer science, and of statistics & data science at Yale School of Medicine. The findings were published in Science .

Single-cell genomics offers a powerful method to understand how genetic variants influence gene expression, especially across the numerous cell types in the human brain.

Moreover, it can potentially refine our understanding of the regulatory mechanisms underlying brain-related traits such as psychiatric disorders.

Girgenti and Gerstein participate in the PsychENCODE Consortium, which performed single-cell experiments (single-nucleus RNA-Seq, ATAC-Seq, and Multiome plus DNA sequencing) and computational analyses on samples from almost 400 human prefrontal-cortex samples of adults with a range of brain-related disorders such as schizophrenia, autism spectrum disorder, bipolar disorder, and Alzheimer’s disease, as well as controls.

These population-scale cohorts, with a wide range of brain phenotypes, are needed to infer significant associations among genetic variants and to develop models of regulation in specific cell types of the brain.

Integration of RNA expression and genotype data revealed >1.4M single-cell eQTLs (DNA positions that regulate gene expression), many of which were not seen in prior gene-expression datasets and a subset of which are involved in brain disorders. The researchers also found that expression patterns across cell types recapitulated the spatial architecture of neurons and enabled the identification of "dynamic eQTLs," with changes in regulatory effects across cortical layers.

The chromatin datasets in the resource allowed for identification of >550K single-cell cis regulatory elements, which were enriched at loci linked to brain-related traits. Combining gene expression, chromatin, and eQTL datasets, the researchers built cell-type-specific gene-regulatory networks and developed cell-to-cell communication networks, which highlighted differences in signaling pathways in the schizophrenic and bipolar disorder brain, including altered WNT and FGF signaling.

This integration allowed for accurate imputation of cell-type-specific expression and phenotype from genotype and allowed them to prioritize >250 risk genes and drug targets for brain-related disorders within specific cell types. Computationally simulated perturbation of individual genes led to predicted expression changes mirroring those for disease cases, increasing confidence in the predicted drug targets.

“This is the largest single cell multi-omic dataset of the human brain to date,” Girgenti said. “This population-scale resource for the human brain will help facilitate precision-medicine approaches for neuropsychiatric disorders, especially by prioritizing follow-up genes and drug targets linked to specific cell types.”

Funding was provided by the Simons Foundation and National Institute of Mental Health.

• Basic Science Research

Featured in this article

• Matthew Girgenti, PhD Assistant Professor of Psychiatry
• Mark Gerstein, PhD Albert L Williams Professor of Biomedical Informatics and Professor of Molecular Biophysics & Biochemistry, of Computer Science, and of Statistics & Data Science

• Open access
• Published: 03 June 2024

Genomic profiling informs therapies and prognosis for patients with hepatocellular carcinoma in clinical practice

• Mengqi Song 1   na1 ,
• Haoyue Cheng 2   na1 ,
• Hao Zou 1   na1 ,
• Kai Ma 1   na1 ,
• Lianfang Lu 1   na1 ,
• Qian Wei 1   na1 ,
• Zejiang Xu 1   na1 ,
• Zirui Tang 3   na1 ,
• Yuanzheng Zhang 4   na1 ,
• Yinan Wang 5   na1 &
• Chuandong Sun 1   na1  

BMC Cancer volume  24 , Article number:  673 ( 2024 ) Cite this article

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Hepatocellular carcinoma (HCC) genomic research has discovered actionable genetic changes that might guide treatment decisions and clinical trials. Nonetheless, due to a lack of large-scale multicenter clinical validation, these putative targets have not been converted into patient survival advantages. So, it's crucial to ascertain whether genetic analysis is clinically feasible, useful, and whether it can be advantageous for patients. We sequenced tumour tissue and blood samples (as normal controls) from 111 Chinese HCC patients at Qingdao University Hospital using the 508-gene panel and the 688-gene panel, respectively. Approximately 95% of patients had gene variations related to targeted treatment, with 50% having clinically actionable mutations that offered significant information for targeted therapy. Immune cell infiltration was enhanced in individuals with TP53 mutations but decreased in patients with CTNNB1 and KMT2D mutations. More notably, we discovered that SPEN , EPPK1 , and BRCA2 mutations were related to decreased median overall survival, although MUC16 mutations were not. Furthermore, we found mutant MUC16 as an independent protective factor for the prognosis of HCC patients after curative hepatectomy. In conclusion, this study connects genetic abnormalities to clinical practice and potentially identifies individuals with poor prognoses who may benefit from targeted treatment or immunotherapy.

Peer Review reports

Introduction

Hepatocellular carcinoma (HCC) is a malignant and high heterogeneity tumour originating from the liver. It is the sixth most commonly diagnosed cancer and the third leading cause of cancer death worldwide in 2020 [ 1 ]. The incidence and mortality of HCC in China account for 45.3% and 47.1% of the world, respectively. The 5-year overall survival rate is currently only 14.1% [ 2 ]. The major risk factor for HCC is shifting from viral and alcoholic liver disease to obesity, type 2 diabetes, and nonalcoholic fatty liver disease [ 3 ]. The molecular pathogenesis of HCC involves the dysregulation of multiple signalling pathways, including Wnt/ß-Catenin, RAS/MAPK, PI3K/AKT/mTOR, TP53/cell cycle, IGFR, and MET, which is related to point mutations, copy number variations, epigenetic alternations, tumour suppressor inactivation and so on [ 4 ]. Hopefully, these discoveries will enable us to identify biomarkers for foretelling prognosis or responses to therapy.

The landscape of genetic alterations in HCC has a clear delineation, including the most prevalent mutations affecting TERT promoter (60%) [ 5 ], TP53 (12–48%) [ 6 , 7 , 8 ], and CTNNB1 (11–37%) [ 9 ]. The genetic alterations provide potential targets for treatment planning and prognostic assessment of HCC. About 25% of patients with HCC were detected potentially actionable mutations [ 10 ]. Sorafenib inhibits tumour growth and angiogenesis by targeting the RAF/MEK/ERK pathway and receptor tyrosine kinases [ 11 ]. PRI-724, a specific inhibitor targeting β-catenin, can be used to address HCC due to CTNNB1 mutation [ 12 ]. HCC caused by TERT promoter mutation can be intervened by using targeted drugs such as GX301, Imtelstat, and GV1001 [ 13 ]. Regarding prognostic markers, ARID1A , MLL , [ 14 ] LRP1B , and TP53  mutations, particularly the hotspot mutations R249S and V157F, are associated with poor prognosis for patients with HCC [ 15 , 16 ]. Song et al. found that TSC2 mutations were independently associated with early recurrence in HCC patients who underwent hepatectomy [ 17 ]. Nonetheless, these potential targets are yet to be translated into the actual survival benefits of patients due to the low mutation rates of most driver genes, no targeted drugs for oncogenic mutations, and the lack of large-scale multicenter clinical validation [ 13 ].

This study employed multigene sequencing panels targeting cancer driver genes involving key deregulated pathways in HCC, 175 drug-targeted genes, 23 immunotherapy-related genes, and 18 chemotherapy-related genes. Based on the real-world evidence from 111 patients with HCC, we aimed to determine the clinical viability and utility of genome analysis and whether patients can benefit from genomic profiling. Moreover, we identified mutations in four genes associated with survival, mutations in three other genes related to immune infiltration, and 292 novel potentially pathogenic mutations that could serve as potential targets for treatment decisions and prognostic assessment.

Materials and methods

Patient selection and clinical data collection.

This is a retrospective study. We screened 111 HCC patients with somatic mutations detected by targeted-capture sequencing. They were treated at the Affiliated Hospital of Qingdao University between October 2015 and November 2020. The follow-up was conducted up to January 15, 2022. Postoperative histopathological examinations confirmed clinical diagnoses. Clinicians gathered clinical data on the progression of the condition (Table S1). All patients were treated surgically. The extent of surgical resection is shown in Table S1. The study was authorized by the Ethics Committee of the Affiliated Hospital of Qingdao University (approval no. QYFYWZLL27327). The informed consent form was offered and signed by each patient. The experiment complied with the official key recommendations of the National Health and Family Planning Commission of China.

DNA extraction, library construction and sequencing

DNA was isolated from tumour tissue samples and whole blood samples (as normal controls) by QIAamp Fast DNA Tissue Kit and QIAamp DNA Blood mini Kit (QIAGEN), respectively. The concentration of DNA was determined using qubit fluorometry, and the integrity and purity were evaluated using agarose gel electrophoresis and the Qubit 2.0 fluorimeter (Thermo Fisher, USA). The targeted DNA sequence was then enriched and captured by two custom sequence capture probes (Nimblegen, USA) that targeted 7708 exons of 508 cancer-related genes and 10,176 exons of 688 cancer-related genes, respectively. Sequencing was performed on the MGISeq-2000 platform with a coverage depth of 1000 × for tumour tissue and 400 × for blood (MGI, Shenzhen, China).

The specific target gene list is in Table S2. There are 850 targeted genes captured by 688 and 508-gene panels, including 345 shared genes, 343 genes specific in the 688-gene panel, and 163 genes specific in the 508-gene panel. In the 508-gene panel, there were 135 genes involved in tumour signalling pathways, 89 associated with targeted therapy, and 16 associated with immunotherapy, 12 of which were associated with both targeted therapy and immunotherapy. The 688-gene panel included 452 genes involved in tumour signalling pathways, 11 associated with chemotherapy, 165 associated with targeted therapy, and 22 associated with immunotherapy, 15 of which involved both targeted therapy and immunotherapy.

Sequencing data analysis

SOAPnuke [ 18 ] was used to remove adapters and filter low-quality reads after obtaining raw sequencing data. Using bwa-mem2 ( https://github.com/bwa-mem2/bwa-mem2 ) [ 19 ], clean reads were mapped to the human reference genome (hg38). GATK (v 4.1.9.0) [ 20 ] was used to eliminate duplicates, identify somatic variants, and filter variants. The assessment of clinical importance and the prediction of the functional impact of sequence variants were done using ANNOVAR ( http://www.openbioinformatics.org/annovar/ ) [ 21 ]. Somatic variants were filtered based on the following criteria: i) variants with allele depth < 10 were excluded; ii) variants with allele frequencies < 0.1 were excluded; and iii) variants with population frequencies > 1% were excluded from the further investigation based on the Exome Aggregation Consortium dataset (ExAC http://exac.broadinstitute.org ), 1000 Genomes Project ( http://www.1000genomes.org/ ) [ 22 ]. ESP6500SI-V2 and avsnp150 databases. Additionally, actionable mutations were identified using OncoKB ( http://oncokb.org ) [ 23 ]. HCC driver genes were identified by IntOGen [ 24 ]. TIMER ( https://timer.comp-genomics.org/ ) [ 25 ] database offered tumour immune infiltration analysis.

Mutation statistics and visualization

Detailed information about mutations, including their features, distribution, and enrichment in oncogenic signalling pathways, was compiled and visualized using the R package maftools (version 2.8.05) [ 26 ]. We measured overall survival (OS) from the date of the first clinic visit to the last follow-up or death. Survival analysis was visualized using the R package survival (version 3.3.1) and survminer (version 0.4.9), using R package "jskm" to make landmark analysis. We used the Oviz-Bio platform to landscape the mutation type, mutated gene, mutation frequency, and clinical data about the patient [ 27 ]. Associations between driver genes and clinical features and the difference between the rates of affected cases in the TCGA cohort and this cohort were investigated using Fisher's exact test or the χ 2 test. P less than 0.05 was deemed significant.

Clinical characteristics of the patients with HCC

One hundred and eleven patients with HCC were included in this study (17 in female, 92 in male and two unknown). The median age was 53.5 years (range 33–78). According to TNM staging, the majority of patients (44.14%, 49/111) were in stage T1b. Over 40% of patients had small lesions with 2-5 cm tumour diameters, while 14.41% had large lesions with diameters greater than 10 cm. Lymph node metastasis was common in HCC and a key step in tumour metastasis. In our study, over one-quarter of patients developed lymph node metastasis. Furthermore, 30 patients (27.03%) experienced relapses, with the most common site of recurrence being intrahepatic (50%, 15/30). Over 85% of patients had one or more risk factors, including 88 patients with hepatitis B virus infection, 31 drinkers, and 17 with diabetes.

Concerning clinical indicators, the ASL/ALT ratios in serum were less than 0.8 in 29 patients and greater than 1.5 in 14 patients. The AFP level of 56 patients was above 25 ng/ml, and the CA19-9 level of 25 patients was more than or equal to 37 U/ml. In addition, most patients had normal CEA and CA125 levels (Table  1 and Table S1).

The spectrum of somatic mutations in genes

Of these 111 patients, 86 and 25 were detected mutations in 688 genes and 508 genes, respectively. In total, we detected 14,225 somatic mutations in all patients, including 1,125 SNVs, 1,789 insertions, and 1,017 deletions. Most mutations were located in the coding region, 32.1% in exonic regions, and 0.7% in splicing regions (Fig.  1 a). The two most common types of mutations were frameshift insertion and nonsynonymous SNV (Fig.  1 b). For SNV, C > T was the major mutant form (Fig.  1 c). In addition, there were 287 synonymous mutations, 9,148 variants in the intronic region, and 602 variants in the non-coding region, which were filtered out in the following analysis.

Results of variants calling in 111 patients with liver cancer. a Distribution of mutant sites in the coding and non-coding regions of DNA. b Types and numbers of variants. c Numbers of each SNV class

We identified 57 driver genes mutated in 111 patients in this study. About 34% (38/111) of patients harboured TERT promoter mutations. The frequently mutated driver genes were TP53 (50.45%, 56/111), KMT2D (36.05%, 31/86), FAT1 (30.23%, 26/86), FAT4 (29.07%, 25/86), and KMT2C (26.74%, 23/86; Fig.  2 ). The P53 structural domain was affected most frequently (Fig.  3 a). As driver genes, TP53 and CTNNB1 had no interactions with other genes, while Histone-lysine N-methyltransferase 2 ( KMT2 ) family genes had a synergistic effect with the FAT gene family, ARID1A/B , and GNAS (Fig.  3 b). Except for these driver genes, the top five high-frequency mutation genes were MUC16 (56.98%, 49/86), APOB (52.33%, 45/86), ZFHX4 (39.53%, 34/86), FAT3 (26.13%, 29/111), and EPPK1 (22.52%, 25/111; Figure S1). Epigenetic modifiers, such as ARID1A (21.62%, 24/111), ARID2 (17.12%, 19/111), and MLL genes, were also recurrently altered.

The landscape of frequently mutated genes of liver cancer. Significantly mutated genes in 111 patients. Above, the histogram shows the number of variants of each patient. Left, the percentages of patients with mutations. Diagonally indicated the information that was not available. Different colours correspond to different types of mutations. Variants annotated as Multhit_Hit are those genes that are mutated more than once in the same sample

Clinical implications of mutations and domain and pathway enrichment analysis. a Frequently mutated Pfam protein domains in liver cancer. The bubble size is in proportion to the number of genes containing prominent display domains. b Somatic gene interactions. c Enrichment of known oncogenic signalling pathways. d Mutated genes in the RTK-RAS pathway. Tumor suppressor genes are in red, and oncogenes are in blue. e Venn plot of database-registered variants. f Location of the MUC16 mutations schematic. Red circles highlight novel missense mutations

The spectrum of somatic mutations in driver pathways

RTK-RAS (72.81%), TP53 (57.01%), Hippo (53.51%), Wnt/ß-Catenin (50%), NOTCH (49.12%), PI3K (38.60%), and Cell Cycle (31.58%) were activated frequently (Figs.  3 c and S1). We found 40 mutated genes involved in the RTK-RAS oncogenic signalling pathway, with five of the mutated genes being tumour suppressors and 35 being oncogenes. Oncogenes IRS2 (20.72%, 23/111), RET (10.81%, 12/111), and tumour suppressor genes NF1 (8.11%, 9/111) frequently mutated in the RTK-RAS pathway (Fig.  3 d). The mutations in KMT2D , CTNNB1 (21.62%, 24/111), GNAS (18.92%, 21/111), and AXIN1 (14.41%, 16/111) affected Wnt/ß-Catenin pathway and mutations in TP53 , ATM (9.01%, 10/111), RB1 (11.71%, 13/111), CDKN2A (8.11%, 9/111), and CDKN1A (5.41%, 6/111) altered cell cycle control (Figure S2). The oxidative stress pathway was altered in 9.65% of patients with mutations in NFE2L2 , KEAP1 , and CUL3 .

Clinical implications of mutations

We used the CLINVAR, dbSNP, COSMIC, and OncoKB databases to analyze the clinical significance of mutations. In our study, 528, 132, and 102 functional and meaningful variants have been registered in the dbSNP, CLINVAR, and COSMIC databases, respectively (Fig.  3 e). More importantly, 92.98% of patients had variants in targeted therapy-related genes (Fig.  2 ). Among them, 110 variants of 20 genes in 55 patients were reported as drug targets. In other words, 49.55% (55/111) of patients in this study had potentially actionable genomic alterations that required further clinical trials for HCC. For example, frameshift indels and stopain mutations in ARID1A and TSC1/2 were the targets of EZH2 inhibitors (Tazemetostat and GSK126) and mTOR inhibitors (ABI-009 and Everolimus), respectively. The nonsynonymous SNV, G3145C, in exon 21 of PIK3CA was the target of PIK3 inhibitors (Table S3). According to the follow-up results, 15 patients received targeted therapy, immunotherapy, and/or chemotherapy due to tumour recurrence (see Table S1 for treatment options). Three patients were found to carry TP53 mutations associated with sorafenib resistance. This provides evidence for the ultimate selection of lenvatinib. One patient who received sorafenib possessed CCND1 mutation that can result in sensitivity to sorafenib. Moreover, genotype CT of rs11598702 in NT5C2 suggested that one patient with this genotype may have a lower risk of toxic side effects with the use of gemcitabine. Ultimately, this patient also received chemotherapy with gemcitabine. The genetic testing results provided follow-up medication reference evidence for one-third of these 15 patients.

Three gene mutations have been found to be associated with immune infiltration. Based on the TIMER database, patients harbouring TP53 mutations had higher levels of B cells ( P  = 0.039) and macrophages ( P  = 0.023; Figure S3). In contrast, patients harboring CTNNB1 and KMT2D mutations had lower levels of CD8 + T cells ( P  = 0.003 for CTNNB1 ; P  = 0.004 for KMT2D ), macrophages ( P  < 0.001; P  = 0.004), neutrophils ( P  < 0.001; P  = 0.002) and dendritic cells ( P  = 0.004; P  = 0.007).

We performed pathogenic mutation prediction using 21 algorithms. For the 631 novel variants, at least five algorithms predicted that 292 variants were deleterious, of which we detected the most novel pathogenic mutations in MUC16 , following DNMT3A, UPF1, COL11A1 , and BIRC3 (Tables S4 and S5). The variants in MUC16 included 26 missense mutations and two frameshift deletions, of which 57.14% were novel pathogenic mutations. Most novel pathogenic mutations were located outside the SEA and tandem repeat region structural domains (Fig.  3 f).

Prognostic implications of genomic and clinical features

We compared survival between patients with or without mutations in genes. The median follow-up of 111 patients was 14.8 (IQR 0.1–84.0) months. We explored the relationship between genes with mutation frequencies greater than 15% and survival and found that alterations in four genes correlated with a poor or good prognosis. Patients harboring SPEN (18.3 vs. 15.0 months; Log-rank test, P  = 0.024; Fig.  4 a), BRCA2 (20.3 vs. 15.1[months; P  = 0.023; Fig.  4 b), and EPPK1 (18.3 vs.13.1 months; P  = 0.044; Fig.  4 c) mutations had a shorter OS, while patients harboring MUC16 (18.7 vs. 15 months; P  = 0.002, after landmark; Fig.  4 d) mutations had a longer OS. Mutations in BRCA2 (HR = 1.74), EPPK1 (HR = 1.59), and SPEN (HR = 2.02) were risk factors for patients with HCC, while MUC16 mutation (HR 0.32) was a protective factor.

Survival analysis of mutated genes. a - d Survival analysis of SPEN , BRCA2 , EPPK1 , and MUC16 . The red line indicates the mutated group, and the blue line indicates the wild-type group. e Multivariate analysis of clinical prognostic factors of HCC. Log-rank test. HR: Hazard ratio

Moreover, we conducted univariate analysis considering clinical factors (e.g., gender, age, tumour size, HBsAg) and genes with mutation frequencies > 15%. Univariate analysis revealed that SPEN ( P  = 0.028), BRCA2 ( P  = 0.026), EPPK1 ( P  = 0.047), and MUC16 ( P  = 0.022) mutations were associated with the prognosis of HCC after hepatectomy. No clinical factor was found to be associated with prognosis. Next, we included the above four genes, as well as clinical factors (i.e., gender, HBsAg, and tumor size) in our multivariable regression analysis based on clinical significance and previous literature research findings. Positive HBsAg (HR = 2.3, 95% CI 1.2–4.6) was a risk factor for the prognosis of HCC patients after curative hepatectomy. Otherwise, mutant MUC16 (HR = 0.2, 95% CI 0.1–0.5) was a prognostic protective factor (Fig.  4 e).

Associations between driver genes and clinical characteristics

We explored the relationship between driver genes and clinical characteristics. LRP1B mutations were more common in smokers (55.88% vs 44.12%; χ 2 test, P  = 0.03). Also, LRP1B mutations appeared to be associated with tumor diameter, which was more likely to be greater than 10 cm in patients with LRP1B mutations (68.75% vs. 31.25%; P  = 0.01; Table S6).

Based on the real-world evidence from 111 Chinese patients, this study enhanced our comprehension of genome analysis's clinical viability and utility in HCC. Approximately 95% of patients had mutations in driver genes and/or pathways in HCC and 48.25% potentially actionable alterations, which yielded valuable information for targeted therapy or immunotherapy. TP53 , CTNNB1, and KMT2D mutations were related to immune cell infiltration. SPEN , EPPK1 , BRCA2 , and MUC16 mutations were associated with OS. More importantly, we identified mutant MUC16 as an independent protective factor for the prognosis of HCC patients after curative hepatectomy.

We revealed hotspot mutations in 111 Chinese patients with HCC, and the genomic mutation frequency of CTNNB1 (21.6% vs. 22.6%), AXIN1 (14.4% vs. 13.7%), RB1 (11.7% vs. 11.9%) in our cohort was not significantly different from previous reports [ 28 ]. However, the mutation frequency of TP53 (50.5% vs. 56.5%) and TERT (34% vs. 45.2%) in our cohort are lower to the cohort of Wang et al. [ 28 ]. We deduced that the difference in the number of patients with HBV infection may be one of the reasons. Some studies have reported that the mutation frequency of TP53 was higher in HCC caused by HBV infection than those without HBV infection [ 28 , 29 ]. In the cohort of Wang et al., 84.8% (140/165) of patients were positive for hepatitis B surface antigen (HBsAg), while in our cohort, only 54.1% (60/111) of patients were positive for HBsAg. Moreover, Wang et al. counted more mutation types than us, including gene amplification and fusion/rearrangement. Another reason might be that the criteria for patient enrollment are different. We screened HCC patients with somatic mutations and excluded patients without somatic mutations detected by targeted-capture sequencing. This might lead to a change in the frequency of gene mutations.

The immunological analysis revealed that mutations in TP53 were related to the level of immune infiltration. A report showed that HCC patients with mutant TP53 had significantly macrophage infiltration higher than those with wild-type TP53 [ 30 ], which coincided with our results. Loss or alteration of p53 caused by TP53 mutations can regulate the recruitment and activation of immune cells, resulting in the suppression or evasion of anti-tumor immune responses [ 31 , 32 ]. TP53 mutants can reprogram macrophages to tumour-associated macrophages (TAMs) [ 33 ] and were found to relate to the infiltration of TAMs into primary tumours [ 34 ]. One possible mechanism is that TP53 mutants lead to increased expression of the chemokine CCL2. CCL2, through the CCL2-CCR2 signalling axis, recruits TAMs to the tumour area [ 34 , 35 ]. Given the profound impact of the TP53 status of the cancer cell on the immune response, previous studies have found that TP53 mutations have the potential to serve as a predictive factor in guiding anti-PD-1/PD-L1 immunotherapy [ 36 , 37 ]. Because TP53 mutation significantly increased the expression of PD-1 and PD-L1. These studies focus on lung adenocarcinoma. High expression of PD-1 or PD-L1 has been consistently identified as a reliable predictor of a positive response to immunotherapy in various types of cancer. However, the association between TP53 and PD-L1 expression varies among cancer types [ 38 , 39 ]. Indeed, no definitive biomarkers have been identified to predict the efficacy of immunotherapy in HCC. Studies on PD-L1 expression in HCC are limited or have limited clinical value due to their low occurrence frequencies. The positive rate of PD-L1 expression in HCC tumour cells ranges from 10 to 20% [ 40 ], but objective responses have been observed in PD-1 monotherapy regardless of PD-L1 expression [ 41 , 42 ]. Therefore, more comprehensive and in-depth research is needed to determine whether TP53 mutants can serve as biomarkers for immune therapy in HCC.

Approximately 25% of potentially actionable mutations are found in HCC [ 6 ]. Unfortunately, the most prevalent drivers and trunk mutations, such as TERT promoter, AXIN1 , and TP53 mutations, are currently undruggable [ 43 ]. Nevertheless, recent studies have shown that there are already relevant, targeted drugs in Phase I to III trials [ 44 ]. For instance, CTNNB1 mutation-blocking drugs are expected to be useful for precision medicine [ 44 ]. A Japanese early clinical experience explored the effect of Atezolizumab plus Bevacizumab (ATZ/BV) in HCC patients harbouring CTNNB1 Mutation and found that ATZ/BV might improve the immunosuppressive tumour microenvironment caused by CTNNB1 mutation [ 45 ]. Patients harbouring CTNNB1 mutations are mainly manifested as immune rejection in the previous study [ 46 ] and our study. Thus, 25 patients harboring CTNNB1 mutations in our study might use ATZ/BV to improve immunosuppression. Additionally, Lim et al. conducted a phase II clinical study on treating RAS-mutant HCC using refametinib or refametinib plus sorafenib, which has shown promise [ 47 ]. This provided new potential treatment options for RAS-mutant HCC patients.

Notably, we found that mutant MUC16 was an independent protective factor for the prognosis of HCC patients after curative hepatectomy. MUC16 , encoding CA125, is the second most commonly mutated gene in HCC and has the most novel potential pathogenic variants in our study. Mutant MUC16 was also found to result in a better prognosis in gastric cancer and low-grade glioma [ 48 , 49 , 50 ]. The mechanisms underlying the favourable prognosis associated with MUC16 mutations remain unclear. In gastric cancer research, the group with MUC16 mutations showed increased infiltration of tumour-killing cells and decreased presence of immunosuppressive cells [ 48 ]. The infiltration of immune cells may significantly contribute to a better prognosis. However, we did not observe any differences in immune cell infiltration between the MUC16 mutation group and the wild-type group in our study on HCC. The mechanisms by which MUC16 mutation leads to a better prognosis may vary across different tumours. Therefore, MUC16 mutations may assist in HCC prognosis and should be further studied in this tumour type. Moreover, we observed that positive HBsAg was a risk factor for prognosis in multivariable analysis, but HBsAg did not show a significant association with prognosis in univariate analysis. A possible reason is that the effects of other factors are eliminated through multivariable analysis, revealing the independent effect of HBsAg on prognosis.

There are several limitations to our study. Firstly, targeted sequencing cannot detect changes in genes excluded from the assay, structure variation, and HBV/HCV integration. Secondly, sampling a single site cannot represent the whole tumour since HCC is highly heterogeneous. Thirdly, this study should be continued to collect more information on postoperative treatment and patient survival to link drug response and prognosis with molecular profiles. Fourthly, the average follow-up time of this study was not long enough (slightly over one year) to assess persistence of the impact of mutations. Despite these limitations, we identified novel potential immunotherapy efficacy and prognosis predictors.

Linking genomic alterations to clinical practice can identify patients who are likely to benefit from targeted therapies or immunotherapy and have a poor prognosis. We hope that our findings will make routine genetic testing more accessible in clinical practice and a research context.

Availability of data and materials

The dataset(s) supporting the conclusions of this article is(are) available in the China National Center for Bioinformation (CNCB) repository, [unique persistent identifier and hyperlink to dataset(s) in https://ngdc.cncb.ac.cn/gvm/getProjectDetail?project=GVM000754 ].

Abbreviations

• Hepatocellular carcinoma

Overall survival

Single nucleotide variants

Interquartile range

Hazard ratio

Atezolizumab plus Bevacizumab

Hepatitis B virus

Hepatitis C virus

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This work was supported by the grant of Peking University Shenzhen Hospital Foundation (Grant No.KYQD2022132).

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Mengqi Song and Haoyue Cheng contributed equally to this work.

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Department of Hepatopancreatobiliary Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

Mengqi Song, Hao Zou, Kai Ma, Lianfang Lu, Qian Wei, Zejiang Xu & Chuandong Sun

Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China

Haoyue Cheng

Software Engineering, Northeastern University, Shenyang, Liaoning, China

Collage of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China

Yuanzheng Zhang

Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China

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C.D.S and Y.N.W participated in the study conception and design. M.Q.S, H.Y.C and Z.R.T have performed analysis and interpretation of the data. M.Q.S, H.Z, K.M, L.F.L, Q.W and Z.J.X were involved in data analysis and interpretation. M.Q.S, H.Y.C, Z.R.T and Y.Z.Z prepared the manuscript and figures. H.Y.C conducted the statistical analysis. Y.N.W and C.D.S edited, critically read, and revised the manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Yinan Wang or Chuandong Sun .

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12885_2024_12407_moesm1_esm.docx.

Additional file 1: Figure S1. Mutated genes in the TP53 pathway. Figure S2. The landscape of frequently mutated genes and chemotherapy-related genes in 111 patients. Figure S3. Association of immune infiltration with mutant genes.

12885_2024_12407_MOESM2_ESM.xlsx

Additional file 2: Table S1. Clinical information of 111 patients with Hepatocellular carcinoma. Table S2. List of the targeted genes. Table S3. Overview of the clinically actionable genomic alterations. Table S4. List of genes that have been detected by at least five software programs with new deleterious mutations. Table S5. The pathogenicity prediction results of the variants. Table S6. Thirteen mutation statuses stratified by clinical characteristics.

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Song, M., Cheng, H., Zou, H. et al. Genomic profiling informs therapies and prognosis for patients with hepatocellular carcinoma in clinical practice. BMC Cancer 24 , 673 (2024). https://doi.org/10.1186/s12885-024-12407-2

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High-integrity Pueraria montana var. lobata genome and population analysis revealed the genetic diversity of Pueraria genus

Xuan-Zhao Huang, Shao-Da Gong and Xiao-hong Shang contributed equally to this work.

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Xuan-Zhao Huang, Shao-Da Gong, Xiao-hong Shang, Min Gao, Bo-Yuan Zhao, Liang Xiao, Ping-li Shi, Wen-dan Zeng, Sheng Cao, Zheng-dan Wu, Jia-Ming Song, Ling-Ling Chen, Hua-bing Yan, High-integrity Pueraria montana var. lobata genome and population analysis revealed the genetic diversity of Pueraria genus, DNA Research , Volume 31, Issue 3, June 2024, dsae017, https://doi.org/10.1093/dnares/dsae017

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Pueraria montana var. lobata ( P. lobata ) is a traditional medicinal plant belonging to the Pueraria genus of Fabaceae family. Pueraria montana var . thomsonii ( P. thomsonii ) and Pueraria montana var. montana ( P. montana ) are its related species. However, evolutionary history of the Pueraria genus is still largely unknown. Here, a high-integrity, chromosome-level genome of P. lobata and an improved genome of P. thomsonii were reported. It found evidence for an ancient whole-genome triplication and a recent whole-genome duplication shared with Fabaceae in three Pueraria species. Population genomics of 121 Pueraria accessions demonstrated that P. lobata populations had substantially higher genetic diversity, and P. thomsonii was probably derived from P. lobata by domestication as a subspecies. Selection sweep analysis identified candidate genes in P. thomsonii populations associated with the synthesis of auxin and gibberellin, which potentially play a role in the expansion and starch accumulation of tubers in P. thomsonii . Overall, the findings provide new insights into the evolutionary and domestication history of the Pueraria genome and offer a valuable genomic resource for the genetic improvement of these species.

Pueraria (2 n  = 2 x  = 22) is a genus of perennial vines in the Fabaceae family comprising more than 20 species native to Asia. Pueraria montana var. lobata ( P. lobata ) ( Fig. 1a ) with its related species, Pueraria montana var. thomsonii ( P. thomsonii ) and Pueraria montana var. montana ( P. montana ), are focal of the study in Pueraria. And their nutritional value, medicinal components, and morphological characteristics exhibited widely variations. 1 P. lobata and P. thomsonii are traditional Chinese medicinal materials with a long history of medicinal use in China, first recorded in the ‘Shen Nong’s Herbal Book’. Their roots are rich in puerarin, daidzein, genistein, and other flavonoids or isoflavone secondary metabolites. 2 , 3 The difference in morphological characteristics is that the roots of P. thomsonii are more expanded than those of P. lobata . However, P. montana is a variant with no expanded roots, exhibiting great difference with P. lobata and P. thomsonii ( Fig. 1b ). The root of P. montana does not contain puerarin, so it has low nutritional and medicinal value but strong resistance and can survive at low temperature. 4

Morphology and genome features of the P. lobata . (a) Morphological characteristics of P. lobata . (left, vines and leaves; middle, flower; right, root). (b) Evolutionary relationships of P. lobata , P. thomsonii , and P. montana with their divergence time (Mya, million years ago). (c) The landscape of genome features of P. lobata and P. thomsonii . (I) length of individual chromosomes; (II) GC contents; (III) repetitive sequences density; (IV) gene density; (V) DNA-Transposable elements density; (VI) long terminal repeat (LTR) density; (VII) inner lines indicate syntenic blocks.

P. montana could be differentiated from P. thomsonii and P. lobata based on significant differences in root morphology and nutrient content. However, it is challenging to distinguish P. thomsonii and P. lobata by traditional morphological characteristics or nutrient content. 5 , 6 The need for a precise classification of P. thomsonii and P. lobata significantly limits their potential value in medicine and nutrition. 7 In the past, DNA molecular marker technology and chloroplast genomic information have aided in classifying and utilizing Pueraria species, especially in distinguishing between P. thomsonii and P. lobata . 8–10 However, genome information has more significant advantages for addressing these issues, and high-quality genome assembly will provide the basis for detecting genomic variation and exploring evolutionary history. Advances in sequencing technology and reduced costs have enabled large-scale genome sequencing of plant populations, promoting plant research significantly. 11 , 12 Population genomic information can reveal populations’ genetic composition and diversity, effectively solve the challenging problem of classifying closely related species, better elucidate the evolutionary relationships among different subgroups and individuals, and provide critical evidence for species’ domestication and origin history. 13–15 Currently, research on the genomics and population genetics of Pueraria species needs to be developed more. Therefore, integrating these approaches to achieve a more precise classification of Pueraria species and exploit their potential genetic resources is of great significance.

The high-quality genomes of P. thomsonii and P. montana at the chromosome level were obtained in the previous study, and multi-omics analysis was used to comprehensively explore the biosynthetic pathways of critical secondary metabolites, such as isoflavones and puerarin in P. thomsonii . 4 , 16 Additionally, a comparison of gene families between P. thomsonii and P. montana was conducted to elucidate the underlying reasons behind distinct metabolic and cold-adaptation characteristics exhibited by these two species. 4 However, the more comprehensive comparative analysis of the genomes, phylogeny, and population genetics among the three Pueraria species has yet to be elucidated.

In this study, a high-quality chromosome-level genome of P. lobata was constructed using a combination of PacBio reads, paired-end reads, ONT reads, and Hi–C data. In addition, it generated an updated genome of P. thomsonii by removing the redundant haploid contigs. Whole-genome duplication (WGD) events, gene family expansion, and contraction occurring in Pueraria plants were explored through comparative genomics analysis. Whole-genome sequencing of 121 Pueraria accessions was used to address the evolutionary relationship among the species and identify selective signatures during domestication in P. thomsonii population. These results provide novel insights into the evolutionary history of the Pueraria genome and offer comprehensive information for breeding in the future.

Sample preparation and sequencing

The P. lobata accession ‘YG-19’ was selected for genome sequencing, having been grown in Luojiao Village, Guiping City, Guangxi Province. Genomic DNA was isolated from young leaves using a standard CTAB protocol and its integrity was verified by agarose gel electrophoresis. The genome sequencing was performed using Single-molecule real-time (SMRT) PacBio sequencing libraries, prepared using the SMRTbell Express Template Prep Kit 2.0 by Pacific Bioscience recommendation. The resulting libraries were sequenced on a PacBio Sequel II platform. Additionally, paired-end reads sequencing was performed using libraries selected for PE150 and sequenced using a BGIseq 500 sequencer. For ONT and Hi–C sequencing, the ONT and Hi–C libraries were prepared following the manufacturer’s instructions and the genome was sequenced on the Nanopore PromethION and BGI MGISEQ-2000 using standard protocols.

Genome assembly and quality assessment

For the P. lobata genome, a de novo assembly strategy was adopted. Short-read paired-end sequencing reads were used to count the total number and frequency of k-mer ( k  = 17) using Jellyfish software (v2.2.9), 17 and then Genomescope (v1.0) 18 was used to predict genomic features. Pacbio long reads were preliminarily corrected and assembled with NextDenovo (v2.5.0,  https://github.com/Nextomics/NextDenovo ), and both long reads and short reads were used to polish the resulting contig using NextPolish (v1.4.0). 19 We used 3D-DNA pipeline 20 to correct the resulting contigs with the Hi–C data, and then the PurgeHaplotigs software (v1.1.2) 21 was used to remove the redundant contigs. The final draft genome was assembled into scaffolds with Hi–C data using Juicer 22 and 3D-DNA pipeline. 20 These scaffolds were then manually curated using Juicebox. 23 Genomic sequence gaps were filled using TGS-gapcloser (v1.1.1) 24 and LR-gapcloser 25 in combination with ONT ultra-long reads (> 10 kb). Finally, Racon (v1.5.0) 26 with Pacbio reads and Pilon (v1.23) 27 with BGI short-reads were used to do a final error correction of the genomic gaps on the padding.

The quality of the P. thomsonii genome was improved by removing redundant sequences resulting from the high heterozygosity. The genome file was obtained from a previous study, 16 and genome size and heterozygosity were estimated by using the same process as applied to P. lobata genome. Redundant sequences were removed using Purge_Dups software (v1.2.5). 28 Finally, this updated version P. thomsonii genome was employed for subsequent analyses and evaluations.

The genome completeness was evaluated using BUSCO (v5.3.0) which contained 2,326 genes in the eudicots_odb10 database. 29 Short reads were mapped with BWA software (v0.7.17), 30 Pacbio long reads and ONT reads with minimap2 31 to assess the accuracy of the assembled genome. Picard software (v2.27.1) ( https://broadinstitute.github.io/picard ) was used to remove PCR duplicates in the short-read alignment, and BCFtools commands mpileup and call (v1.9) 32 were then used for SNV calling.

Genome annotation

The repetitive sequences in the P. lobata and P. thomsonii genomes were identified by using a combination of homologous similarity and de novo prediction. In the homology-based annotation step, repetitive sequences were predicted using RepeatMasker (v4.0.9) 33 and RepeatProteinMasker (v4.0.9) by searching against the RepBase library ( http://www.girinst.org/repbase ). During the de novo step, de novo repeat libraries were constructed based on the genome sequences using the de novo prediction program RepeatModeler (v1.0.11, http://www.repeatmasker.org/RepeatModeler/ ). Subsequently, the de novo repeat library was used to identify repetitive sequences using RepeatMasker (v4.0.9). We then combined all the repeat prediction results to remove redundancy and obtain the final set of the genome repeat sequences. Additionally, tandem repeats in the genome were identified using Tandem Repeats Finder ( http://tandem.bu.edu/trf/trf.html ). Three methods were used to predict protein-coding genes in the genomes of P. lobata and P. thomsonii , including homology-based prediction, de novo prediction, and transcriptomes data prediction. AUGUSTUS (v3.3.2), 34 Genscan (v1.0), 35 and GlimmmerHMM (v3.0.4) 36 were used to perform de novo gene prediction. The protein sequences of six species ( Medicago sativa , Oryza sativa , Melilotus albus , Medicago truncatula , Glycine max , and Arabidopsis thaliana ) were aligned to our genomes for homology-based gene prediction, and the structure of coding genes was predicted using Exonerate (v2.4.0). 37 The RNA sequencing reads were assembled using HISAT (v2.1.0) 38 and StringTie (v2.1.4), 39 and then TransDecoder ( https://github.com/TransDecoder ) were used for gene prediction. The results of the three methods are integrated and filtered to form a non-redundant, more complete gene set using MAKER (v2.31.10). 40

To predict gene functions, protein-coding genes to different protein databases, such as NR, UniProt, and Pfam, using BLASTP (with an E-value threshold of 1e-5) (v2.12.0+) 41 were mapped based on sequence similarity. We performed KEGG annotation using KOBAS (v3.0), 42 and extracted Gene Ontology information from the respective UniProt descriptions. Next, InterProScan (5.52-86.0) 43 was used to search the InterPro database and obtain the proteins’ conserved sequences, motifs, and structural domains.

Comparative genomics analysis

Orthofinder (v2.5.4, -S blast) 44 was used to detect gene families of P. lobata , P. thomsonii , P. montana , as well as the selected 12 species, including eight Fabaceae species outside the Pueraria genus ( A. duranensis , C. cajan , C. arietinum , G. max , M. sativa , P. vulgaris , M. truncatula , and P. sativum ), three non-Fabaceae dicotyledons ( V. vinifera , A. thaliana , and P. trichocarpa ) and one monocotyledon ( O. sativa ). Multiple sequence alignments were performed on the single-copy genes of 15 species using Muscle (v3.8.1). 45 The phylogenetic tree was then reconstructed using the maximum likelihood method with RAxML (v8.2.12). 46 The protein sequence alignment was converted to codon alignment with PAL2NAL ( http://www.bork.embl.de/pal2nal/ ). The MCMCTree program from the PAML package was used to estimate the species divergence times based on the TimeTree database. 47 Gene family expansions and contractions were analyzed using CAFE5 48 with default settings based on the identified phylogenetic tree and gene family statistics. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of expanding gene families were performed using the R package clusterProfiler (v4.2.2, p  < 0.05, q  < 0.2). 49 We used the python version of MCScan (v20101014) 50 to identify syntenic blocks among P. lobata , P. thomsonii , and P. montana .

Analysis of WGD events

The paralogs and one-to-one orthologs from G. max , 51 V. vinifera , 51 P. thomsonii , P. lobata , and P. montana 4 were performed all-versus-all BLASTP (v2.12.0+) similarity searches with an E-value threshold of 1e−5. WGDI (v0.6.0) 52 was used to identify syntenic blocks and calculate synonymous substitutions rate (Ks) with default parameters based on the result of alignment within or between the selected species and corresponding genome annotation. The syntenic blocks of paralogs or one-to-one orthologs and the distribution of Ks values were used to determine WGD events.

Re-sequencing and variant calling

Young leaves of 110 accessions with P. thomsonii and P. lobata were individually harvested, and genomic DNA was extracted. DNA libraries construction and re-sequencing were performed with DNBSEQ workflow (BGI), generating 5.56 Tb reads with 150-bp paired-end. All Pueraria accessions re-sequenced in this study were identified, collected, and deposited in the Guangxi Academy of Agricultural Sciences, Nanning, China. Additionally, the genome sequencing data of 11 P. montana accessions were downloaded from our previous study. 4 Raw re-sequenced data was filtered by SOAPnuke software 53 with the following parameters: ‘-n 0.01 -l 20 -q 0.3 --adaMR 0.25 --ada_trim --polyX 50 --minReadLen 150’. The clean reads were mapped to the P. lobata genome by BWA with default parameters. Samtools (v1.15.1) was used to sort the alignment results and convert them into BAM files, and PCR duplicates were marked with Picard. Variant detection was performed by GATK (v3.8-0-ge9d8) 54 following the prescribed procedures for variation disclosure. GATK IndelRealigner locally realigned BAM files to eliminate mismatches around small-scale deletions and insertions. The genome Variant Call Formats (gVCFs) for each accession were generated by GATK HaplotypeCaller, and all gVCF files were merged to generate the VCF file of all accessions using GATK CombineGVCFs and GenotypeGVCFs. Furthermore, the hard filter was performed to raw variants using GATK VariantFiltration with following parameters: for SNPs, ‘QUAL < 30.0 || QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0’; for InDels, ‘QUAL < 30.0 || QD < 2.0 || ReadPosRankSum < -20.0 || InbreedingCoeff < -0.8 || FS > 200.0 || SOR > 10.0’. The SNPs and InDels of filtered variants were selected to screen further using parameters of ‘--maf 0.05 --max-missing 0.9 --max-alleles 2’ with VCFtools (v0.1.16), 55 generating a high-confidence SNPs and InDels set with biallelic, respectively. For InDels, only those with a length of less than 50 bp will be retained. The identified variants were annotated using SnpEff (v5.1d) 56 with P. lobata genome and its gene annotation file. SNPs and InDels were categorized based on their chromosome position and effects.

Population genetic analysis

Based on the high-confidence SNPs set, we implemented LD pruning with PLINK (v1.90b6.21) 57 option ‘--indep-pairwise 50 5 0.2’, generating a core set of 1,061,987 SNPs for population genetic analyses. The maximum-likelihood tree was constructed using IQ-TREE (v2.2.0.3), 58 based on the best model (TVM + F + I + R8) determined by the Bayesian information criterion, and the format conversion of the input file was performed using vcf2phlip (v2.8) ( https://github.com/edgardomortiz/vcf2phylip ). Bootstrap support values were calculated using the ultrafast bootstrap approach (UFboot) with 1,000 replicates. The closely related species G. max was used as an outgroup downloaded from a previous study. 59 The phylogenetic tree was visualized by iTOL ( https://itol.embl.de ). The genetic ancestry was examined using ADMIXTURE (v1.3.0) 60 with K values (the putative number of population) from 2 to 10, and the cross-validation (CV) was set to 5-fold. Even though the cross-validation (CV) error gradually decreased from K  = 2 to 9, we chose K  = 4 to divide groups or subgroups, rather than K  = 9 with the minimum CV error. The reasons are as follows: (1) from K  = 4 on, the CV error decreases (or increases) at a slower rate and falls within the narrow range of 0.31006 to 0.31749; (2) at K  = 4, the corresponding ancestries are enough to categorize most P. montana , P. lobata , and P. thomsonii accessions into distinct groups or subgroups. Principal component analysis (PCA) was performed using GCTA (v1.93.2beta). 61 Considering the differences in genetic structure caused by the diversity of P. lobata accessions, the top three principal components were used to clearly distinguish the different groups or subgroups. Based on the population structure analyses mentioned above, 38 ungrouped accessions were removed from subsequent analyses. Additionally, the GMM93 ( P. montana ) accession within the subgroup G3-1 was suspected to be mis-identified and excluded from subsequent analysis. The squared correlation coefficient ( r 2 ) between pairwise SNPs in subgroups G3-1 and G3-2 was calculated to estimate the LD decay using PopLDdecay (v3.42). 62 Nucleotide diversity ( π ) and fixation index ( F ST ) of different populations were calculated by VCFtools with 50 kb windows sliding in 20 kb steps; Similarly, Tajima’s D value was calculated with 50 kb windows, and the statistical significance between subgroup G3-1 and G3-2 was compared using the two-sided Wilcoxon rank-sum test.

Identification of selective sweeps

Two statistical methods were employed to identify candidate genomic regions potentially affected by selection based on the high-confidence SNPs set. The cross-population composite likelihood ratio test (XP-CLR, https://github.com/hardingnj/xpclr ) was performed in subgroups G3-1 and G3-2 with 50 kb windows sliding in 20 kb steps. The windows with the top 5% XP-CLR score were taken as outliers. Based on the nucleotide diversity ( π ) of subgroups G3-1 and G3-2 calculated above, the statistic π (G3-2/G3-1) was then calculated concerning subgroups G3-1 and G3-2. An abnormally low value (5% outliers) suggests selection in subgroup G3-2, and the top value (5% outliers) indicates selection in subgroup G3-1. The overlapping regions with the top 5% XP-CLR score and low or top 5% π (G3-2/G3-1) value were assigned to candidate selective regions. Adjacent putative regions were merged into a single region to represent the effect of a single selective sweep. Haplotype differentiation patterns of gene loci were visualized by RectChr (v1.36, https://github.com/BGI-shenzhen/RectChr ).

Genome sequencing, assembly, and annotation

Multiple sequencing datasets were used to assemble the genome of P. lobata ( Fig. 1a ). First, k-mer analysis ( k  = 17) revealed an estimated genome size of 1.06 Gb using 144.68 Gb (136 × coverage) of paired-end reads ( Supplementary Fig. S1 , Supplementary Table S1 ). Pacbio sequencing (182.63 Gb, 172 × coverage) was used for the initial contig assembly, followed by polishing and quality improvement of the resulting sequences ( Supplementary Table S1 ). Given the high heterozygosity in specific genomic regions, this may lead to the assembly of regional duplication. We further improved the assembly by removing redundant contigs, resulting in the removal of 193 Mb of sequences from the initial 1.21 Gb assembly ( Supplementary Table S2 ). The contigs were then grouped, sorted, and oriented using Hi–C interaction datasets (152.41 Gb, 144 × coverage) to achieve a chromosome-level assembly ( Supplementary Table S1 ). To enhance the continuity and completeness of the P. lobata genome, we used ONT ultra-long reads (82 Gb, 77 × coverage) with N50 of 36.56 kb to fill gaps in the genome ( Supplementary Table S1 ). As a result, we obtained a relatively complete genome of P. lobata , with a size of 1.05 Gb, and 91.69% of the sequences were anchored to 11 chromosomes, including 7 gap-free chromosomes, and the scaffold N50 was 86.86 Mb ( Fig. 1c , Supplementary Fig. S2,-S3 , Supplementary Table S3 ). Additionally, we performed a redundancy removal process on the previously published P. thomsonii genome, resulting in a new version of the genome with a size of 1.03 Gb and scaffold N50 was 98.03 Mb ( Fig. 1c , Supplementary Table S4 ).

The quality of de novo assembled P. lobata genome and the updated P. thomsonii genome were evaluated by using multiple methods. Accuracy of the two genomes was demonstrated by high mapping rates obtained from different sequencing data sets, including PacBio long reads, short paired-end reads, and ONT reads. The mapping rates for paired-end reads of P. lobata and P. thomsonii were 99.35% and 99.16%, respectively, and homozygous single-nucleotide variations (SNVs) accounted for approximately 0.0019% and 0.0057% of the two genomes ( Supplementary Table S5 ). Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis estimated the completeness of the P. lobata and P. thomsonii genomes to be 99.0% (2,303 out of 2,326) and 98.5% (2,291 out of 2,326), respectively, indicating the high completeness of our genomes ( Supplementary Table S6 ).

Subsequently, repetitive sequence elements, protein-coding genes, and non-coding RNAs of both genomes were identified. It predicted 679.32 Mb (64.7%) and 651.91 Mb (62.9%) of repetitive sequences from P. lobata and P. thomsonii assemblies, respectively ( Supplementary Table S7 ), which is consistent with previous reports. 16 In P. lobata genome, Long Terminal Repeats (LTRs) were the most abundant repetitive elements (17.2%), followed by DNA transposable elements (3.67%) and LINEs (3.35%). Among the LTRs, Gypsy and Copia accounted for 9.91% and 6.84%, respectively. In P. thomsonii genome, LTRs (14.3%), LINEs (5.02%), and DNA transposable elements (3.42%) were the most abundant repetitive sequences, with Gypsy (6.37%) and Copia (7.41%) being the best represented among the LTRs.

Using homology analysis, de novo prediction, and transcriptome evidence, it annotated the protein-coding genes of P. lobata and P. thomsonii after masking repetitive sequences. It identified 38,386 genes in P. lobata genome, with an average gene length of 5,312 bp and average coding DNA sequences (CDS) length of 1,274 bp, and predicted 38,891 genes in P. thomsonii genome, with an average gene length of 4,477 bp and an average CDS length of 1,254 bp ( Supplementary Table S8 ). We further compared the number of genes, gene length, and CDS length among three Pueraria species ( P. lobata , P. thomsonii , and P. montana ) and Glycine max . The gene number of three Pueraria species was found to be similar, while P. lobata exhibited a more significant average gene length and average CDS length compared to the other three species ( Supplementary Table S8 ).

Compared to public databases, including Interpro, Pfam, GO, Uniprot, Pathway, and KEGG, approximately 97.27% (37,339) and 97.38% (37,873) of these genes were functionally annotated in P. lobata and P. thomsonii genome, respectively ( Supplementary Table S9 ). 3,432 and 3,072 non-coding RNAs were also identified, including microRNAs, transfer RNAs, ribosomal RNAs, and small nuclear RNAs ( Supplementary Table S10 ).

Comparative genomic and evolutionary analysis

To explore the genomic evolution of Pueraria , we clustered genes from the three Pueraria species ( P. lobata , P. thomsonii , and P. montana ), eight Fabaceae species outside the Pueraria genus ( A. duranensis , C. cajan , C. arietinum , G. max , M. sativa , P. vulgaris , M. truncatula , and P. sativum ), and other four representative species, including three non-Fabaceae dicotyledons ( V. vinifera , A. thaliana , and P. trichocarpa ) and one monocotyledon ( O. sativa ) ( Fig. 2a ). In total, we identified 35,121 gene families, with 17,416, 17,487, and 17,037 families detected in P. lobata , P. thomsonii , and P. montana , respectively ( Supplementary Table S11 ).

Genome comparison and evolution analysis. (a) Gene numbers of each category in 15 representative species. (b) Venn diagram showing the shared and unique gene families among P. lobata, P. thomsonii , P. montana , and G. max . (c) Phylogenomic analysis and expansion/contraction of the gene family. The numbers on the nodes represent the species divergence time with the confidence range list in brackets. (d) Genomic collinearity between three Pueraria crops. (e) Distribution of synonymous substitutions (Ks) values of syntenic paralogs in five selected species. Estimated whole-genome duplication (WGD) and whole-genome triplication (WGT) events occurring in Pueraria were highlighted.

The genes of three Pueraria species re-clustered by using G. max as the outgroup. These four species shared 18,203 gene families, while P. lobata , P. thomsonii , and P. montana shared 19,470 gene families, which can be considered a conservative gene set for the Pueraria species ( Fig. 2b ). It found 237 and 324 unique gene families in the genomes of P. lobata and P. thomsonii , including 1,074 genes and 1,286 genes respectively, which were significantly less than that (1,094 unique gene families; 3,954 genes) in P. montana genome ( Fig. 2b ). Importantly, P. lobata shared the majority gene families with P. thomsonii , indicating their closer evolutionary relationship.

Single-copy gene families obtained from 15 representative plant genomes were used to reconstruct a phylogenetic tree and estimate divergence times ( Fig. 2c ). The results supported a close evolutionary relationship among three Pueraria crops. Specifically, our phylogenetic analysis shows that P. lobata and P. thomsonii have the closest relationship, with a divergence time of 1.1 Mya. The common ancestor of P. lobata and P. thomsonii diverged from P. montana approximately 5.8 Mya. Moreover, the estimated divergence time between Pueraria species and G. max was 15.3 Mya. These findings provide valuable insights into the evolutionary history of Pueraria genus and its relationship with other plant species.

Gene family expansion and contraction were investigated in 15 representative plants ( Fig. 2c ). There were 800/624, 675/819, and 915/1133 expanded/contracted gene families in P. lobata , P. thomsonii , and P. montana genome, respectively. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the expanded gene families of each Pueraria species were conducted ( Supplementary Fig. S4 ). GO enrichment analysis indicated that the expanded gene families of P. lobata and P. thomsonii are related to stress responses, such as ‘response to oxidative stress’, ‘cellular response to salt stress’, and ‘response to herbicide’. This result suggests that P. lobata and P. thomsonii may be more capable of adapting to diverse environmental conditions. KEGG enrichment analysis shows that the expanded genes of P. lobata are involved in ‘Photosynthesis’, ‘Oxidative phosphorylation’, and ‘Ribosome’. Additionally, it was found that ‘Sesquiterpenoid and triterpenoid biosynthesis’ and ‘Quorum sensing’ are the most enriched terms of P. thomsonii and P. montana , respectively. Overall, the expansion of distinct gene families may have driven the observed differentiation among three Pueraria species. We also observed a significant expansion of gene families related to ‘Isoflavones biosynthesis’ and ‘Flavones and flavonols biosynthesis’ in the common ancestor of P. lobata and P. thomsonii ( Supplementary Fig. S5 ). In contrast, it does not exist in P. montana . This result indicates the potential role of these expanded gene families in enhancing the medicinal properties of Pueraria species.

Syntenic analysis and whole-genome duplication

We conducted genome comparisons among P. lobata , P. thomsonii , and P. montana , and identified syntenic blocks that are distributed across 11 chromosomes, indicating a strong interspecies synteny ( Fig. 2d ). A total of 500 syntenic blocks comprising 27,295 gene pairs were identified between P. lobata and P. thomsonii , of which 309 blocks containing 25,851 gene pairs were located on the same chromosome. Furthermore, it observed 361 syntenic blocks with 23,285 gene pairs shared between P. lobata and P. montana , with 231 blocks containing 18,083 gene pairs located on the same chromosome ( Supplementary Table S12 ). The largest block with the highest number of genes between P. lobata and P. thomsonii was located at the end of chromosome 9 and contained 951 pairs of orthologous genes, covering 26.7 Mb and 26.9 Mb of genomic regions on the P. lobata and P. thomsonii genomes, respectively. Meanwhile, 425 syntenic blocks containing 23,266 gene pairs were detected between P. thomsonii and P. montana , with 17,789 gene pairs located on the same chromosome ( Supplementary Table S12 ). High chromosomal relatedness was observed among the three Pueraria genomes, with an almost one-to-one synteny relationship between P. lobata and P. thomsonii , providing genomic evidence for the evolution of Pueraria species.

Two peaks of Ks values corresponding to two whole-genome duplication (WGD) events were detected in the Pueraria genus by analyzing synonymous substitutions (Ks). The first WGD event, represented by the ancient peak (Ks = 1.666–1.714), was consistent with the whole-genome triplication (γ event) in core eudicots, 63 as observed in V. vinifera (Ks = 1.240) and G. max (Ks = 1.738) ( Fig. 2e ). 64 , 65 The second WGD event, shared by Fabaceae, 65 is indicated by the recent peak (Ks = 0.563–0.572) ( Fig. 2e ). Syntenic blocks of paralogs with three Pueraria species indicate that the recent Ks peak was generated by a WGD event rather than segmental duplication ( Supplementary Fig. S6 ). Based on these results, we presume that the three Pueraria species did not undergo a lineage-specific WGD event.

To investigate the order of species differentiation, the syntenic blocks of orthologs and Ks values for two pairs of three Pueraria species were identified. We note that the Ks peak of P. thomsonii versus P. lobata (Ks = 0.004) is significantly lower than that in P. thomsonii versus P. montana (Ks = 0.043) or P. lobata versus P. montana (Ks = 0.041) ( Supplementary Fig. S7 ). According to the difference of Ks peak, it speculates that P. lobata and P. thomsonii are more closely related, which is also confirmed by phylogenetic tree ( Fig. 2c ).

Genomic polymorphism and population structure

To investigate genomic polymorphisms in three Pueraria species, a total of 121 Pueraria accessions were collected in this study, including 64 P. thomsonii and 46 P. lobata accessions with an average depth of 48×, 11 P. montana accessions from our previous study with an average depth of 28 × ( Supplementary Table S13 ). The P. thomsonii and P. lobata accessions sequenced in this study are mostly distributed in southern China, with over half of the accessions from Guangxi province and the remaining accessions mainly from Guangdong, Jiangxi, and Yunnan provinces ( Supplementary Fig. S8 ). All sequencing reads were mapped to P. lobata genome, and all mapping rates ranged from 96.07% to 99.20%, leading to the identification and annotation of 14,132,140 high-confidence single-nucleotide polymorphisms (SNPs) and 1,799,556 insertions/deletions (InDels) ( Supplementary Table S13 ). Of these, 577,166 SNPs (2.71%) and 26,355 InDels (0.90%) were in coding regions, with 308,061 nonsynonymous SNPs and 14,573 frameshift InDels that could potentially cause significant changes in protein sequences ( Supplementary Table S14–S15 ). Most of SNPs were found in intergenic regions (52.91%), followed by upstream (17.20%), downstream (16.25%), and intronic regions (9.36%).

We aimed to determine the evolutionary relationships among the three Pueraria species by examining their population genetics. To this end, we explored the phylogenetic relationships and population structure based on a core dataset of SNPs (Method). It conducted a phylogenetic tree and found that the 121 Pueraria accessions could be divided into three clades ( Fig. 3a ). Clade1 comprises nine P. montana accessions, representing the P. montana population. Clade2 and Clade3 harbor most P. lobata and P. thomsonii accessions, standing for the P. lobata and P. thomsonii populations ( Supplementary Table S13 ). Combining the results of estimated ancestral component and principal component analysis (PCA), G1 possesses a significant evolutionary distance from both G2 and G3 (Clade3) ( Fig. 3b , Supplementary Fig. S9 ). Notably, eight P. lobata accessions in G2 differ from those in G3 (Clade3), as revealed by principal components 2 (PC2) and 3 (PC3), suggesting an earlier divergence of G2 ( Fig. 3a–c , Supplementary Fig. S9 ). As the K value increased from 3 to 9, G3 (Clade3) could be further subdivided into two subgroups and 36 ungrouped accessions, which aligns with the optimal K value ( K  = 4) we have chosen ( Fig. 3b , Supplementary Fig. S10 ). The classification criterion of the subgroup is as follows: (i) Any individual with a major ancestry component (> 70%) will be categorized into the corresponding subgroup; (ii) Subgroups must constitute a monophyletic branch in the phylogenetic tree. Subgroup G3-1 was predominantly composed of P. lobata accessions, with origins from Guangxi and Yunnan provinces ( Supplementary Table S13 ). The ungrouped accessions in G3 exhibit more admixture and relatively scattered PCs ( Fig. 3b–c , Supplementary Fig. S9 , S 11 ), likely resulting from wide crossing or wild introgression during domestication and cultivar development involving several related species. 66 Subgroup G3-2 includes most P. thomsonii accessions whose genetic structure demonstrates a remarkable similarity in all the analyses conducted, which is substantially different from the situation observed in other groups or subgroups ( Fig. 3a-c , Supplementary Fig. S9 , S 11 ). In summary, our results suggest that P. thomsonii (G3-2) was derived from P. lobata as a subspecies.

Phylogeny, population structure and genomic diversity of Pueraria accessions. (a) Maximum likelihood phylogenetic tree of 121 Pueraria accessions, using G. max as outgroup. The color of the branches represents different groups or subgroups. (b) Model-based clustering analysis with different numbers of groups or subgroups. Each vertical bar represents one individual, and colored segments represent the proportions of ancestral components. (c) PCA plot with the first two principal components. Colors of different groups or subgroups correspond to those in the phylogenetic tree. The more detailed principal components and quantities of G1 and subgroup G3-2 were shown in the zoomed-in view, respectively. (d) Nucleotide diversity (π) and fixation index ( F ST ) across the four groups and subgroups. Values in circles represent measures of π for the groups or subgroups, and values between pairs indicate F ST value.

Genomic diversity and selection signatures during domestication

Nucleotide diversity ( π ) and fixation indexes ( F ST ) were estimated to compare genome-wide genetic diversity among the above four major groups and subgroups. Despite having a larger accession size, subgroup G3-2 exhibited significantly lower nucleotide diversity ( π =1.79 × 10 −4 ) than subgroup G3-1 (π=2.45 × 10 −4 ) ( Fig. 3d ). This finding is likely due to that the accessions in subgroup G3-1 were mainly from wild populations, while those in subgroup G3-2 were mostly from landraces or cultivars ( Supplementary Table S13 ). The decrease in nucleotide diversity in subgroup G3-2 suggests that it may has undergone more artificial selection. The level of genetic differentiation between subgroup G3-1 and G2 is the lowest ( F ST  = 0.2939), followed by that between subgroup G3-1 and G3-2 ( F ST  = 0.3064), with the remaining levels of genetic differentiation among the other groups or subgroups being significantly higher ( Fig. 3d ). A significant proportion of SNPs (83.4%) identified in subgroup G3-2 are shared with subgroup G3-1, indicating that subgroup G3-1 has served as a primary source of genetic material for subgroup G3-2, potentially explaining the low level of genetic differentiation between them ( Supplementary Fig. S12a ). Linkage disequilibrium (LD), as indicated by r 2 , was calculated to compare LD decay between subgroups G3-2 and G3-1. The result showed that the LD decay reached half its maximum average correlation coefficient at a shorter distance in subgroup G3-2 (0.15 kb) than in subgroup G3-1 (2.61 kb) ( Supplementary Fig. S12b ). This finding is consistent with previous studies on G. max or V. vinifera , where wild populations and cultivars exhibited similar results. 14 , 67 The faster LD decay observed in subgroup G3-2 implies that it may have experienced stronger selection, as supported by the greater Tajima’s D value of subgroup G3-2 ( Supplementary Fig. S12c ).

The most significant phenotypic difference between P. thomsonii and P. lobata is that the root tuber of P. thomsonii is larger and accumulates a higher content of starch, traits that were likely selected during domestication. 68 , 69 To identify potential selective signals associated with P. thomsonii domestication, the cross-population composite likelihood ratio test (XP-CLR) and nucleotide diversity ratio were performed by comparing subgroup G3-2 versus G3-1. Combining these approaches, we identified only five selected regions in subgroup G3-1, consistent with our hypothesis that it underwent relatively weak selection. In contrast, 533 selected regions were found in subgroup G3-2, covering 3.85% (40.41 Mb) of the P. lobata genome and harboring 1,774 genes ( Fig. 4a–c , Supplementary Table S16 ). Previous studies conducted by our team have elucidated the puerarin synthesis pathway specific to P. lobata and P. thomsonii . 16 Within the selected region, we discovered the PlChr10.CHS (PlobChr10G00353750) is situated in the puerarin synthesis pathway, potentially contributing to variations in puerarin content between P. lobata and P. thomsonii ( Fig. 4a , Supplementary Table S17 ). In addition, several genes related to plant hormones were detected in selected regions, including PlChr01.IAA (PlobChr1G00059530), PlChr01.GA2ox (PlobChr1G00059170), PlChr07.GH3s (PlobChr7G00257580 and PlobChr7G00273940) and PlChr08.YUC (PlobChr8G00206820) ( Fig. 4a–c , Supplementary Table S17 ). Previous research has demonstrated that genes associated with auxin are significant contributors to starch synthesis. Specifically, the YUC gene, responsible for auxin synthesis, considerably influences the transcriptional levels of three key genes involved in starch granule synthesis and affects the accumulation of starch granules within the root apex of A. thaliana . 70 Gibberellin regulates the ratio of xylem to phloem in vascular tissue, 71 and the abnormal vascular tissue, consisting primarily of xylem and phloem, is responsible for the expansion of tubers. 72 In addition, the differentiation patterns of haplotypes for PlChr01.GA2ox and PlChr08.YUC were investigated, and it was determined that they carried unique haplotypes in subgroups G3-2 and G3-1, respectively ( Fig. 4d–e ). In summary, it suggests that genes associated with the synthesis of auxin and gibberellin, which have undergone strong selective pressure, may have played a significant role in the domestication process of root expansion and high starch accumulation in P. thomsonii .

Identification of selective sweeps associated with domestication traits in P. thomsonii . (a) Manhattan plot of the selective sweeps identified from the comparison of subgroup G3-2 and G3-1 by XP-CLR. Dashed lines represent the top 5% of the XP-CLR scores. Functionally characterized candidate genes associated with plant development were highlighted. (b–c) Distribution of nucleotide diversity (π) ratio with the upstream and downstream regions of PlChr01.GA2ox and PlChr08.YUC . Horizontal dashed lines represent the bottom 5% of the π ratio. Vertical dashed lines indicate the position of PlChr01.GA2ox and PlChr08.YUC . (d–e) Haplotype distributions were shown for SNPs within PlChr01.GA2ox and PlChr08.YUC . Each column represents the SNPs of PlChr01.GA2ox and PlChr08.YUC . Each row indicates the accessions of subgroups G3-2 or G3-1. Four colors represent different types of SNP allele respectively.

Pueraria belongs to the Fabaceae family and is a plant with significant economic value widely cultivated throughout Asia. Traditional Pueraria categories need to be clarified for evolutionary studies and exploitation of medicinal usage, mainly because of a lack of genomic and population genetic information. In this study, based on ONT reads and PacBio long reads, we constructed a high-quality genome of P. lobata with some chromosomes without gaps. The continuity of the P. lobata genome was improved by filling gaps with ONT ultra-long reads, although the specific regions corresponding to telomeres and centromeres remain unidentified. Completing these regions would necessitate the utilization of high-fidelity (HiFi) reads with low error rates. 73–75 Through a comparison of the sizes of P. lobata genome, P. montana genome, 4 and the previous P. thomsonii genome, 16 we observed that the genome size of P. lobata (~1.05 Gb) is more closely related to that of P. montana (~978 Mb), while exhibiting a substantial difference from that of previous P. thomsonii (~1.38 Gb). Based on k-mer analysis ( k  = 17) with paired-end reads, the size and heterozygosity of the previous P. thomsonii genome were reevaluated to be 1.02 Gb and 1.25% ( Supplementary Fig. S13 ), respectively, which closely resemble those of P. lobata genome ( Supplementary Fig. S1 ). In addition, the result of BUSCO analysis shows that the completeness and duplication of the reference gene set in the previous P. thomsonii genome were 98.9% and 33.1% ( Supplementary Table S4 ), respectively. These results suggest that the previous P. thomsonii genome likely contains many redundant haploid contigs. This phenomenon could be attributed to the high levels of heterozygosity typical in the genomes of highly heterozygous species, such as lychee, 76 Medicago ruthenica , 77 and Cinnamomum camphora . 78 Therefore, we have excised the redundant regions from the previous P. thomsonii genome, leading to the generation of an updated genome. This improved genome provides a robust foundation for conducting more comprehensive investigations into P. thomsonii .

In the three Pueraria species, they all have a karyotype of 2 n  = 2 x  = 22. We observed significant genome syntenic relationships among P. lobata , P. thomsonii , and P. montana , indicating their high genetic relationship ( Fig. 2d ). This result suggests the possibility of extensive interspecific hybridization among Pueraria species. Enrichment analysis showed that gene families associated with stress responses were expanded in P. lobata and P. thomsonii . In contrast, changes in gene families associated with secondary metabolites such as isoflavonoids may have contributed to their different nutritional values. Numerous reports have been on the WGD events and evolutionary relationships of Fabaceae species; however, few researches have been conducted on Pueraria species. 65 , 79–81 In this study, we inferred that three Pueraria species collectively experienced two WGD events, with the most recent one being shared with Fabaceae plants. 82 Since then, none of these species has undergone separate WGD events. Previous studies have reported a recent WGD event in P. thomsonii (~4.8 Mya). 16 Nevertheless, such a recent WGD event could produce many collinearity blocks of paralogs, as seen in G. max , a closely related species to P. thomsonii . However, this was not observed in P. thomsonii genome ( Supplementary Fig. S14 ). 65 Therefore, it concludes that this event was likely due to segmental duplication rather than an authentic WGD event. A similar phenomenon has been observed in V. radiata . 79 Phylogenetic tree constructed using single-copy genes and Ks analysis based on orthologs also indicated that the three Pueraria species are closely related to G. max and belong to Papilionoideae ( Fig. 2c , Supplementary Fig. S7 ). 80 Among them, P. thomsonii and P. lobata exhibit a closer genetic relationship, consistent with their similar biological morphology and functions. Our results further contribute to the understanding of evolutionary relationships among Fabaceae species.

In addition, we first reported the characterization of genome-wide SNPs from 121 Pueraria accessions, which are mainly distributed in the south of China. Based on these SNPs, three Pueraria species populations were differentiated into three groups: G1, G2, and G3. Among them, G3 could be further divided into two subgroups. The subgroup G3-1 displayed greater population diversity than the subgroup G3-2, rendering it a valuable genetic resource for improving the P. thomsonii germplasm. Additionally, we found that the GMM93 ( P. montana ) accession was classified into subgroup G3-1 and exhibited a distant phylogenetic relationship with other P. montana accessions in both previous and current studies ( Fig. 3b–c ). 4 Given the current limitations of morphological-based identification of Pueraria species, we place greater confidence in the findings derived from genomics and population genetics analyses. Consequently, we infer that GMM93 is more likely to P. lobata rather than P. montana . Previous studies indicated that the three Pueraria species have a large mixed distribution in the southwestern of China, which may be the center of their origin. 83 Notably, most accessions in G1 and G2, located at the root of the phylogenetic tree, were collected from Yunnan and Guangxi provinces ( Supplementary Table S13 ). Thus, we speculate that the three Pueraria species may have originated from Yunnan and Guangxi provinces. The phylogenetic tree conducted with SNPs corroborated the close evolutionary relationship between P. thomsonii and P. lobata . Further combined with the differences in population diversity between subgroup G3-1 and G3-2, we suggest that P. thomsonii was likely domesticated from P. lobata as a subspecies. Even though our whole-genome SNP data could not able to display the whole scheme of P. thomsonii domestication history, it revealed some valuable details about the evolutionary relationship of three Pueraria species. This study offers a valuable genomic resource for the genetic improvement of Pueraria species.

This work was supported by the National Natural Science Foundation of China (32270712, 32100526, 82204563), Young Elite Scientists Sponsorship Program by CAST (2022QNRC001), the State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources (SKLCUSA-a202205), Chief Expert of Tuberous Crops Innovation Team in Guangxi Province (nycytxgxcxtd-2023-11-01), and Guangxi Science and Technology Major Program (GuikeAA23023035-6).

The authors declare no competing interests.

J.S., L.C., and H.Y. conceived and designed the study. X.S., L.X., P.S., W.Z., S.C., and Z.W. contributed to sample preparation. X.H., S.G., M.G., B.Z., and J.S. performed the bioinformatics analyses. X.H., S.G., X.S., J.S., H.Y., and L.C. wrote and revised the manuscript. All authors read and approved the final manuscript.

All data used in this study are publicly available. Whole genome sequencing data have been deposited in BioProject under accession numbers PRJCA016835 at https://ngdc.cncb.ac.cn/gwh .

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Current status of community resources and priorities for weed genomics research

• Jacob Montgomery 1 ,
• Sarah Morran 1 ,
• Dana R. MacGregor   ORCID: orcid.org/0000-0003-0543-0408 2 ,
• J. Scott McElroy   ORCID: orcid.org/0000-0003-0331-3697 3 ,
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• Longjiang Fan   ORCID: orcid.org/0000-0003-4846-0500 8 ,
• Ana L. Caicedo   ORCID: orcid.org/0000-0002-0378-6374 9 ,
• Peter J. Maughan 10 ,
• Bianca Assis Barbosa Martins 11 ,
• Jagoda Mika 11 ,
• Alberto Collavo 11 ,
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• Nithya K. Subramanian   ORCID: orcid.org/0000-0002-1659-7396 13 ,
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• Bikram S. Gill   ORCID: orcid.org/0000-0003-4510-9459 16 ,
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• Roger Gast 17 ,
• Neeta Soni   ORCID: orcid.org/0000-0002-4647-8355 17 ,
• Terry R. Wright   ORCID: orcid.org/0000-0002-3969-2812 18 ,
• Gina Zastrow-Hayes 18 ,
• Gregory May 18 ,
• Jenna M. Malone   ORCID: orcid.org/0000-0002-9637-2073 19 ,
• Deepmala Sehgal   ORCID: orcid.org/0000-0002-4141-1784 20 ,
• Shiv Shankhar Kaundun   ORCID: orcid.org/0000-0002-7249-2046 20 ,
• Richard P. Dale 20 ,
• Barend Juan Vorster   ORCID: orcid.org/0000-0003-3518-3508 21 ,
• Bodo Peters 11 ,
• Jens Lerchl   ORCID: orcid.org/0000-0002-9633-2653 22 ,
• Patrick J. Tranel   ORCID: orcid.org/0000-0003-0666-4564 23 ,
• Roland Beffa   ORCID: orcid.org/0000-0003-3109-388X 24 ,
• Alexandre Fournier-Level   ORCID: orcid.org/0000-0002-6047-7164 25 ,
• Mithila Jugulam   ORCID: orcid.org/0000-0003-2065-9067 15 ,
• Kevin Fengler 18 ,
• Victor Llaca   ORCID: orcid.org/0000-0003-4822-2924 18 ,
• Eric L. Patterson   ORCID: orcid.org/0000-0001-7111-6287 14 &
• Todd A. Gaines   ORCID: orcid.org/0000-0003-1485-7665 1  

Genome Biology volume  25 , Article number:  139 ( 2024 ) Cite this article

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Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

Each year globally, agricultural producers and landscape managers spend billions of US dollars [ 1 , 2 ] and countless hours attempting to control weedy plants and reduce their adverse effects. These management methods range from low-tech (e.g., pulling plants from the soil by hand) to extremely high-tech (e.g., computer vision-controlled spraying of herbicides). Regardless of technology level, effective control methods serve as strong selection pressures on weedy plants and often result in rapid evolution of weed populations resistant to such methods [ 3 , 4 , 5 , 6 , 7 ]. Thus, humans and weeds have been locked in an arms race, where humans develop new or improved control methods and weeds adapt and evolve to circumvent such methods.

Applying genomics to weed science offers a unique opportunity to study rapid adaptation, epigenetic responses, and examples of evolutionary rescue of diverse weedy species in the face of widespread and powerful selective pressures. Furthermore, lessons learned from these studies may also help to develop more sustainable control methods and to improve crop breeding efforts in the face of our ever-changing climate. While other research fields have used genetics and genomics to uncover the basis of many biological traits [ 8 , 9 , 10 , 11 ] and to understand how ecological factors affect evolution [ 12 , 13 ], the field of weed science has lagged behind in the development of genomic tools essential for such studies [ 14 ]. As research in human and crop genetics pushes into the era of pangenomics (i.e., multiple chromosome scale genome assemblies for a single species [ 15 , 16 ]), publicly available genomic information is still lacking or severely limited for the majority of weed species. Recent reviews of current weed genomes identified 26 [ 17 ] and 32 weed species with sequenced genomes [ 18 ]—many assembled to a sub-chromosome level.

Here, we summarize the current state of weed genomics, highlighting cases where genomics approaches have successfully provided insights on topics such as population genetic dynamics, genome evolution, and the genetic basis of herbicide resistance, rapid adaptation, and crop dedomestication. These highlighted investigations all relied upon genomic resources that are relatively rare for weedy species. Throughout, we identify additional resources that would advance the field of weed science and enable further progress in weed genomics. We then introduce the International Weed Genomics Consortium (IWGC), an open collaboration among researchers, and describe current efforts to generate these additional resources.

Evolution of weediness: potential research utilizing weed genomics tools

Weeds can evolve from non-weed progenitors through wild colonization, crop de-domestication, or crop-wild hybridization [ 19 ]. Because the time span in which weeds have evolved is necessarily limited by the origins of agriculture, these non-weed relatives often still exist and can be leveraged through population genomic and comparative genomic approaches to identify the adaptive changes that have driven the evolution of weediness. The ability to rapidly adapt, persist, and spread in agroecosystems are defining features of weedy plants, leading many to advocate agricultural weeds as ideal candidates for studying rapid plant adaptation [ 20 , 21 , 22 , 23 ]. The insights gained from applying plant ecological approaches to the study of rapid weed adaptation will move us towards the ultimate goals of mitigating such adaptation and increasing the efficacy of crop breeding and biotechnology [ 14 ].

Biology and ecological genomics of weeds

The impressive community effort to create and maintain resources for Arabidopsis thaliana ecological genomics provides a motivating example for the emerging study of weed genomics [ 24 , 25 , 26 , 27 ]. Arabidopsis thaliana was the first flowering plant species to have its genome fully sequenced [ 28 ] and rapidly became a model organism for plant molecular biology. As weedy genomes become available, collection, maintenance, and resequencing of globally distributed accessions of these species will help to replicate the success found in ecological studies of A. thaliana [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Evaluation of these accessions for traits of interest to produce large phenomics data sets (as in [ 36 , 37 , 38 , 39 , 40 ]) enables genome-wide association studies and population genomics analyses aimed at dissecting the genetic basis of variation in such traits [ 41 ]. Increasingly, these resources (e.g. the 1001 genomes project [ 29 ]) have enabled A. thaliana to be utilized as a model species to explore the eco-evolutionary basis of plant adaptation in a more realistic ecological context. Weedy species should supplement lessons in eco-evolutionary genomics learned from these experiments in A. thaliana .

Untargeted genomic approaches for understanding the evolutionary trajectories of populations and the genetic basis of traits as described above rely on the collection of genotypic information from across the genome of many individuals. While whole-genome resequencing accomplishes this requirement and requires no custom methodology, this approach provides more information than is necessary and is prohibitively expensive in species with large genomes. Development and optimization of genotype-by-sequencing methods for capturing reduced representations of newly sequence genomes like those described by [ 42 , 43 , 44 ] will reduce the cost and computational requirements of genetic mapping and population genetic experiments. Most major weed species do not currently have protocols for stable transformation, a key development in the popularity of A. thaliana as a model organism and a requirement for many functional genomic approaches. Functional validation of genes/variants believed to be responsible for traits of interest in weeds has thus far relied on transiently manipulating endogenous gene expression [ 45 , 46 ] or ectopic expression of a transgene in a model system [ 47 , 48 , 49 ]. While these methods have been successful, few weed species have well-studied viral vectors to adapt for use in virus induced gene silencing. Spray induced gene silencing is another potential option for functional investigation of candidate genes in weeds, but more research is needed to establish reliable delivery and gene knockdown [ 50 ]. Furthermore, traits with complex genetic architecture divergent between the researched and model species may not be amenable to functional genomic approaches using transgenesis techniques in model systems. Developing protocols for reduced representation sequencing, stable transformation, and gene editing/silencing in weeds will allow for more thorough characterization of candidate genetic variants underlying traits of interest.

Beyond rapid adaptation, some weedy species offer an opportunity to better understand co-evolution, like that between plants and pollinators and how their interaction leads to the spread of weedy alleles (Additional File 1 : Table S1). A suite of plant–insect traits has co-evolved to maximize the attraction of the insect pollinator community and the efficiency of pollen deposition between flowers ensuring fruit and seed production in many weeds [ 51 , 52 ]. Genetic mapping experiments have identified genes and genetic variants responsible for many floral traits affecting pollinator interaction including petal color [ 53 , 54 , 55 , 56 ], flower symmetry and size [ 57 , 58 , 59 ], and production of volatile organic compounds [ 60 , 61 , 62 ] and nectar [ 63 , 64 , 65 ]. While these studies reveal candidate genes for selection under co-evolution, herbicide resistance alleles may also have pleiotropic effects on the ecology of weeds [ 66 ], altering plant-pollinator interactions [ 67 ]. Discovery of genes and genetic variants involved in weed-pollinator interaction and their molecular and environmental control may create opportunities for better management of weeds with insect-mediated pollination. For example, if management can disrupt pollinator attraction/interaction with these weeds, the efficiency of reproduction may be reduced.

A more complete understanding of weed ecological genomics will undoubtedly elucidate many unresolved questions regarding the genetic basis of various aspects of weediness. For instance, when comparing populations of a species from agricultural and non-agricultural environments, is there evidence for contemporary evolution of weedy traits selected by agricultural management or were “natural” populations pre-adapted to agroecosystems? Where there is differentiation between weedy and natural populations, which traits are under selection and what is the genetic basis of variation in those traits? When comparing between weedy populations, is there evidence for parallel versus non-parallel evolution of weediness at the phenotypic and genotypic levels? Such studies may uncover fundamental truths about weediness. For example, is there a common phenotypic and/or genotypic basis for aspects of weediness among diverse weed species? The availability of characterized accessions and reference genomes for species of interest are required for such studies but only a few weedy species have these resources developed.

Population genomics

Weed species are certainly fierce competitors, able to outcompete crops and endemic species in their native environment, but they are also remarkable colonizers of perturbed habitats. Weeds achieve this through high fecundity, often producing tens of thousands of seeds per individual plant [ 68 , 69 , 70 ]. These large numbers in terms of demographic population size often combine with outcrossing reproduction to generate high levels of diversity with local effective population sizes in the hundreds of thousands [ 71 , 72 ]. This has two important consequences: weed populations retain standing genetic variation and generate many new mutations, supporting weed success in the face of harsh control. The generation of genomic tools to monitor weed populations at the molecular level is a game-changer to understanding weed dynamics and precisely testing the effect of artificial selection (i.e., management) and other evolutionary mechanisms on the genetic make-up of populations.

Population genomic data, without any environmental or phenotypic information, can be used to scan the genomes of weed and non-weed relatives to identify selective sweeps, pointing at loci supporting weed adaptation on micro- or macro-evolutionary scales. Two recent within-species examples include weedy rice, where population differentiation between weedy and domesticated populations was used to identify the genetic basis of weedy de-domestication [ 73 ], and common waterhemp, where consistent allelic differences among natural and agricultural collections resolved a complex set of agriculturally adaptive alleles [ 74 , 75 ]. A recent comparative population genomic study of weedy barnyardgrass and crop millet species has demonstrated how inter-specific investigations can resolve the signatures of crop and weed evolution [ 76 ] (also see [ 77 ] for a non-weed climate adaptation example). Multiple sequence alignments across numerous species provide complementary insight into adaptive convergence over deeper timescales, even with just one genomic sample per species (e.g., [ 78 , 79 ]). Thus, newly sequenced weed genomes combined with genomes available for closely related crops (outlined by [ 14 , 80 ]) and an effort to identify other non-weed wild relatives will be invaluable in characterizing the genetic architecture of weed adaptation and evolution across diverse species.

Weeds experience high levels of genetic selection, both artificial in response to agricultural practices and particularly herbicides, and natural in response to the environmental conditions they encounter [ 81 , 82 ]. Using genomic analysis to identify loci that are the targets of selection, whether natural or artificial, would point at vulnerabilities that could be leveraged against weeds to develop new and more sustainable management strategies [ 83 ]. This is a key motivation to develop genotype-by-environment association (GEA) and selective sweep scan approaches, which allow researchers to resolve the molecular basis of multi-dimensional adaptation [ 84 , 85 ]. GEA approaches, in particular, have been widely used on landscape-wide resequencing collections to determine the genetic basis of climate adaptation (e.g., [ 27 , 86 , 87 ]), but have yet to be fully exploited to diagnose the genetic basis of the various aspects of weediness [ 88 ]. Armed with data on environmental dimensions of agricultural settings, such as focal crop, soil quality, herbicide use, and climate, GEA approaches can help disentangle how discrete farming practices have influenced the evolution of weediness and resolve broader patterns of local adaptation across a weed’s range. Although non-weedy relatives are not technically required for GEA analyses, inclusion of environmental and genomic data from weed progenitors can further distinguish genetic variants underpinning weed origins from those involved in local adaptation.

New weeds emerge frequently [ 89 ], either through hybridization between species as documented for sea beet ( Beta vulgaris ssp. maritima) hybridizing with crop beet to produce progeny that are well adapted to agricultural conditions [ 90 , 91 , 92 ], or through the invasion of alien species that find a new range to colonize. Biosecurity measures are often in place to stop the introduction of new weeds; however, the vast scale of global agricultural commodity trade precludes the possibility of total control. Population genomic analysis is now able to measure gene flow between populations [ 74 , 93 , 94 , 95 ] and identify populations of origin for invasive species including weeds [ 96 , 97 , 98 ]. For example, the invasion route of the pest fruitfly Drosophila suzukii from Eastern Asia to North America and Europe through Hawaii was deciphered using Approximate Bayesian Computation on high-throughput sequencing data from a global sample of multiple populations [ 99 ]. Genomics can also be leveraged to predict invasion rather than explain it. The resequencing of a global sample of common ragweed ( Ambrosia artemisiifolia L.) elucidated a complex invasion route whereby Europe was invaded by multiple introductions of American ragweed that hybridized in Europe prior to a subsequent introduction to Australia [ 100 , 101 ]. In this context, the use of genomically informed species distribution models helps assess the risk associated with different source populations, which in the case of common ragweed, suggests that a source population from Florida would allow ragweed to invade most of northern Australia [ 102 ]. Globally coordinated research efforts to understand potential distribution models could support the transformation of biosecurity from perspective analysis towards predictive risk assessment.

Herbicide resistance and weed management

Herbicide resistance is among the numerous weedy traits that can evolve in plant populations exposed to agricultural selection pressures. Over-reliance on herbicides to control weeds, along with low diversity and lack of redundancy in weed management strategies, has resulted in globally widespread herbicide resistance [ 103 ]. To date, 272 herbicide-resistant weed species have been reported worldwide, and at least one resistance case exists for 21 of the 31 existing herbicide sites of action [ 104 ]—significantly limiting chemical weed control options available to agriculturalists. This limitation of control options is exacerbated by the recent lack of discovery of herbicides with new sites of action [ 105 ].

Herbicide resistance may result from several different physiological mechanisms. Such mechanisms have been classified into two main groups, target-site resistance (TSR) [ 4 , 106 ] and non-target-site resistance (NTSR) [ 4 , 107 ]. The first group encompasses changes that reduce binding affinity between a herbicide and its target [ 108 ]. These changes may provide resistance to multiple herbicides that have a common biochemical target [ 109 ] and can be effectively managed through mixture and/or rotation of herbicides targeting different sites of action [ 110 ]. The second group (NTSR), includes alterations in herbicide absorption, translocation, sequestration, and/or metabolism that may lead to unpredictable pleotropic cross-resistance profiles where structurally and functionally diverse herbicides are rendered ineffective by one or more genetic variant(s) [ 47 ]. This mechanism of resistance threatens not only the efficacy of existing herbicidal chemistries, but also ones yet to be discovered. While TSR is well understood because of the ease of identification and molecular characterization of target site variants, NTSR mechanisms are significantly more challenging to research because they are often polygenic, and the resistance causing element(s) are not well understood [ 111 ].

Improving the current understanding of metabolic NTSR mechanisms is not an easy task, since genes of diverse biochemical functions are involved, many of which exist as extensive gene families [ 109 , 112 ]. Expression changes of NTSR genes have been implicated in several resistance cases where the protein products of the genes are functionally equivalent across sensitive and resistant plants, but their relative abundance leads to resistance. Thus, regulatory elements of NTSR genes have been scrutinized to understand their role in NTSR mechanisms [ 113 ]. Similarly, epigenetic modifications have been hypothesized to play a role in NTSR, with much remaining to be explored [ 114 , 115 , 116 ]. Untargeted approaches such as genome-wide association, selective sweep scans, linkage mapping, RNA-sequencing, and metabolomic profiling have proven helpful to complement more specific biochemical- and chemo-characterization studies towards the elucidation of NTSR mechanisms as well as their regulation and evolution [ 47 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 ]. Even in cases where resistance has been attributed to TSR, genetic mapping approaches can detect other NTSR loci contributing to resistance (as shown by [ 123 ]) and provide further evidence for the role of TSR mutations across populations. Knowledge of the genetic basis of NTSR will aid the rational design of herbicides by screening new compounds for interaction with newly discovered NTSR proteins during early research phases and by identifying conserved chemical structures that interact with these proteins that should be avoided in small molecule design.

Genomic resources can also be used to predict the protein structure for novel herbicide target site and metabolism genes. This will allow for prediction of efficacy and selectivity for new candidate herbicides in silico to increase herbicide discovery throughput as well as aid in the design and development of next-generation technologies for sustainable weed management. Proteolysis targeting chimeras (PROTACs) have the potential to bind desired targets with great selectivity and degrade proteins by utilizing natural protein ubiquitination and degradation pathways within plants [ 125 ]. Spray-induced gene silencing in weeds using oligonucleotides has potential as a new, innovative, and sustainable method for weed management, but improved methods for design and delivery of oligonucleotides are needed to make this technique a viable management option [ 50 ]. Additionally, success in the field of pharmaceutical drug discovery in the development of molecules modulating protein–protein interactions offers another potential avenue towards the development of herbicides with novel targets [ 126 , 127 ]. High-quality reference genomes allow for the design of new weed management technologies like the ones listed here that are specific to—and effective across—weed species but have a null effect on non-target organisms.

Comparative genomics and genome biology

The genomes of weed species are as diverse as weed species themselves. Weeds are found across highly diverged plant families and often have no phylogenetically close model or crop species relatives for comparison. On all measurable metrics, weed genomes run the gamut. Some have smaller genomes like Cyperus spp. (~ 0.26 Gb) while others are larger, such as Avena fatua (~ 11.1 Gb) (Table  1 ). Some have high heterozygosity in terms of single-nucleotide polymorphisms, such as the Amaranthus spp., while others are primarily self-pollinated and quite homozygous, such as Poa annua [ 128 , 129 ]. Some are diploid such as Conyza canadensis and Echinochloa haploclada while others are polyploid such as C. sumetrensis , E. crus-galli , and E. colona [ 76 ]. The availability of genomic resources in these diverse, unexplored branches of the tree of life allows us to identify consistencies and anomalies in the field of genome biology.

The weed genomes published so far have focused mainly on weeds of agronomic crops, and studies have revolved around their ability to resist key herbicides. For example, genomic resources were vital in the elucidation of herbicide resistance cases involving target site gene copy number variants (CNVs). Gene CNVs of 5-enolpyruvylshikimate-3-phosphate synthase ( EPSPS ) have been found to confer resistance to the herbicide glyphosate in diverse weed species. To date, nine species have independently evolved EPSPS CNVs, and species achieve increased EPSPS copy number via different mechanisms [ 153 ]. For instance, the EPSPS CNV in Bassia scoparia is caused by tandem duplication, which is accredited to transposable element insertions flanking EPSPS and subsequent unequal crossing over events [ 154 , 155 ]. In Eleusine indica , a EPSPS CNV was caused by translocation of the EPSPS locus into the subtelomere followed by telomeric sequence exchange [ 156 ]. One of the most fascinating genome biology discoveries in weed science has been that of extra-chromosomal circular DNAs (eccDNAs) that harbor the EPSPS gene in the weed species Amaranthus palmeri [ 157 , 158 ]. In this case, the eccDNAs autonomously replicate separately from the nuclear genome and do not reintegrate into chromosomes, which has implications for inheritance, fitness, and genome structure [ 159 ]. These discoveries would not have been possible without reference assemblies of weed genomes, next-generation sequencing, and collaboration with experts in plant genomics and bioinformatics.

Another question that is often explored with weedy genomes is the nature and composition of gene families that are associated with NTSR. Gene families under consideration often include cytochrome P450s (CYPs), glutathione- S -transferases (GSTs), ABC transporters, etc. Some questions commonly considered with new weed genomes include how many genes are in each of these gene families, where are they located, and which weed accessions and species have an over-abundance of them that might explain their ability to evolve resistance so rapidly [ 76 , 146 , 160 , 161 ]? Weed genome resources are necessary to answer questions about gene family expansion or contraction during the evolution of weediness, including the role of polyploidy in NTSR gene family expansion as explored by [ 162 ].

Translational research and communication with weed management stakeholders

Whereas genomics of model plants is typically aimed at addressing fundamental questions in plant biology, and genomics of crop species has the obvious goal of crop improvement, goals of genomics of weedy plants also include the development of more effective and sustainable strategies for their management. Weed genomic resources assist with these objectives by providing novel molecular ecological and evolutionary insights from the context of intensive anthropogenic management (which is lacking in model plants), and offer knowledge and resources for trait discovery for crop improvement, especially given that many wild crop relatives are also important agronomic weeds (e.g., [ 163 ]). For instance, crop-wild relatives are valuable for improving crop breeding for marginal environments [ 164 ]. Thus, weed genomics presents unique opportunities and challenges relative to plant genomics more broadly. It should also be noted that although weed science at its core is an applied discipline, it draws broadly from many scientific disciplines such as, plant physiology, chemistry, ecology, and evolutionary biology, to name a few. The successful integration of weed-management strategies, therefore, requires extensive collaboration among individuals collectively possessing the necessary expertise [ 165 ].

With the growing complexity of herbicide resistance management, practitioners are beginning to recognize the importance of understanding resistance mechanisms to inform appropriate management tactics [ 14 ]. Although weed science practitioners do not need to understand the technical details of weed genomics, their appreciation of the power of weed genomics—together with their unique insights from field observations—will yield novel opportunities for applications of weed genomics to weed management. In particular, combining field management history with information on weed resistance mechanisms is expected to provide novel insights into evolutionary trajectories (e.g. [ 6 , 166 ]), which can be utilized for disrupting evolutionary adaptation. It can be difficult to obtain field history information from practitioners, but developing an understanding among them of the importance of such information can be invaluable.

Development of weed genomics resources by the IWGC

Weed genomics is a fast-growing field of research with many recent breakthroughs and many unexplored areas of study. The International Weed Genomics Consortium (IWGC) started in 2021 to address the roadblocks listed above and to promote the study of weedy plants. The IWGC is an open collaboration among academic, government, and industry researchers focused on producing genomic tools for weedy species from around the world. Through this collaboration, our initial aim is to provide chromosome-level reference genome assemblies for at least 50 important weedy species from across the globe that are chosen based on member input, economic impact, and global prevalence (Fig.  1 ). Each genome will include annotation of gene models and repetitive elements and will be freely available through public databases with no intellectual property restrictions. Additionally, future funding of the IWGC will focus on improving gene annotations and supplementing these reference genomes with tools that increase their utility.

The International Weed Genomics Consortium (IWGC) collected input from the weed genomics community to develop plans for weed genome sequencing, annotation, user-friendly genome analysis tools, and community engagement

Reference genomes and data analysis tools

The first objective of the IWGC is to provide high-quality genomic resources for agriculturally important weeds. The IWGC therefore created two main resources for information about, access to, or analysis of weed genomic data (Fig.  1 ). The IWGC website (available at [ 167 ]) communicates the status and results of genome sequencing projects, information on training and funding opportunities, upcoming events, and news in weed genomics. It also contains details of all sequenced species including genome size, ploidy, chromosome number, herbicide resistance status, and reference genome assembly statistics. The IWGC either compiles existing data on genome size, ploidy, and chromosome number, or obtains the data using flow cytometry and cytogenetics (Fig.  1 ; Additional File 2 : Fig S1-S4). Through this website, users can request an account to access our second main resource, an online genome database called WeedPedia (accessible at [ 168 ]), with an account that is created within 3–5 working days of an account request submission. WeedPedia hosts IWGC-generated and other relevant publicly accessible genomic data as well as a suite of bioinformatic tools. Unlike what is available for other fields, weed science did not have a centralized hub for genomics information, data, and analysis prior to the IWGC. Our intention in creating WeedPedia is to encourage collaboration and equity of access to information across the research community. Importantly, all genome assemblies and annotations from the IWGC (Table  1 ), along with the raw data used to produce them, will be made available through NCBI GenBank. Upon completion of a 1-year sponsoring member data confidentiality period for each species (dates listed in Table  1 ), scientific teams within the IWGC produce the first genome-wide investigation to submit for publication including whole genome level analyses on genes, gene families, and repetitive sequences as well as comparative analysis with other species. Genome assemblies and data will be publicly available through NCBI as part of these initial publications for each species.

WeedPedia is a cloud-based omics database management platform built from the software “CropPedia” and licensed from KeyGene (Wageningen, The Netherlands). The interface allows users to access, visualize, and download genome assemblies along with structural and functional annotation. The platform includes a genome browser, comparative map viewer, pangenome tools, RNA-sequencing data visualization tools, genetic mapping and marker analysis tools, and alignment capabilities that allow searches by keyword or sequence. Additionally, genes encoding known target sites of herbicides have been specially annotated, allowing users to quickly identify and compare these genes of interest. The platform is flexible, making it compatible with future integration of other data types such as epigenetic or proteomic information. As an online platform with a graphical user interface, WeedPedia provides user-friendly, intuitive tools that encourage users to integrate genomics into their research while also allowing more advanced users to download genomic data to be used in custom analysis pipelines. We aspire for WeedPedia to mimic the success of other public genomic databases such as NCBI, CoGe, Phytozome, InsectBase, and Mycocosm to name a few. WeedPedia currently hosts reference genomes for 40 species (some of which are currently in their 1-year confidentiality period) with additional genomes in the pipeline to reach a currently planned total of 55 species (Table  1 ). These genomes include both de novo reference genomes generated or in progress by the IWGC (31 species; Table  1 ), and publicly available genome assemblies of 24 weedy or related species that were generated by independent research groups (Table  2 ). As of May 2024, WeedPedia has over 370 registered users from more than 27 countries spread across 6 continents.

The IWGC reference genomes are generated in partnership with the Corteva Agriscience Genome Center of Excellence (Johnston, Iowa) using a combination of single-molecule long-read sequencing, optical genome maps, and chromosome conformation mapping. This strategy has already yielded highly contiguous, phased, chromosome-level assemblies for 26 weed species, with additional assemblies currently in progress (Table  1 ). The IWGC assemblies have been completed as single or haplotype-resolved double-haplotype pseudomolecules in inbreeding and outbreeding species, respectively, with multiple genomes being near gapless. For example, the de novo assemblies of the allohexaploids Conyza sumatrensis and Chenopodium album have all chromosomes captured in single scaffolds and most chromosomes being gapless from telomere to telomere. Complementary full-length isoform (IsoSeq) sequencing of RNA collected from diverse tissue types and developmental stages assists in the development of gene models during annotation.

As with accessibility of data, a core objective of the IWGC is to facilitate open access to sequenced germplasm when possible for featured species. Historically, the weed science community has rarely shared or adopted standard germplasm (e.g., specific weed accessions). The IWGC has selected a specific accession of each species for reference genome assembly (typically susceptible to herbicides). In collaboration with a parallel effort by the Herbicide Resistant Plants committee of the Weed Science Society of America, seeds of the sequenced weed accessions will be deposited in the United States Department of Agriculture Germplasm Resources Information Network [ 186 ] for broad access by the scientific community and their accession numbers will be listed on the IWGC website. In some cases, it is not possible to generate enough seed to deposit into a public repository (e.g., plants that typically reproduce vegetatively, that are self-incompatible, or that produce very few seeds from a single individual). In these cases, the location of collection for sequenced accessions will at least inform the community where the sequenced individual came from and where they may expect to collect individuals with similar genotypes. The IWGC ensures that sequenced accessions are collected and documented to comply with the Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization under the Convention on Biological Diversity and related Access and Benefit Sharing Legislation [ 187 ]. As additional accessions of weed species are sequenced (e.g., pangenomes are obtained), the IWGC will facilitate germplasm sharing protocols to support collaboration. Further, to simplify the investigation of herbicide resistance, the IWGC will link WeedPedia with the International Herbicide-Resistant Weed Database [ 104 ], an already widely known and utilized database for weed scientists.

Training and collaboration in weed genomics

Beyond producing genomic tools and resources, a priority of the IWGC is to enable the utilization of these resources across a wide range of stakeholders. A holistic approach to training is required for weed science generally [ 188 ], and we would argue even more so for weed genomics. To accomplish our training goals, the IWGC is developing and delivering programs aimed at the full range of IWGC stakeholders and covering a breadth of relevant topics. We have taken care to ensure our approaches are diverse as to provide training to researchers with all levels of existing experience and differing reasons for engaging with these tools. Throughout, the focus is on ensuring that our training and outreach result in impacts that benefit a wide range of stakeholders.

Although recently developed tools are incredibly enabling and have great potential to replace antiquated methodology [ 189 ] and to solve pressing weed science problems [ 14 ], specialized computational skills are required to fully explore and unlock meaning from these highly complex datasets. Collaboration with, or training of, computational biologists equipped with these skills and resources developed by the IWGC will enable weed scientists to expand research programs and better understand the genetic underpinnings of weed evolution and herbicide resistance. To fill existing skill gaps, the IWGC is developing summer bootcamps and online modules directed specifically at weed scientists that will provide training on computational skills (Fig.  1 ). Because successful utilization of the IWGC resources requires more than general computational skills, we have created three targeted workshops that teach practical skills related to genomics databases, molecular biology, and population genomics (available at [ 190 ]). The IWGC has also hosted two official conference meetings, one in September of 2021 and one in January of 2023, with more conferences planned. These conferences have included invited speakers to present successful implementations of weed genomics, educational workshops to build computational skills, and networking opportunities for research to connect and collaborate.

Engagement opportunities during undergraduate degrees have been shown to improve academic outcomes [ 191 , 192 ]. As one activity to help achieve this goal, the IWGC has sponsored opportunities for US undergraduates to undertake a 10-week research experience, which includes an introduction to bioinformatics, a plant genomics research project that results in a presentation, and access to career building opportunities in diverse workplace environments. To increase equitable access to conferences and professional communities, we supported early career researchers to attend the first two IWGC conferences in the USA as well as workshops and bootcamps in Europe, South America, and Australia. These hybrid or in-person travel grants are intentionally designed to remove barriers and increase participation of individuals from backgrounds and experiences currently underrepresented within weed/plant science or genomics [ 193 ]. Recipients of these travel awards gave presentations and gained the measurable benefits that come from either virtual or in-person participation in conferences [ 194 ]. Moving forward, weed scientists must amass skills associated with genomic analyses and collaborate with other area experts to fully leverage resources developed by the IWGC.

The tools generated through the IWGC will enable many new research projects with diverse objectives like those listed above. In summary, contiguous genome assemblies and complete annotation information will allow weed scientists to join plant breeders in the use of genetic mapping for many traits including stress tolerance, plant architecture, and herbicide resistance (especially important for cases of NTSR). These assemblies will also allow for investigations of population structure, gene flow, and responses to evolutionary mechanisms like genetic bottlenecking and artificial selection. Understanding gene sequences across diverse weed species will be vital in modeling new herbicide target site proteins and designing novel effective herbicides with minimal off-target effects. The IWGC website will improve accessibility to weed genomics data by providing a single hub for reference genomes as well as phenotypic and genotypic information for accessions shared with the IWGC. Deposition of sequenced germplasm into public repositories will ensure that researchers are able to access and utilize these accessions in their own research to make the field more standardized and equitable. WeedPedia allows users of all backgrounds to quickly access information of interest such as herbicide target site gene sequence or subcellular localization of protein products for different genes. Users can also utilize server-based tools such as BLAST and genome browsing similar to other public genomic databases. Finally, the IWGC is committed to training and connecting weed genomicists through hosting trainings, workshops, and conferences.

Conclusions

Weeds are unique and fascinating plants, having significant impacts on agriculture and ecosystems; and yet, aspects of their biology, ecology, and genetics remain poorly understood. Weeds represent a unique area within plant biology, given their repeated rapid adaptation to sudden and severe shifts in the selective landscape of anthropogenic management practices. The production of a public genomics database with reference genomes and annotations for over 50 weed species represents a substantial step forward towards research goals that improve our understanding of the biology and evolution of weeds. Future work is needed to improve annotations, particularly for complex gene families involved in herbicide detoxification, structural variants, and mobile genetic elements. As reference genome assemblies become available; standard, affordable methods for gathering genotype information will allow for the identification of genetic variants underlying traits of interest. Further, methods for functional validation and hypothesis testing are needed in weeds to validate the effect of genetic variants detected through such experiments, including systems for transformation, gene editing, and transient gene silencing and expression. Future research should focus on utilizing weed genomes to investigate questions about evolutionary biology, ecology, genetics of weedy traits, and weed population dynamics. The IWGC plans to continue the public–private partnership model to host the WeedPedia database over time, integrate new datasets such as genome resequencing and transcriptomes, conduct trainings, and serve as a research coordination network to ensure that advances in weed science from around the world are shared across the research community (Fig.  1 ). Bridging basic plant genomics with translational applications in weeds is needed to deliver on the potential of weed genomics to improve weed management and crop breeding.

Availability of data and materials

All genome assemblies and related sequencing data produced by the IWGC will be available through NCBI as part of publications reporting the first genome-wide analysis for each species.

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Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

The International Weed Genomics Consortium is supported by BASF SE, Bayer AG, Syngenta Ltd, Corteva Agriscience, CropLife International (Global Herbicide Resistance Action Committee), the Foundation for Food and Agriculture Research (Award DSnew-0000000024), and two conference grants from USDA-NIFA (Award numbers 2021–67013-33570 and 2023-67013-38785).

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Department of Agricultural Biology, Colorado State University, 1177 Campus Delivery, Fort Collins, CO, 80523, USA

Jacob Montgomery, Sarah Morran & Todd A. Gaines

Protecting Crops and the Environment, Rothamsted Research, Harpenden, Hertfordshire, UK

Dana R. MacGregor

Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, USA

J. Scott McElroy

Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark

Paul Neve & Célia Neto

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Institute of Crop Sciences, Zhejiang University, Hangzhou, China

Longjiang Fan

Department of Biology, University of Massachusetts Amherst, Amherst, MA, USA

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Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT, USA

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Bayer AG, Weed Control Research, Frankfurt, Germany

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JMo and TG conceived and outlined the article. TG, DM, EP, RB, JSM, PJT, MJ wrote grants to obtain funding. MMI, BSG, and MJ performed mitotic chromosome visualization. VL performed sequencing. VL and KF assembled the genomes. LC and ELP annotated the genomes. JMo, SM, DRM, JSM, PN, CN, MV, MVS, AIM, JMK, LF, ALC, PJM, BABM, JMi, AC, MVB, LC, AFL, and ELP wrote the first draft of the article. All authors edited the article and improved the final version.

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Correspondence to Todd A. Gaines .

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Supplementary Information

13059_2024_3274_moesm1_esm.docx.

Additional file 1. List of completed and in-progress genome assemblies of weed species pollinated by insects (Table S1).

13059_2024_3274_MOESM2_ESM.docx

Additional file 2. Methods and results for visualizing and counting the metaphase chromosomes of hexaploid Avena fatua (Fig S1); diploid Lolium rigidum  (Fig S2); tetraploid Phalaris minor (Fig S3); and tetraploid Salsola tragus (Fig S4).

Additional file 3. Review history.

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Montgomery, J., Morran, S., MacGregor, D.R. et al. Current status of community resources and priorities for weed genomics research. Genome Biol 25 , 139 (2024). https://doi.org/10.1186/s13059-024-03274-y

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Whole-genome sequencing reveals genomic diversity and selection signatures in Xia’nan cattle

• Xingya Song 1   na1 ,
• Zhi Yao 1   na1 ,
• Zijing Zhang 2   na1 ,
• Shijie Lyu 2 ,
• Ningbo Chen 1 ,
• Xingshan Qi 3 ,
• Xian Liu 4 ,
• Weidong Ma 5 ,
• Wusheng Wang 5 ,
• Chuzhao Lei 1 ,
• Yu Jiang 1 ,
• Eryao Wang 2 &
• Yongzhen Huang 1  

BMC Genomics volume  25 , Article number:  559 ( 2024 ) Cite this article

Metrics details

The crossbreeding of specialized beef cattle breeds with Chinese indigenous cattle is a common method of genetic improvement. Xia’nan cattle, a crossbreed of Charolais and Nanyang cattle, is China’s first specialized beef cattle breed with independent intellectual property rights. After more than two decades of selective breeding, Xia’nan cattle exhibit a robust physique, good environmental adaptability, good tolerance to coarse feed, and high meat production rates. This study analyzed the population genetic structure, genetic diversity, and genomic variations of Xia’nan cattle using whole-genome sequencing data from 30 Xia’nan cattle and 178 published cattle genomic data.

The ancestry estimating composition analysis showed that the ancestry proportions for Xia’nan cattle were mainly Charolais with a small amount of Nanyang cattle. Through the genetic diversity studies (nucleotide diversity and linkage disequilibrium decay), we found that the genomic diversity of Xia’nan cattle is higher than that of specialized beef cattle breeds in Europe but lower than that of Chinese native cattle. Then, we used four methods to detect genome candidate regions influencing the excellent traits of Xia’nan cattle. Among the detected results, 42 genes (θπ and CLR) and 131 genes ( F ST and XP-EHH) were detected by two different detection strategies. In addition, we found a region in BTA8 with strong selection signals. Finally, we conducted functional annotation on the detected genes and found that these genes may influence body development ( NR6A1 ), meat quality traits ( MCCC1 ), growth traits ( WSCD1 , TMEM68 , MFN1 , NCKAP5 ), and immunity ( IL11RA , CNTFR , CCL27 , SLAMF1 , SLAMF7 , NAA35 , and GOLM1 ).

We elucidated the genomic features and population structure of Xia’nan cattle and detected some selection signals in genomic regions potentially associated with crucial economic traits in Xia’nan cattle. This research provided a basis for further breeding improvements in Xia’nan cattle and served as a reference for genetic enhancements in other crossbreed cattle.

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Introduction

Domestic cattle include Bos taurus , Bos indicus , and their crossbreeds [ 1 ]. Previous research has divided domestic cattle around the world into five core populations based on geographic location: European taurine, Eurasian taurine, East Asian taurine, Chinese indicine, and Indian indicine [ 2 , 47 ]. Chinese native cattle, due to the development of agricultural civilization, have historically been bred for plowing, and it resulted in Chinese native cattle exhibiting better environmental adaptability and tolerance to coarse feed compared to specialized beef cattle breeds [ 3 , 4 ]. However, these local breeds notably lag behind specialized beef cattle in terms of meat production performance. In the 1980s, the demand for draft cattle gradually declined with the rapid advancement of agricultural mechanization. Concurrently, the market demand for beef cattle increased fast, necessitating improving both meat quantity and quality. To tap into the production potential of Chinese native cattle, breeders often introduced foreign bloodlines through hybridization to develop new breeds suitable for China. The Charolais, originating from France, are renowned as a large-sized beef cattle breed known for their rapid growth and high meat yield. Nanyang cattle, one of China’s five major native cattle breeds, are mainly distributed in Nanyang City, Henan Province. Previous study has shown that it belonged to the crossbreed of Bos taurus and Bos indicus . It has the advantages of the tall physique, fine meat quality, and more intramuscular fat, but also has the disadvantages of the slow growth rate and low slaughter rate [ 5 , 48 , 49 ]. Then, breeders in Henan province introduced Charolais cattle to crossbreed with the Nanyang cattle. After over two decades of selective breeding, they cultivated China’s first beef cattle breed with independent intellectual property rights. Xia’nan cattle possess several advantages, such as early maturity, rapid growth, good meat quality, and low dystocia rates, enhancing both beef production efficiency and the profitability of cattle farming for farmers.

In order to deeply study the genetic origin of excellent traits during Xia’nan cattle breeding, we sequenced 30 Xia’nan cattle’s genome and detected single nucleotide polymorphisms (SNPs) based on the reference genome of Bos taurus (ARS-UCD1.2). SNPs from Xia’nan cattle were compared with sites from beef cattle and Chinese native cattle previously collected.

Sequencing mapping and SNP detection

This study generated 6,574,344,629 clean reads from 30 Xia’nan cattle whole-genome sequencing data. The reads were mapped to the reference genome (ARS-UCD1.2) and the average coverage of these samples is 11.64×. Then, 20,546,726 biallelic SNPs discovered in these 30 Xia’nan cattle were annotated. Functional annotation of SNP sites showed that the majority of SNPs were located in intergenic regions (58.8%) or intronic regions (37.5%). Exonic SNPs comprised 0.7% of the total SNPs, including 54,180 non-synonymous SNPs and 74,085 synonymous SNPs (Table S3 ). The total number of detected SNPs in the breeds is shown in Table S3 . The count of SNPs in Xia’nan cattle is significantly lower than in Chinese indicine, Indian indicine, and Chinese native cattle (Nanyang cattle, Qinchuan cattle, and Jiaxian Red cattle) yet higher than in European taurine and Eurasian taurine. This distribution pattern of SNPs is similar to Pi’nan cattle, which is also a hybrid breed of Nanyang cattle in a previous study [ 6 ].

Population structure analysis and genetic diversity analysis

In order to delve deeper into the genetic background of Xia’nan cattle, this study conducted ancestry estimating analysis and Principal Component Analysis (PCA) and built the Neighbor-Joining (NJ) tree. The results from NJ tree and PCA exhibit similar patterns, revealing distinct geographical clustering among cattle populations. The first Principal Component (PC) explained 13.13% of the whole genome variation and the second PC explained 4.26%. In the NJ tree, these cattle are clearly divided into different clusters according to five “core” populations [ 2 ]. Xia’nan cattle was positioned between Chinese native cattle and Eurasian taurine, closer to the Eurasian taurine (Fig.  1 B and C). The result without Indian indicine is also shown in this study (Fig. S1 ), and it’s similar to Fig.  1 B. In the ancestry estimating analysis, we showed the cases which the CV error (Table S4 ) is small, and the rest were shown in Fig. S2 . When K = 2, There are clearly two components to the pedigree of these cattle: Bos taurus and Bos indicus . When K = 3, When K = 3, the Xia’nan cattle exhibited a higher resemblance to Charolais than Nanyang cattle, and it has more European taurine ancestry (Fig.  1 D). because these cattle were divided into six reference groups and one target group, we also plotted the case at K = 4–7 (Fig. S1 ).

A Geographical distribution map of cattle breeds used in this study. B Principal component analysis of 17 cattle populations (208 individuals). C Neighbor-joining tree of the relationships in these populations. D Ancestry component analysis of these cattle breeds using ADMIXTURE with K = 2 and K = 3

To assess the genetic diversity of Xia’nan cattle, we calculated nucleotide diversity and linkage disequilibrium (LD) in Xia’nan cattle and other breeds. The results revealed that the highest nucleotide diversity was observed in Chinese indicine. Xia’nan cattle exhibited slightly higher nucleotide diversity compared to its paternal Charolais but lower than its maternal Nanyang cattle (Fig.  2 A). Linkage disequilibrium analysis at the distance of 100 kb indicated that Jiaxian Red cattle and Qinchuan cattle had the lowest LD values, while Tibetan cattle exhibited the highest LD value, followed by Mongolian cattle. Xia’nan cattle showed higher LD levels than Charolais but lower than Nanyang cattle (Fig.  2 B). In addition, we tested runs of homozygous (ROH) in these breeds and counted the distribution of ROHs of different lengths in different populations. We used the average of the number of ROHs in each breed to characterize the distribution pattern (Fig. S3 ). The results showed Chinese native cattle and Chinese indicine had more short ROHs (0.5-1 Mb and 1–2 Mb). Xia’nan cattle exhibited a pattern similar to that of the European taurine (Angus, Hereford and Red Angus).

Genetic diversity analysis of these cattle. A Genome-wide distribution of nucleotide diversity of each breed in 50 kb windows with 50 kb steps. The horizontal line inside the box indicates the median of this distribution; box limits indicate the first and the thirds quartiles, points shows outliers. Data points outside the whiskers can be considered as outliers. B Genome-wide average LD decay estimated from each breed

Genome-wide selective sweep test

We employed nucleotide diversity (θπ) analysis and composite likelihood ratio (CLR) methods to detect and select genomic regions associated with genetic diversity. Regions that showed high signals (top 1%) by both methods were considered candidate selection regions (Fig.  3 A and B). In Xia’nan cattle, a total of 1184 genes (θπ) and 484 genes (CLR) exhibiting selection features were identified, with 40 genes overlapping (Tables S5 and S6 ). Functional enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) was conducted for these overlapping genes. The top term enriched by KEGG is “Cytokine-cytokine receptor interaction” ( P -value = 0.006), Involving 3 genes ( IL11RA , CNTFR , CCL27 ). The GO terms with a significant ( P -value < 0.05) association in these overlapped genes include cytokine binding (GO:0019955), regulation of transcription, DNA-templated (GO:0006355), receptor complex (GO:0043235), and cytokine receptor activity (GO:0004896). Most terms are closely associated with the immune (Table S7 and S8 ).

Manhattan plots of Analysis of the signatures of positive selection in the genome of Xia’nan cattle

Subsequently, we conducted fixation index ( F ST ) and cross-population extended homozygosity (XP-EHH) tests between the Xia’nan cattle and Nanyang cattle, and the top 1% of all sites are positive sites (Fig.  3 C and D). The F ST method detected 1344 genes, while the XP-EHH test identified 1355 genes (Tables S9 and S10 ). Among these, 131 genes were detected by both methods and confirmed as potential candidate genes specific to Xia’nan cattle. The KEGG and GO analysis results of these genes are shown in Table S12 . Additionally, some regions with strong signals of selection contained known genes associated with traits of interest, such as growth ( WSCD1 , TMEM68 , MFN1 , NCKAP5 ), environmental adaptation ( ITPR2 , GBA3 ), fat metabolism ( CRTC1 , HSPA12A ), and meat quality ( MCCC1 ).

In addition, we observed a significant peak on BTA8: 79.1–79.4 Mb, which was found by three methods (CLR, θπ, and F ST ) (Fig.  4 A and B). This region includes two immune-related genes: NAA35 and GOLM1 .

A Tajima’s D and CLR values on BTA8: 79.1–79.4 Mb region. B Venn diagram showing the overlapping gene counts among θπ, CLR, F ST and XP-EHH.

Population genetics and genetic diversity analyses can aid in assessing genetic resources and breed improvement in Xia’nan cattle. The results of ADMIXTURE software analysis show that the ancestry composition of Xia’nan cattle is more similar to that of Charolais cattle, with most European taurine ancestry. This tiny genetic contribution from Nanyang cattle results higher nucleotide diversity of the Xia’nan cattle genome than European taurine’s. However, the genomic diversity of Xia’nan cattle is lower than that of Chinese native cattle. It may be due to the founder effect or the recent intensive artificial selection in this population of Xia’nan cattle. The LD pattern of each variety was basically consistent with the results of nucleotide diversity. The higher LD level in Xia’nan cattle than in Charolais indicated that there may be more breeding in Xia’nan cattle. The differing LD imbalance intensity in Nanyang cattle compared to other Chinese local breeds might stem from a smaller sample size. The collected samples have undergone stronger artificial selection, emphasizing the rich variation sites and genetic resources inherent in Chinese native cattle breeds. The distribution pattern of ROHs can illustrate the breeding history of the cattle. The ROH distribution pattern of Xia’nan cattle is similar to that of European taurine, reflecting the recent intensive artificial selection in this breed. The pattern of Nanyang cattle and other Chinese native cattle breeds showed large differences, probably because the sample count of Nanyang cattle was too less.

Chinese native cattle are often better resistant and more adaptable than foreign beef breeds. In our selective sweep analysis of Xia’nan cattle, we found several genes associated with immune function, including IL11RA , CNTFR , and CCL27 , within the “cytokine-cytokine receptor interaction” pathway. Interleukin-11 receptor alpha (IL11RA) is the receptor of IL-11. In mice, the specific expression of IL-11 transgenes in fibroblasts or the injection of IL-11 leads to heart and kidney fibrosis, ultimately resulting in organ failure. Conversely, the deletion of IL11RA1 offered protection against these diseases [ 7 ]. Recent research has shown that inhibition of CNTFR could diminish the inhibitory effects of CLCF1 on mitochondrial biogenesis and thermogenesis [ 8 ]. The CC chemokine receptor 27 (CCL27), predominantly expressed by skin keratinocytes, plays a crucial role in the establishment of resident lymphocytes and maintaining immune balance in barrier tissues. It’s closely linked to normal skin and hair follicle development [ 9 ].

The candidate genes SLAMF1 and SLAMF7 are also related to immunity in cattle in previous research [ 10 ]. SLAMF1 and SLAMF7 both belong to the signaling lymphocytic activation molecule (SLAM) family, typically regarded as potential targets for inflammation and autoimmune diseases [ 11 , 12 ]. Previous studies have identified the SLAMF1 as a crucial negative regulator in humoral immune responses [ 13 ], initiating signal transduction networks across various immune cells [ 14 ]. Meanwhile, the SLAMF7 plays a significant role in macrophage hyperactivation and may have critical implications in modulating T-cell functionality within the tumor microenvironment [ 15 ]. Additionally, SLAMF7 is involved in regulating B-cell responses and adaptive immunity [ 16 ], potentially modulating susceptibility to autoimmune conditions in the central nervous system [ 17 ]. The strong selection region (BTA8: 79.1–79.4 Mb) that we found by three methods contains two genes: N-Alpha-Acetyltransferase 35 ( NAA35 ) and Golgi membrane protein 1 ( GOLM1 ). A previous study has shown that the cytokine quantitative trait locus (QTL), including the NAA35 - GOLM1 , significantly regulates the production of interleukin-6 in response to various pathogens [ 18 ].

These findings may be linked to the high disease resistance observed in Nanyang cattle and potentially contribute to Xia’nan cattle’s superior disease resistance compared to Charolais cattle [ 19 ]. Additionally, among the positively selected genes, we discovered NR6A1 , a gene associated with mammalian trunk development and vertebral count [ 20 , 21 ]. It may serve as a candidate locus influencing the body size of Xia’nan cattle.

We discovered lots of genes when comparing selection signals between Xia’nan and Charolais cattle. By searching the literature, we found that some of these genes influence important traits, for example, growth traits ( WSCD1 , TEME68 , MFN1 , NCKAP5 ), fat deposition ( CRTC1 , HSPA12A ), environmental adaptation ( ITPR2 , GBA3 ), and meat quality ( MCCC1 ). The WSC Domain Containing 1 ( WSCD1 ) gene encodes a protein exhibiting sulfotransferase activity and playing a role in glucose metabolism. This gene has been identified to be associated with daily feeding time and feeding rate in the White Duroc × Erhualian F population in a GWAS study [ 22 ], and it was linked to three reproductive periods’ body size in Simmental beef cattle in another GWAS study [ 8 ]. Transmembrane protein 68 ( TEME68 ) was found that it may be associated with feed intake and growth phenotypes in cattle [ 23 ]. Mitochondrial fusion protein 1 (MFN1) is a key regulator of mitochondrial fusion in mammalian cells, playing a pivotal role in maintaining the stability of mitochondrial morphology. The copy number of this gene was found to be related to the growth traits of beef cattle [ 24 ]. NCK-associated protein 5 ( NCKAP5 ) was found to be potentially associated with important phenotypes in Limousin cattle [ 25 ]. CREB-regulated transcription coactivator 1 ( CRTC1 ) and Heat shock protein 12 A ( HSPA12A ) can respectively regulate adipocyte differentiation and fat metabolism through the PPARγ pathway [ 26 , 27 ]. Inositol 1,4,5-Trisphosphate receptor type 2 ( ITPR2 ) and Glucosylceramidase Beta 3 ( GBA3 ) were reported as high-altitude adaptation genes [ 28 , 29 , 30 ]. Methylcrotonyl-CoA carboxylase 1 ( MCCC1 ) was identified as a candidate gene for pork meat quality through weighted gene co-expression network analysis [ 31 ].

These genes, which have been found to be related to immunity, fat deposition, meat quality, and adaptability, may be the components of the genetic basis of Nanyang cattle for good adaptability, excellent disease resistance, and tender meat quality. These screened genes may all play important roles in the formation of Xia’nan cattle’s excellent traits and can be used as candidate genes for breeding of beef cattle.

Conclusions

This study offers a thorough insight into genomic variations of Xia’nan cattle through Whole Genome Sequencing (WGS) data analysis. The exploration of population structure and genetic diversity in Xia’nan cattle will provide valuable guidance for developing informed and effective breeding strategies. Furthermore, we identified some candidate genes that may play crucial roles in growth traits, immune responses, and meat quality in beef cattle. Xia’nan cattle stand as a successful example of improvement among Chinese native cattle breeds, and unraveling the genetic factors behind their superior traits is pivotal for further enhancements within this breed and the development of novel breeds.

Sample and sequencing

We collected 30 blood samples of Xia’nan cattle from the Xia’nan breeding farm in Henan province (Table S1 ). The cattle were not treated harshly during sampling and were released after sampling. We employed the standard phenol-chloroform method for blood DNA extraction [ 32 ].

After assessing the DNA quality, gene libraries were assembled, each with an average fragment size of 300 bp per sample. The sequencing was carried out by BGI using the DNBSEQ-T7 sequencer.

To better elucidate the genetic variations in Xia’nan cattle, we expanded our sample collection by acquiring an additional 178 samples from five “core” cattle populations [ 2 ]. These samples encompass European taurine (Angus ( n  = 11), Hereford ( n  = 10), Red Angus ( n  = 9)), Eurasian taurine (Chariolais ( n  = 10), Gelbvieh ( n  = 6), Jersey ( n  = 10), Piedmontese ( n  = 5), and Simmental ( n  = 8)), East Asian taurine (Hanwoo ( n  = 7), Mongolia ( n  = 5), Tibetan ( n  = 5)), Chinese indicine (Guangfeng cattle ( n  = 4), Leiqiong cattle ( n  = 3), Ji’an cattle ( n  = 4), Jinjiang cattle ( n  = 4), Wannan cattle ( n  = 5)), Indian indicine (Gir ( n  = 2), Haryana ( n  = 1), Nelore ( n  = 1), Sahiwal ( n  = 1), Tharparkar ( n  = 1), unidentified breed ( n  = 1)) and Chinese native cattle ((Nanyang ( n  = 5), Jiaxian ( n  = 30), Qinchuan ( n  = 30)) (Table S2 ).

Reads mapping, SNP calling, and annotation

The Trimmomatic software (version 0.38) [ 33 ]was utilized for trimming sequence reads, employing the following parameters: “LEADING:20, TRAILING:20, SLIDINGWINDWOE: 3:15, AVGQUAL:20, MINLEN:35, PHRED33”. The clean reads were aligned to the reference genome ARS-UCD1.2 by “bwa mem” software (version 0.7.13-r1126) with default parameters. Picard software ( http://broadinstitute.github.io/picard ) was used to remove PCR duplicates. After these, we used the “HaplotypeCaller”, “GenotypeGVCFs”, and “SelectVariants” modules in the Genome Analysis Toolkit (GATK, version 4.1.8.1) to detect SNPs and used the “VariantFiltration” module to filter SNPs with the parameters (QD < 2.0, QUAL < 30.0, SOR > 3.0, FS > 60.0, MQ < 40.0, MQRankSum < -12.5, and ReadPosRankSum < -8.0) [ 34 ]. Finally, BCFtools [ 35 ] was used to retain SNP sites with a genotype missing rate less than 0.1 and a minimum minor allele count greater than 2 (F_MISSING < 0.1 & MAC > 2). We employed ANNOVAR [ 30 ] software to annotate the functions of each SNP based on the Bos taurus reference genome annotation file ( https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/002/263/795/GCF_002263795.1_ARS-UCD1.2/GCF_002263795.1_ARS-UCD1.2_genomic.gff ).

Population genetic analysis

We used PLINK (version 1.90) [ 36 ] to remove the SNPs with high LD. The parameter is “--indep-pair-wise 50 5 0.2”. PopLDdecay [ 37 ] software was used to calculate and visualize Linkage disequilibrium (LD) decay with physical distance between SNPs. PCA was conducted using the “SmartPCA” module within Eigensoft (v6.1.4) [ 38 ]. For ancestry estimation analysis, ADMIXTURE (version 1.3) [ 39 ] was employed with the kinship (K-value) ranging from 2 to 7, and TBtools [ 40 ] was used for visualization. We utilized the matrix of pairwise genetic distances supplied by PLINK was used to construct an unrooted evolutionary tree. The visualization of the evolutionary tree was carried out using MEGA11 [ 41 ] and FigTree v1.4.4 ( http://tree.bio.ed.ac.uk/software/figtree/ ). We detected ROHs by PLINK with the following parameters: (1) the size of the window of 50 SNPs (--homozyg-window-snp 50); (2) required minimum density (--homozyg-density 50); (3) number of heterozygotes allowed in a window (--homozyg-window-het 3); (4) the number of missing calls allowed in window (--homozyg-window-missing 5). ROHs were divided into four categories based on length: 0.5-1 Mb, 1–2 Mb, 2–4 Mb, > 4 Mb.

Selective sweep identification

In order to reveal the signatures of selection influenced by artificial selection and genetic adaptation to the local environment, we employed various strategies to detect the genome selection signals of Xia’nan cattle. Within the Xia’nan cattle population, the composite likelihood ratio (CLR) method [ 42 ] and the nucleotide diversity (θπ) were employed. The value for nucleotide diversity in the plot is -log 10 (θπ). We used VCFtools [ 43 ] software to estimate the whole-genome nucleotide diversity using the sliding window approach utilizing window sizes of 50 kb and steps of 20 kb. SweepFinder2 [ 44 ] was employed to calculate the CLR for every 50 kb window. Empirical P -values of these windows were calculated. The regions with the empirical P -value in the top 1% are positive regions, and the genes detected by both methods are regarded as candidate genes.

Furthermore, fixation index ( F ST ) and cross-population extended haplotype homozygosity (XP-EHH) analyses were conducted to compare Xia’nan cattle with Nanyang cattle. F ST analysis was carried out by VCFtools with the same window size as CLR analysis. In the XP-EHH selection scans, the statistics were derived from the average of standardized XP-EHH scores calculated for every 50 kb region by SELSCAN V1.1 [ 45 ] software. The directional XP-EHH score indicated selection: a positive score suggested potential selection in Xia’nan cattle, while a negative score implied potential selection in the reference population. A significance threshold of P -value < 0.01 was applied to identify noteworthy genomic regions. Genes identified through both methods were considered as candidates for positive selection.

To further understand the functions and signaling pathways associated with these candidate genes, we used KOBAS (version 3.0) [ 46 ] to get enrichment pathways by GO and KEGG.

Data availability

Sequences are available from the Sequence Read Archive (SRA) database. Bioproject accession number is PRJNA1058368.

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Acknowledgements

We would like to thank Yu Jiang for his technical support.

This research has been supported by Technological Innovation 2030- Major Projects (2023ZD040480204); National Key R&D Plan (2022YFD1602310); 2022 Henan Province Central Leading Local Science and Technology Development Fund Project (Z20221343039); Major Science and Technology Projects in Henan Province (221100110200); China Agriculture Research System of MOF and MARA (CARS-37).

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First Author: Xingya Song, Zhi Yao, Zijing Zhang and Shijie Lyu.

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College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling Shaanxi, 712100, Shaanxi, People’s Republic of China

Xingya Song, Zhi Yao, Ningbo Chen, Chuzhao Lei, Yu Jiang & Yongzhen Huang

Institute of Animal Husbandry, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, Henan, People’s Republic of China

Zijing Zhang, Shijie Lyu & Eryao Wang

Biyang County Xiananniu Technology Development Co., Ltd, Zhumadian, 463700, People’s Republic of China

Xingshan Qi

Henan Provincial Livestock Technology Promotion Station, Zhengzhou, 450008, Henan, People’s Republic of China

Shaanxi Agricultural and Animal Husbandry Seed Farm, Shaanxi Fufeng, 722203, People’s Republic of China

Weidong Ma & Wusheng Wang

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YH conceived and designed the experiments. XS and ZY performed the statistical analysis and data upload. ZZ and SL performed the sample DNA extraction. EW provided suggestions for the revision of the manuscript. YJ, CL , and NC provided technical assistance. QX, XL, MW, WW, and EW contributed to the sample collections. YJ provides a data analysis platform. YH provided the laboratories for DNA extraction and statistical analysis. XS drafted the manuscript. All authors read and approved the final manuscript.

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Correspondence to Eryao Wang or Yongzhen Huang .

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Song, X., Yao, Z., Zhang, Z. et al. Whole-genome sequencing reveals genomic diversity and selection signatures in Xia’nan cattle. BMC Genomics 25 , 559 (2024). https://doi.org/10.1186/s12864-024-10463-3

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Received : 30 December 2023

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• Published: 30 January 2018

Cloud computing for genomic data analysis and collaboration

• Ben Langmead 1 &
• Abhinav Nellore 2  

Nature Reviews Genetics volume  19 ,  pages 208–219 ( 2018 ) Cite this article

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A Corrigendum to this article was published on 12 February 2018

This article has been updated

Cloud computing is a paradigm whereby computational resources such as computers, storage and bandwidth can be rented on a pay-for-what-you-use basis.

The cloud's chief advantages are elasticity and convenience. Elasticity refers to the ability to rent and pay for the exact resources needed, and convenience refers to the fact that the user need not deal with the disadvantages of owning or maintaining the resources.

Archives of sequencing data are vast and rapidly growing. Cloud computing is an important enabler for recent efforts to reanalyse large cross-sections of archived sequencing data.

The cloud is becoming a popular venue for hosting large international collaborations, which benefit from the ability to hold data securely in a single location and proximate to the computational infrastructure that will be used to analyse it.

Funders of genomics research are increasingly aware of the cloud and its advantages and are beginning to allocate funds and create cloud-based resources accordingly.

Cloud clusters can be configured with security measures needed to adhere to privacy standards, such as those from the Database of Genotypes and Phenotypes (dbGaP).

Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

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Acknowledgements

The authors thank J. Taylor, E. Afgan, M. Schatz, J. Goecks and A. Margolin for reading through a draft of this work and providing helpful comments. B.L. was supported by the US National Institutes of Health/National Institute of General Medical Sciences grant 1R01GM118568.

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Langmead, B., Nellore, A. Cloud computing for genomic data analysis and collaboration. Nat Rev Genet 19 , 208–219 (2018). https://doi.org/10.1038/nrg.2017.113

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Vaccine-related myocarditis associated with the BNT162b2 vaccine is a rare complication, with a higher risk observed in male adolescents. However, the contribution of genetic factors to this condition remains uncertain. In this study, we conducted a comprehensive genetic association analysis in a cohort of 43 Hong Kong Chinese adolescents who were diagnosed with myocarditis shortly after receiving the BNT162b2 mRNA COVID-19 vaccine. A comparison of whole-genome sequencing data was performed between the confirmed myocarditis cases and a control group of 481 healthy individuals. To narrow down potential genomic regions of interest, we employed a novel clustering approach called ClusterAnalyzer, which prioritised 2,182 genomic regions overlapping with 1,499 genes for further investigation. Our pathway analysis revealed significant enrichment of these genes in functions related to cardiac conduction, ion channel activity, plasma membrane adhesion, and axonogenesis. These findings suggest a potential genetic predisposition in these specific functional areas that may contribute to the observed side effect of the vaccine. Nevertheless, further validation through larger-scale studies is imperative to confirm these findings. Given the increasing prominence of mRNA vaccines as a promising strategy for disease prevention and treatment, understanding the genetic factors associated with vaccine-related myocarditis assumes paramount importance. Our study provides valuable insights that significantly advance our understanding in this regard and serve as a valuable foundation for future research endeavours in this field.

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Introduction

Since the COVID-19 outbreak, different types of vaccines have been developed and swiftly approved for emergency use worldwide by the US Food and Drug Administration (FDA) [ 1 , 2 , 3 ]. As early as April 2021, various platform-specific adverse effects have been reported following the administration of mRNA vaccines (Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273) vaccines) through surveillance programs globally. These adverse effects include clotting disorders, Guillian-Barre syndrome, leukocytoclastic vasculitis [ 4 , 5 , 6 ], as well as acute myocarditis and pericarditis [ 7 , 8 ]. Comparing the incidence rates of vaccine-induced myocarditis in Hong Kong and the US, the incidence rate in Hong Kong was at 18.52 per 100,000 adolescents (age 12–17) [ 9 ] which is much higher than that of 6.28 per 100,000 in the US [ 10 ]. Furthermore, the incidence appeared to be higher in males than females, in adolescents (aged 12–29) compared to adults, and in individuals receiving the second dose compared to the first dose [ 9 , 11 , 12 , 13 , 14 , 15 , 16 ]. Most cases of BNT162b2-associated myocarditis presented with mild symptoms such as chest pain, which typically required either no treatment or the use of nonsteroidal anti-inflammatory drugs (NSAIDs) [ 9 ].

Messenger RNA (mRNA) vaccines introduce a small piece of genetic material called mRNA into host cells. In the case of BNT162b2 vaccines, the mRNA encoding the spike protein (S protein) of the virus is administered intramuscularly [ 17 ]. The mRNA delivered via lipid nanoparticles is taken up by host cells; it then escapes into host cell cytoplasm and instructs the production of the S protein [ 17 ]. S proteins produced by the host are expressed on the surface of host cells, and subsequently recognized, processed and presented by antigen-presenting cells to induce adaptive immune response via expansion of S protein-specific T cells (CD4 + and CD8 + ) [ 17 ]. Consequently, the generation of memory B cells and T cells enables a faster and stronger immune response on future insults.

While the etiology of vaccine-induced myocarditis remains unclear, molecular mimicry against S protein, where antibodies against SARS-CoV-2 cross-react with host tissue proteins, was believed to trigger the autoimmune response of vaccine-induced myocarditis [ 18 ]. The fact that subjects are more likely to develop myocarditis after second dose might be due to the fact that the first dose sensitized the immune system, and the second dose activates it [ 18 ]. High levels of testosterone were suspected to cause the higher incidence rate in males, possibly by promoting aggressive helper T cell response and downregulating anti-inflammatory immune cells [ 18 ]. The involvement of autoimmunity in mediating this complication motivated the hypothesis that genetic variations regulating the activities of autoimmune cells modulates disease susceptibility of vaccine-induced myocarditis. Recent identification of disease susceptibility loci in HLA (DRB1*14:01, DRB1*15:03, and motifs in HLA-A (Leu62 and Gln63)) and KIR ( KIR2DL5B(−)/KIR2DS3(+)/KIR2DS5(−)/KIR2DS4del(+) ) [ 19 , 20 ] suggested the involvement of T cells and NK cells in inducing myocarditis post vaccination, demonstrating the role of genetic predisposition in mediating susceptibility of vaccine-induced myocarditis. To our knowledge, most genetic studies in this field have taken a similar candidate gene approach, this however is biased towards immune-related genes. An unbiased genome-wide approach with the potential of uncovering disease susceptibility loci beyond immune-related genes could greatly impact personalized care in vaccine administration. This allows healthcare professionals to develop targeted screening strategies, prioritize monitoring for high-risk individuals, and implement early interventions. This personalized approach would enhance patient safety, improve outcomes, and contribute to the ongoing development of safer vaccines.

The important role of genetics can also be seen from a case of vaccine-induced myocarditis in 13-year-old monozygotic twins, which indicated a high degree of phenotypic similarity [ 21 ]. Studying rare variant associations would require a large cohort, necessitating ongoing recruitment and international collaboration. Common variants on the other hand, can be studied as clusters by linkage to enhance power in smaller cohorts. As a local effort to investigate the genetic role in vaccine-induced myocarditis, the current study aims to systematically analyze common variant associations of BNT162b2 vaccine-related myocarditis in a cohort of Hong Kong adolescent patients, leading to a hypothesis that disease susceptibility loci related to vaccine-induced myocarditis can be uncovered by using an unbiased genome-wide approach.

Methods and materials

Subject recruitment.

This study was conducted on a cohort of confirmed cases reported earlier by our team [ 9 , 22 ]. Individuals who received the mRNA COVID-19 vaccine consented to link their electronic health records from the Hospital Authority (HA) to their vaccination records through the COVID-19 vaccines Adverse events Response and Evaluation (CARE) programme. Participants aged 12–17 years with suspected post-vaccine acute myocarditis, having received the 1st, 2nd, or 3rd dose of the mRNA COVID-19 vaccine within 14 days before admission to a Hong Kong Hospital Authority (HA) hospital, were included in this study. A series of cardiological investigations were monitored for the admitted patients, including measuring serum cardiac troponin T, electrocardiograms (ECGs) and echocardiograms for the admitted patients. These cases were confirmed according to the criteria suggested by the Brighton Collaboration Case Definition of Myocarditis and Pericarditis and managed by the study team following the Hong Kong Paediatrics Investigation Protocol for Comirnaty-related Myocarditis/Pericarditis [ 23 ]. The exclusion criteria followed the epidemiological study published earlier by our group [ 9 ].

WGS data of 375 Hong Kong Chinese from a study of Hirschsprung’s disease conducted at The University of Hong Kong [ 24 ] and 106 healthy Hong Kong Chinese parents from another study on biliary atresia (unpublished) were used as controls for the association analysis. Allosomal data were not available for the dataset of 375 subjects. Importantly, since all control data were collected before the COVID-19 pandemic, none of the controls had a known diagnosis of vaccine-related myocarditis. To date, there have been no reported mechanistic or pathogenic crosstalk between the phenotypes of the controls (Hirschsprung’s disease and biliary atresia) and vaccine-related myocarditis, thus the sequencing data could be adopted as control dataset in conducting the association analysis in this study.

Whole genome sequencing, variant calling, and association analysis

Whole genome sequencing was performed on genomic DNA extracted from whole blood samples of 43 patients with vaccine-induced myocarditis. The sequencing was conducted using the DNBseq platform with paired-end reads (2 × 150 bp). However, 8 of the 43 samples had inconsistent FASTQ file formats and were excluded from downstream analysis. The raw reads from the remaining 35 samples were first trimmed using trimgalore v0.6.6 [ 25 ] to remove adapter sequences and low-quality bases. The trimmed reads were then aligned to the UCSC human reference genome build hg19 using bwa v0.7.17 [ 26 ]. The average depth of coverage was estimated to be 30X. Variant calling was performed using DeepVariant v1.4.0, and joint genotyping was conducted using GLnexus v1.4.1 [ 27 ] to generate the Variant Call Format (VCF) file for the cases. Variants from low coverage regions (DP < 10) were excluded to minimize variant calling errors.

The control dataset was obtained from other sources as previously described, and only genotype data in VCF format were available. The average depths of coverage were estimated to be approximately 30X for the control subjects from both the biliary atresia study and the Hirschsprung’s disease study. Three control subjects were found being related and therefore excluded from the analysis.

Quality control procedures were implemented using PLINK v1.90b5.3 [ 28 ] and an overview of these procedures was illustrated in Figure S1 . Non-autosomal SNPs and SNPs with genotype missingness greater than 10% or sample missingness greater than 10% were excluded. Additionally, based on the control VCF, SNPs with a minor allele frequency less than 1% or deviating from Hardy-Weinberg equilibrium ( p  < 0.0001) were excluded. The association analysis was performed on the remaining 6,454,355 SNPs using an additive model to test their association with vaccine-induced myocarditis. Population stratification was adjusted using principal component analysis (PCA), and the first six principal components, along with sex, were included as covariates in the association analysis (Figure S2 and Figure S3 ).

ClusterAnalyzer to identify regions with consistent association P values among SNPs in intermediate to high LD

Based on the summary statistics of SNP associations for vaccine-related myocarditis, the ClusterAnalyzer was designed to prioritize genomic regions. (Fig.  1 ) Genomic regions are defined as non-overlapping sets of SNPs with a p  < 0.05 (significant SNPs) located within 5 kb of one another. To ensure computational efficiency, only regions with a minimum of 10 significant SNPs were considered.

Schematics of ClusterAnalyzer. Based on summary statistics of SNPs tested for BNT162b2 vaccine-related myocarditis association, (i) SNPs with p  < 0.05 were selected and (ii) clustered by genomic distance. P-value consistencies were evaluated for each signal within each cluster and quantified as Mean Absolute Error (MAEs). Clusters containing at least one signal with an MAE < 3.0 were prioritized

For each genomic region, SNPs in linkage equilibrium (LD; R 2  > 0.4) were categorized based on the direction of the effect (OR > 1 and OR < 1), enabling the separate evaluation of p-value consistencies for independent signals. Within each group, p-value consistency was determined by the deviation from the line shown in (A) and quantified as Mean Absolute Errors (MAEs, averaged residuals of K SNPs in the group) in (B). Here, s represents -log(P) of each SNPs, s max represents -log(P) of the top SNP, and r 2 represents LD(R 2 ) between the top SNP and other SNPs in the group. Larger MAE indicated poorer p-value consistencies and are thus more likely to be false signals.

Utilizing a user-defined threshold for MAE, a summary of prioritized genomic regions is presented in TSV format. Additionally, GWAS summary statistics of variants within the chosen regions are reported in PLINK format.

Gene set enrichment analysis

The prioritized genomic regions were extended by 5 kb on each end, and the genes within these regions were tabulated using hg19 gene regions obtained from the UCSC database ( http://api.genome.ucsc.edu/getData/track? genome=hg19;track=refGene ) and processed with pybedtools v0.9.1 [ 29 ]. The list of annotated genes in these regions was submitted to the g: Profiler Gene Ontology Statistics (g:GOSt) web server ( https://biit.cs.ut.ee/gprofiler/gost ) [ 30 ] for functional annotation. Gene set enrichment analysis was conducted based on Gene Ontology molecular function (GO:MF) and Gene Ontology biological process (GO:BP) manual annotations. The reference gene sets were derived from “gprofiler_full_hsapiens.name.gmt” and “gprofiler_full_hsapiens.ENSG.gmt.” Default statistical thresholds were used, except for “term size,” which refers to the number of genes enriched in a particular pathway. “Term size” was set within a range of 5-350 to exclude large pathways with limited interpretive value and small pathways with reduced statistical power [ 31 ]. Identified biological processes and molecular functions were clustered using the EnrichmentMap and AutoAnnotate applications (MCL Cluster) in Cytoscape v3.10.1 [ 32 ].

Statistical analysis and data visualization

Quality control procedures and LD computation were executed using PLINK v1.90b5.3 [ 28 ]. Manhattan plots and QQ-plots were generated with the Python package qmplot v0.3.2 ( https://github.com/ShujiaHuang/qmplot ), seaborn v0.12.1, and matplotlib v3.6.2. Regional association plots of selected regions were produced using LocusZoom v0.14.0 ( http://locuszoom.sph.umich.edu/ ). ClusterAnalyzer and other statistical analyses were implemented in Python.

Forty-three Hong Kong Chinese adolescents, aged between 12 and 17 years, with good past health were diagnosed with vaccine-related myocarditis between July 2021 to June 2022 after a median of 3 days after receiving the BNT162b2 vaccine (Comirnaty). The patient cohorts composed of 38 male patients (88.4%) and 5 female patients (12.6%). Majority (81.4%) presented with myocarditis after receiving the 2nd dose of the vaccine. Table  1 provides a summary of the demographic information of the recruited subjects. Detailed clinical background of the confirmed cases was shown in our earlier published studies [ 9 , 22 ]. The confirmed cases presented common cardiac symptoms including chest pain and palpitations. Abnormal ECGs and echocardiogram were observed with an elevated serum cTnT concentration in patients. Overall, these findings suggested that the confirmed cases exhibited an over-reacted host response to the BNT162b2 vaccine and experiencing the adverse cardiac events.

The effective 35 BNT162b2 vaccine-related myocarditis patients and 478 Hong Kong Chinese controls sequencing data were analyzed. Using an additive model, we tested the association of 6,454,355 SNPs related to vaccine-related myocarditis. Multiple SNPs reached genome-wide significance ( p  < 5 × 10 − 8 ; Fig.  2 A), but only a small portion of SNPs formed stacks across the genome. In light of this, we designed a clustering approach to partially remove likely false positive signals due to the genotyping errors.

Manhattan and quantile-quantile (QQ) plots. Manhattan plot of (A) initial SNP associations and (B) prioritized SNP associations using ClusterAnalyzer. The horizontal axis in each plot represents the SNP location from chromosome 1 to chromosome 22, while the vertical axis represents significance level in negative logarithm. The red line corresponds to p  = 1.0 × 10 − 5 . Green line corresponds to p  = 5.0 × 10 − 8 . QQ plots are also provided for (C) initial SNP associations and (D) prioritized SNP associations

By setting a mean absolute error (MAE) threshold of 3.0 for the ClusterAnalyzer tool, we narrowed down the list of SNPs to 2,182 genomic regions, which contained 162,578 SNPs (Fig.  1 ). The average coverage of SNPs in these prioritized genomic regions was estimated at 27X, comparable to the genome average, indicating minimal bias towards sequence coverage. After applying the ClusterAnalyzer, the genomic SNPs appeared more organized as stacks (Fig.  2 A and B), and the genome inflation factor has increased significantly from 0.864 to 6.371 (Fig.  2 C and D). The tool effectively narrowed down the list of SNPs to a smaller subset, namely 2.5% of all SNPs. Two of the prioritized regions most enriched in significant SNPs were chr4:81,892,037–81,965,272 and chr11:40,473,809 − 49,585,162 (hg19; Fig.  3 ). Linkage-association plots showed linked SNPs have comparable log association values (within 3 logs units and adhered to the fitted line; Fig.  3 ), which is evidence of likely true signal. These regions localized to Bone Morphogenetic Protein 3 ( BMP3 ; Fig.  3 B) and Leucine-Rich Repeat Containing 4 C ( LRRC4C ; Fig.  3 D), suggesting a potential connection between these genomic regions and vaccine-related myocarditis.

Linkage-association plots and regional association plots generated by LocusZoom for chr4:81,892,037–81,965,272 ( A , B ) and chr11:40,473,809 − 40,585,162 ( C , D ). SNPs with different direction of Odds Ratio (OR) compared to the top SNP are not shown. Dotted lines represent predicted log association P values based on Linkage Disequilibrium (LD, R 2 )

To functionally characterize the prioritized regions, a comprehensive analysis was conducted on 1,499 coding genes within those regions, including an additional 5 kb upstream and downstream for thorough examination. Gene set enrichment analysis was performed without automated electronic annotations to reveal biological insights. Among the 14 significantly enriched GO annotations with an FDR < 5% (Supplementary Table S2 ), “Axon Guidance” (GO:0007411) and “Ligand-gated channel activity” (GO:0022834) emerged as the most prominently enriched biological process and molecular function respectively. Clustering of the GO annotations revealed four distinct clusters related to ion channel activity, plasma membrane adhesion, cardiac conduction, and axonogenesis (Fig.  4 ). Taken together, these findings suggest a potential genetic predisposition in these specific functional areas that may contribute to BNT162b2 vaccine-induced myocarditis.

Visualization of gene set enrichment analysis results generated by Cytoscape, using g:Profiler for prioritized genes. Shades of red indicate false discovery rates, calculated based on g:SCS (Set Counts and Sizes, PMID: 17478515). Pathway clustering was carried out with AutoAnnotate, an app in Cytoscape, using the default settings

To the best of our knowledge, this is the first genomic study examining the genetic factors associated with the BNT162b2 vaccinated myocarditis in the largest patient cohort reported. Several epidemiological observations were widely accepted as risk factors associated with the vaccine-related myocarditis. Notable differences were observed in the incidence rate of vaccine-related myocarditis across various age, gender and ethnic group, the underlying reasons were also not fully understood [ 9 ]. In addition, long term complications of vaccine-related myocarditis are still not clearly defined. However, impairment of left ventricular and right ventricular myocardial deformation and persistence of late gadolinium enhancement was observed in certain subset of patients up to 1 year after diagnosed with BNT162b2 vaccine-related myocarditis, suggesting the potential long-term effects on the cardiac functions to the affected individuals [ 33 ]. By studying the largest representative Chinese vaccine-related myocarditis cohort, further analysis on the genetic background of the susceptible individuals may offer valuable information to identify vulnerable groups within the community and supplementing the explanations for current observed risk factors in the highest risk groups.

The present study offers a preliminary overview of the genetic factors contributing to BNT162b2 vaccine-related myocarditis in susceptible individuals. Specifically, the 2 prioritized clusters overlapped with BMP3 and LRRC4C which was found to be genes involved in inflammation and immunity. BMP3 codes for an inflammatory protein related to the regulation of the Smad signaling pathway and the release of pro-inflammatory cytokines including IL-6, IL-1β and IL-17A [ 34 ]. In the same functional study, BMP3 inhibition was associated with elevated levels of pro-inflammatory cytokines, suggesting that BMP3 expression may be involved in inflammation process [ 34 ]. Another region (chr11:40,473,809 − 40,585,162) was in the intronic region of Leucine-rich Repeat Containing 4C ( LRRC4C ) gene. LRRC4C functions are not directly involved in inflammation, but primarily in immune-related activity such as migration of monocytes, mast cells and M2 macrophages [ 35 ]. Moreover, the gene set enrichment analysis in this study provided a broader perspective on the genetic involvement in vaccine-related myocarditis. The identified gene sets in cardiac conduction and ion channel activity suggesting the pre-disposed cardiac functions were also a risk factor in developing the vaccine side effect. A dysfunction of cardiac ion channel and elevated high-sensitivity C-reactive proteins (CRP) was observed in a Brugada syndrome patient that presented with ventricular arrhythmias after receiving BNT162b2 mRNA vaccine, supporting the cardiac ion channel activity may be a potential target in developing the vaccine complications in the susceptible individuals [ 36 ]. Apart from that, gene sets related to plasma membrane adhesion revealed an aspect of molecular events that is rarely reported in BNT162b2 vaccine-related myocarditis. The RBD motif in the SARS-CoV-2 spike protein was found eliciting vascular inflammation by binding to the α5β1 integrin in the vascular endothelial cells, in which further demonstrated with systematic production of chemokines and cytokines including TNF-α, IL-1β and IL-6 in the mice model [ 37 ]. The molecular mimicry triggered by the spike protein encoded by the vaccine mRNA could elicit similar events but postulated with a possible stronger response to vascular endothelial cells in susceptible individuals. Further investigations are needed in elucidating the possible involvements of the identified genomic clusters and gene sets in BNT162b2 vaccine-related myocarditis. In addition, axonogensis, a biological process extends beyond the current knowledge of vaccine-related myocarditis, showed the most statistical significance in the enriched pathway analysis. Although the crosstalk between axonogensis and BNT162b2 vaccine-related myocarditis has not yet established, this finding may offer a new approach in investigating the vaccine effects and its associated complications.

Findings of this study need to be interpreted with the following caveats. First, due to the rarity of BNT162b2 vaccine-related myocarditis, the results should be interpreted cautiously as the number of patients included in this study was relatively small, although this study is among the largest cohort of BNT162b2 vaccine-related myocarditis reported among adolescents globally. The fast-response and efficient adverse drug reaction reporting system in COVID-19 vaccination and comprehensive clinical management network allowed us to recruit all the confirmed cases within the study period, establishing a BNT162b2 vaccine-related myocarditis patient cohort for further investigation. Second, the choice of controls may not be perfect for our study since the controls were not exposed to mRNA vaccines. We acknowledge this as a limitation of our study and justify that the use of this control cohort is valid as a population control of allele frequencies. Third, the limited analytical power may affect the accuracy of the associations to vaccine-related myocarditis. It is possible that false positives were included during the early analysis stage or due to sequencing errors, which could have influenced the subsequent interpretations. To address these issues, ClusterAnalyzer was utilized to maximize the sensitivity. The focus was primarily on clusters of signals with similar significance, rather than including single discrete signals, in order to minimize the inclusion of false positives. Last, the grouping of SNPs may be inaccurate due to uncertainties in p-value estimates and LD relationships estimates by ClusterAnalyzer. This could have limited the ability of the tool to exclude false positives or to include true positives. The current tool was unable to prioritize genetic variants in the HLA and KIR loci due to weak LD among variants intrinsic to the hypervariability of these loci. Further modifications should be implemented to enhance the precision and accuracy of the association signals.

In conclusion, this study has presented preliminary genetic factors associated with BNT162b2 vaccine-related myocarditis. The identified 2,182 genomic clusters potentially associated with the side effect. Further analysis in the genes located in the 2 selected clusters, as well as the enriched pathways analysis, not only align with the current understanding of the BNT162b2 vaccine side effect but also shed light on previously undocumented directions. The group of genes related to inflammation, cardiac conduction, and ion channel activity demonstrated the predisposed immunity and cardiac function differences among the susceptible individuals. Additionally, the highlighted enriched gene sets responsible for plasma membrane adhesion and axonogenesis provided new insights into the genetic factors associated with BNT-162b2 vaccine-related myocarditis. As the vaccine platform is poised to become the foundation for future development of immune protection against pathogens and even disease treatment, it is important to consider identifying individuals who may be more susceptible to major sole-effect of the mRNA platform. This would help mitigate any potential risks associated with mRNA COVID-19 vaccine-related myocarditis.

Data availability

Source code of ClusterAnalyzer is available on GitHub under BSD-3 license (http://github.com/snakesch/clusterAnalysis). Summary association statistics of raw association signals and prioritized signals were publicly available in the repository.

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This work was supported by the Hong Kong Collaborative Research Fund (CRF) 2020/21 and the CRF Coronavirus and Novel Infectious Diseases Research Exercises (Reference Number: C7149-20G). The funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the manuscripts, and the decision to submit the manuscript for publication.

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Chun Hing She and Hing Wai Tsang contributed equally to this work.

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Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

Chun Hing She, Hing Wai Tsang, Xingtian Yang, Sabrina SL Tsao, Sophelia HS Chan, Mike YW Kwan, Gilbert T Chua, Wanling Yang & Patrick Ip

Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

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The project was conceptualized by PI, GTC and HWT. The samples were contributed by GTC and prepared by HWT. CSMT contributed the control data. Design, execution and interpretation of genetic analysis results was conducted by CHS, WY and XY. Manuscript was drafted by CHS, HWT and GTC. PI and WY provided valuable comments to the study. ST acquired the relevant ethics approvals. SC and MK supported the analysis of the study.

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Correspondence to Wanling Yang or Patrick Ip .

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The study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (Reference: UW20-292, UW21-149, UW21-157 and UW21-548), the Kowloon West Cluster Research Ethics Committee [Reference: KW/FR-20-086(148 − 10)] and the Department of Health Ethics Committee (Reference: LM21/2021). Written consent was obtained from parents or legal guardians of the participants.

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She, C.H., Tsang, H.W., Yang, X. et al. Genome-wide association study of BNT162b2 vaccine-related myocarditis identifies potential predisposing functional areas in Hong Kong adolescents. BMC Genom Data 25 , 51 (2024). https://doi.org/10.1186/s12863-024-01238-6

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DOI : https://doi.org/10.1186/s12863-024-01238-6

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• Vaccine-induced myocarditis
• BNT162b2 vaccine
• Genetic risk predisposition
• Vaccine side effect

BMC Genomic Data

ISSN: 2730-6844

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