genetic variants research paper

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• Published: 28 March 2024

Genetic variation across and within individuals

• Zhi Yu   ORCID: orcid.org/0000-0003-4810-3474 1 , 2   na1 ,
• Tim H. H. Coorens   ORCID: orcid.org/0000-0002-5826-3554 1   na1 ,
• Md Mesbah Uddin   ORCID: orcid.org/0000-0003-1846-0411 1 , 2 ,
• Kristin G. Ardlie 1 ,
• Niall Lennon 1 &
• Pradeep Natarajan   ORCID: orcid.org/0000-0001-8402-7435 1 , 2 , 3  

Nature Reviews Genetics ( 2024 ) Cite this article

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• DNA sequencing
• Genetic variation

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

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These authors contributed equally: Zhi Yu, Tim H. H. Coorens.

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Broad Institute of MIT and Harvard, Cambridge, MA, USA

Zhi Yu, Tim H. H. Coorens, Md Mesbah Uddin, Kristin G. Ardlie, Niall Lennon & Pradeep Natarajan

Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Zhi Yu, Md Mesbah Uddin & Pradeep Natarajan

Department of Medicine, Harvard Medical School, Boston, MA, USA

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T.H.H.C. and Y.Z. researched the literature. T.H.H.C., P.N. and Y.Z. contributed substantially to discussion of the content. T.H.H.C., M.M.U. and Y.Z. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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ORIGINAL RESEARCH article

Genetic variation and the distribution of variant types in the horse.

• 1 Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, United States
• 2 Interval Bio LLC, Mountain View, CA, United States
• 3 Department of Veterinary and Biomedical Sciences, University of Minnesota, Minneapolis, MN, United States

Genetic variation is a key contributor to health and disease. Understanding the link between an individual’s genotype and the corresponding phenotype is a major goal of medical genetics. Whole genome sequencing (WGS) within and across populations enables highly efficient variant discovery and elucidation of the molecular nature of virtually all genetic variation. Here, we report the largest catalog of genetic variation for the horse, a species of importance as a model for human athletic and performance related traits, using WGS of 534 horses. We show the extent of agreement between two commonly used variant callers. In data from ten target breeds that represent major breed clusters in the domestic horse, we demonstrate the distribution of variants, their allele frequencies across breeds, and identify variants that are unique to a single breed. We investigate variants with no homozygotes that may be potential embryonic lethal variants, as well as variants present in all individuals that likely represent regions of the genome with errors, poor annotation or where the reference genome carries a variant. Finally, we show regions of the genome that have higher or lower levels of genetic variation compared to the genome average. This catalog can be used for variant prioritization for important equine diseases and traits, and to provide key information about regions of the genome where the assembly and/or annotation need to be improved.

1 Introduction

Genetic variation is a key contributor to health and disease, and understanding the link between an individual’s genotype and the corresponding phenotype is a major goal of genetic research ( Genomes Project et al., 2010 ). Whole genome sequencing (WGS) within and across populations enables highly efficient variant discovery and elucidation of the molecular nature of virtually all genetic variation, from single nucleotide polymorphisms (SNPs) to copy number variants (CNVs) and other large structural variants ( Sudmant et al., 2015 ). Large-scale studies of genetic variation in humans have dramatically improved our understanding of genetic variation across a species and within populations ( Genomes Project et al., 2012 ). Large-scale variant catalogs establish patterns of variation across the genome, including non-coding regions ( Mu et al., 2011 ), permitting elucidation of regional variability in mutation and recombination rates. Knowledge of the background genetic “noise” helps to decipher the link between genotype and phenotype—allowing filtering of likely neutral variants from potentially deleterious variants in genomic regions of interest or within biologic candidate genes.

The equine reference genome ( Wade et al., 2009 ) has provided a key basis for genetic investigations in horse populations ( Rebolledo-Mendez et al., 2015 ; Raudsepp et al., 2019 ). However, despite consistent progress in our understanding of genetic disease in the horse, disease-causing variants have been identified for less than 20% of the currently recognized genetic diseases (Online Mendelian Inheritance in Animals, OMIA). Similar to humans, many of the significant GWAS regions of interest found for equine traits have been in non-coding regions of the genome. The unknown function of these regions has been a barrier to pinpointing and confirming the causal variant, which is necessary to develop effective genetic tests or targeted treatments. A more complete account of “normal” genetic variation in the horse is critical to establishing the link between genotype and phenotype ( Yngvadottir et al., 2009 ; Genomes Project et al., 2010 ).

Here we provide the first large-scale catalog of genetic variation in the horse derived from WGS, with the intent of describing variant numbers, types, allele frequencies, and genomic location distribution across the general population and within and across major breeds.

2 Materials and Methods

2.1 samples.

Paired-end whole genome sequencing was performed on 534 horses using Illumina technology ( Supplementary Table S1 ). Forty-four different breeds predominantly from North America and Central Europe were selected ( Supplementary Table S2 ) based on availability of Illumina WGS from previous and ongoing studies ( Finno et al., 2015 ; McCoy et al., 2016 ; Norton et al., 2016 , 2019 ; Schultz 2016 ; Bellone et al., 2017 ; Schaefer et al., 2017 ), publicly available data from the Sequence Read Archive ( Leinonen et al., 2011 ), and with the aim of collecting a minimum of 15 individuals per breed for 10 target breeds (Arabian, Belgian, Clydesdale, Icelandic, Morgan, Quarter Horse, Shetland, Standardbred, Thoroughbred, and Welsh Pony) that represent major groups of worldwide equine genetic diversity ( Petersen et al., 2013 ).

2.2 Mapping and Variant Calling

Standard fastq quality control and trimming were performed using Fastqc 0.11.8 and Trimmomatic 0.38, respectively. Raw reads were aligned to the EquCab 3.0 reference horse assembly ( Kalbfleisch et al., 2018 ) and variants identified using a modified version of the genome analysis toolkit best practices ( Van der Auwera et al., 2013 ), modified to allow for joint variant calling by GATK haplotype caller ( McKenna et al., 2010 ) and BCFtools mpileup ( Li H. et al., 2009 ). In brief, reads were mapped to the EquCab 3.0 reference genome using Burrows-Wheeler Aligner (BWA) ( Li and Durbin 2009 ). PCR duplicates were detected and removed using Picard tools ( https://broadinstitute.github.io/picard/ ) version 2.18.27 and then indel realignment was performed using the Genome Analysis Toolkit (GATK) version 4.1.0.0 indel realigner and base-quality score recalibration was performed ( DePristo et al., 2011 ). Genome variant call format (genome VCF) files were produced for each individual horse, and then group variant calling was performed using GATK haplotype caller version 4.1.0.0 ( McKenna et al., 2010 ) and BCFtools mpileup version 1.9 ( Li H. et al., 2009 ) using default settings. Hard-filtering was performed using the GATK best practice guidelines ( Van der Auwera et al., 2013 ). To maximize the specificity of the variants, the intersection of the variants across both callers was obtained using GATK “SelectVariants” and was used for downstream analysis ( Van der Auwera et al., 2013 ).

2.3 Variant Analyses

Descriptive statistics for both variant callers and the intersection were created using BCFtools ( Li 2011 ). Missingness for each horse and each variant site was calculated using VCFtools. The transition to transversion (TsTv) and heterozygous to non-reference homozygous (hetNRhom) ratios were calculated for each variant caller, across the population and by breed using BCFtools ( Li 2011 ) and Python. Predicted functional effect for each variant in the intersect file was determined using SnpEff ( Cingolani et al., 2012b ) with a custom dictionary based on the RefSeq version of EquCab 3.0 ( Schaefer et al., 2017 ). High, moderate, and low impact variants were extracted using SnpSift ( Cingolani et al., 2012a ) and Python was used to manipulate VCF files. Python and BCFtools ( Li H. et al., 2009 ) were used to manipulate output files.

2.4 Breed Differences Between Variants

Variants that were considered rare (<3%) or common (>10%) were extracted from each breed VCF file using Python and BCFtools ( Li H. et al., 2009 ). Variants that were rare in one breed and common in another, rare/common or common/rare in the breed/population, or uniquely present in one breed were selected for investigation. BCFtools view was used to extract variants that were only present in homozygous states, or were present in all genotyped individuals. Python was to extract variant details.

2.5 Regions of the Genome With High or Low Genetic Variation

BCFtools stats was used on 10 kb regions across the genome. R was used to calculate the average genetic variation and to find regions with high (more than twice the average variation) and low (less than half the average variation) levels of genetic variation. BCFtools view was used to extract these regions from the intersect. Python was then used to extract variant details and additional analysis performed with R.

2.6 Statistical Analyses

A Kruskal Wallis test and linear regression were used to determine if there were breed differences. The nonlinear relationship between depth of coverage and the number of variants identified was determined using R. Due to the association between depth of coverage and the number of variants detected, estimated marginal means (EMMEANS) ( Lenth 2018 ) were calculated, with depth of coverage included in the regression models. T tests were used to compare variant types and allele frequencies between coding and non-coding variants, and to compare constraint metric scores between high and low variation regions. A chi-square test was used to compare the variant impact between the high and low variation regions. Confidence intervals (95%) were calculated for each breed. All statistical analyses were performed using R. Significance was set at p < 0.05.

3.1 Variant Discovery

WGS of 534 horses across 46 different breeds ( Supplementary Table S2 ), was performed on Illumina platforms ( Supplementary Table S1 ). This sample set included DNA from a minimum of 15 horses in each of 10 target breeds (Arabian, Belgian, Clydesdale, Icelandic Horse, Morgan, Quarter Horse, Shetland Pony, Standardbred, Thoroughbred, and Welsh Pony) that represent major breed clusters in the horse ( Petersen et al., 2013 ), which were sequenced to a target depth of coverage (DOC) of 10X. Raw reads were mapped, quality control was performed, and variants were identified using a modified version of the genome analysis toolkit best practices pipeline ( Van der Auwera et al., 2013 ) (see Section 2). 155,201,208,820 total reads from the 534 horses mapped uniquely to the EquCab 3.0 reference genome ( Kalbfleisch et al., 2018 ). Mean and median read length, uniquely mapped paired reads, and depth of coverage, and ranges for these values are provided in Table 1 .

TABLE 1 . Median, mean, and range of summary statistics of the mapping pipeline derived from WGS data from 534 horses.

GATK Haplotype Caller and BCFtools identified 42,900,494 and 33,395,275 variants, respectively. To increase specificity of the identified variants, the intersect of both variant callers [31,140,769 variants (29,038,030 SNPs and 2,102,379 indels)] was used for downstream analysis ( Table 2 ). On average, there were 2.27 variants (range 0.88–3.12) per kb of sequence. The mean number of heterozygous sites per individual per kb was 1.54 (range 0.56–2.63). There was a significant non-linear association between the number of variants identified and the depth of coverage [DOC (correlation estimate 0.62, p adjusted 0.009)]. The distribution of variants by DOC was similar for each breed ( Figure 1 ), therefore, estimated marginal means (EMMEANs) ( Lenth 2018 ) accounting for DOC were used for further analyses. The median (range) degree of missingness for each individual horse was 0.01 (0.001–0.58) and for each variant site was 0.026 (0.000–0.998) ( Figure 2 ). Missingness by individual was moderately negatively correlated with DOC: correlation −0.45, 95% confidence interval −0.511 to −0.38 and p < 0.001 ( Figure 2 ).

TABLE 2 . Number of variants, TsTv, and HetNRhom ratio from 534 WGS identified by each variant caller [GATK Haplotype Caller (HC) and BCFtools (BT), and the union and intersection of the variant callers].

FIGURE 1 . Distribution of the average number of variants identified for each breed by DOC quantiles (Q1 = 1.43–7.14 X, Q2 = 7.15–9.16 X, Q3 = 9.17–14.6 X, Q4 = 14.7–46.7 X). The colored lines represent the 10 target horse breeds [Arabian, Belgian, Clydesdale, Icelandic horse, Morgan horse, Quarter Horse (QH), Shetland, Standardbred (STB), Thoroughbred (TB), and Welsh Pony (WP)] and the remaining horse breeds (Other).

FIGURE 2 . Missingness by individual (A) , by depth of coverage (B) and by chromosome (C) . The 10 target breeds and other breeds are represented in colors shown in the figure legend in Figures 2A,B .

The transition to transversion (TsTv) ratio and the heterozygous to non-reference homozygous (hetNRhom) ratios from the variant callers intersect were 1.94 and 2.24, respectively ( Table 2 ). There were significant but marginal breed differences in the TsTv and hetNRhom ratios ( p < 0.001), with the highest TsTv ratio in Shetlands (1.95) and the lowest in Thoroughbreds (1.92), The same was true with the hetNRhom ratio, which was the highest in Thoroughbreds (3.21) and the lowest in Clydesdales (1.48) ( Table 3 ). The majority (57%) of variants had a minor allele frequency (MAF) < 5%, Figure 3 ), with the mean MAF being 13.2% (0.09–100%). In total, there were 2,481,075 (2,447,610 SNPs and 33,465 indels) singleton variants.

TABLE 3 . Estimated marginal mean of the transition to transversion (TsTv) and heterozygous to non-reference homozygous (hetNRhom) ratios accounting for depth of coverage.

FIGURE 3 . Minor allele frequency distribution of the variants.

Each individual horse had on average 5,580,202 variants (5,099,978 SNPs and 480,224 indels), with on average 1,805,127 in homozygous and 3,775,075 in heterozygous states ( Table 4 ). There were also breed-specific differences in variant number and homozygous variant number per individual ( Table 4 ). The EMMEAN variants per individual, accounting for DOC, was lowest in Thoroughbreds (5,000,516) and highest in Belgians (6,100,544). The EMMEAN homozygous variants per individual, accounting for DOC, was also lowest in Thoroughbreds (1,225,441) and highest in Belgians (2,325,470).

TABLE 4 . EMMEANs for number of variants within breeds accounting for DOC, standard error (SE), and 95% confidence intervals, with breed and DOC as predictor variables.

The EMMEAN variant number per breed was significantly correlated with one estimation of effective population size across breeds ( Petersen et al., 2013 ) ( p = 0.02, Pearson’s correlation = 0.83, 95% confidence interval 0.22–0.97), but not a more recent estimate of effective population size across breeds using a higher marker density ( Beeson et al., 2019 ) ( p = 0.54, Pearson’s correlation = 0.26, 95% confidence interval −0.54–0.82). The EMMEAN homozygous variant number per breed was not significantly correlated with either estimate ( Petersen et al., 2013 ; Beeson et al., 2019 ) of effective population size ( p = 0.07, Pearson’s correlation = 0.72, 95% confidence interval −0.08–0.95 and p = 1, Pearson’s correlation = −0.001, 95% confidence interval −0.70–0.70, respectively).

3.2 Variants Shared Across Breeds

In total, 27,719,724 variants (25,386,978 SNPs and 2,332,746 indels) were shared by at least two breeds (shared variants, Supplementary Table S3 ). Only 2% (637,610) of these variants were genic (within 5,000 bp of a gene). Genic variants included 8,427 high (4,934 SNPs and 3,493 indels), 199,243 moderate (195,630 SNPs and 3,613 indels), and 429,940 low (427,436 SNPs and 2,504 indels) impact variants with 6,697 predicted to cause loss of function (LOF). Genic variants were within or near 10,013 individual genes. The mean shared variant MAF was 0.07. The mean shared variants per breed was 13,802,105 (range 11,800,825 in Clydesdales to 15,724,634 in Quarter Horses).

3.3 Variants With Large Allele Frequency Discrepancies

10,633,492 variants (121,900 genic) were considered rare (MAF <3%) in one breed and common (MAF >10%) in at least one other breed ( Supplementary Table S4 ). Each breed had on average 1,216,800 variants that had large allele frequency discrepancies with another breed. The fewest number of variant discrepancies were between the Quarter Horse and Thoroughbred (728,459 variants) and the highest number of variant discrepancies were between the Thoroughbred and the Shetland (2,028,658 variants).

4,876,293 variants (190,653 genic) were rare (MAF <3%) in one breed and common (MAF >10%, mean MAF 0.15) in the remainder of the study population (excluding individuals of that breed) (breed-specific rare variants, Supplementary Table S4 ). Each genic variant was present in or close to at least one of 10,361 genes. The mean number of breed-specific rare variants was 475,458. The fewest number of variant discrepancies was between the Quarter Horse and the population (8,248 variants) and the highest number of variant discrepancies was between the Thoroughbred and the population (913,739).

3,563,454 variants (62,803 coding) were common (MAF >10%) in one breed and rare (MAF <3%, mean MAF 0.02) in the remaining population (excluding individuals of that breed) (breed-specific common variants, Supplementary Table S4 ). On average, each of these variants was present in 1.13 breeds (range 1–5) and each genic variant was shared by 1.11 breeds (range 1–4). Genic variants were present in or close to at least one of 10,163 genes. On average, each breed had 444,464 variants that were common in the breed and rare in the population. The fewest number of variant discrepancies was between the Thoroughbred and the population (115,241 variants) and the highest number of variant discrepancies was between the Icelandic horse and the population (745,623).

3.4 Variants With No Homozygotes Present

2,889 variants (2,586 SNPs, 303 indels) were only present in a heterozygous state, with no homozygotes identified. Twenty-six of these variants were present within 14 different genes. 12/14 of these genes are uncharacterized or equine specific transcripts or olfactory related genes. Six were predicted to be high impact (four were predicted to be LOF variants), 10 were predicted to be moderate impact, and 10 were predicted to be low impact variants ( Supplementary Table S5 ). The allele frequency was marginally but significantly different ( p = 0.003) between non-genic (0.498) and genic (0.499) variants.

3.5 Variants Present in all Horses

114,733 variants (103,414 SNPs, 11,319 indels) were present in all 534 horses. Of these variants, 1,426 were present in genic regions of 504 genes. These variants were predicted to have a high (170 variants), moderate (644 variants) and low (612 variants) impact on phenotype, with 145 predicted to be LOF variants. Of these variants, 9,756 (9,351 SNPs, 405 indels) were homozygous in all horses. Ninety-two of these were present in genic regions affecting 58 genes. Eight were predicted to be high impact (four were predicted to be LOF), 46 were predicted to be moderate impact, and 38 were predicted to be low impact variants.

3.6 Regions of the Genome With High or Low Genetic Variation

The variant caller intersect file was first split by chromosome and then into 10,000 bp windows (240,910 regions in total) to determine the average number of variants per 10 Kb region across the genome. Regions with more than two times or less than half of the average number of variants were classified as regions of high or low variability, respectively. Each 10 Kb region carried on average 122 variants (range 0–3,143 variants), consisting of 114 SNPs (range 0–3,075 SNPs) and 8 indels (range 0–121). There were 6,341 regions with more than double the mean variant number, including, on average 414 variants (396 SNPs and 18 indels) with a TsTv ratio of 1.76. There were 17,791 regions with less than half the average number of variants, including, on average 35 variants (32 SNPs and three indels) with a TsTv ratio of 1.59.

Highly variable regions contained 2,625,382 variants ( Supplementary Table S6 ) with a mean MAF of 17% (range 0.01–100%). The most common variant types in the high variability regions were intergenic (1,287,507) and intronic (574,441) ( Figure 4 ). The low variability regions contained 20,777 variants with a mean MAF of 11% (range 0.01–100%). The most common variant types in the low variation regions were intronic (306,569) and intergenic (182,937) variants ( Figure 4 ). The variant impact was significantly different between regions of high and low variation [ p < 0.001 ( Table 5 )].

FIGURE 4 . Percentage of coding variants for each type of variant called by SnpEff for low variation regions (orange) and high variation regions (teal).

TABLE 5 . Impact of variants identified in high and low variation regions.

The allele frequency of the variants in the low variability regions was significantly less than the allele frequency of variants in high variability regions ( p < 0.001, 95% confidence interval: 0.06–0.06). For the genes with haploinsufficiency scores available, the mean score was lower ( p < 0.001, 95% confidence interval: −0.15 to −0.10) in genes containing variants in the high variability regions (0.23) compared to genes containing variants in the low variability regions (0.33). The predicted loss of function tolerance score was not significantly different ( p = 0.13, 95% confidence interval: −0.07–0.01) between genes containing variants in the high variability regions (0.33) compared to genes containing variants in the low variability regions (0.37).

4 Discussion

This report comprises the first large-scale catalog of genetic variation developed for the horse, a species with potential as a translational model for many athletic phenotypes. We used WGS of 534 horses to determine overall genetic variation in the general equine population as well as 10 individual breeds, report variants with population and breed MAF discrepancies, identify variants with no homozygotes as well as variants that are present in all individuals, and in addition identify genomic regions with high or low genetic variation.

The significant association between the number of variants identified and the depth of coverage was unsurprising, as it has long been recognized that deeper coverage improves variant calling accuracy ( Li R. et al., 2009 ). The nonlinear association between depth of coverage and number of variants identified is particularly relevant to future genetic studies, as there is minimal to no gain in the numbers of variants detected by increasing depth of coverage over 10X.

We elected to use the intersect of two commonly used callers based on evidence from previous work showing that specificity of identified variants could be improved ( Field et al., 2015 ). This method identified 29,882,273 variants, which is higher than the 25,800,000 variants previously identified in a smaller cohort of 88 horses using only GATK haplotype caller ( Jagannathan et al., 2019b ). This difference is likely due to the increased number of horses and inclusion of more genetically distinct breeds in our study, increasing our ability to identify rare variants down to a MAF across breeds of 0.0009 compared with 0.0057 in the earlier study. Additionally, we use the intersect of two variant callers (GATK HaplotypeCaller and BCFtools) to improve specificity of variants identified in the previous publication ( Jagannathan et al., 2019b ). While this likely would have led to a reduced sensitivity, we were still able to identify an additional 4,082,273 variants.

The 1.54 heterozygous variants per kb of sequence is similar to that reported in cattle (1.44 per kb) ( Daetwyler et al., 2014 ), but is higher than reported in the Yoruba (1.03 per kb) and European human populations (0.68 per kb) ( Genomes Project et al., 2010 ). The cause of this higher variation in the horse is likely related to the heterogeneity of this population, which included 46 different breeds, compared to three cattle breeds ( Daetwyler et al., 2014 ) and only a single population in the human studies ( Genomes Project et al., 2010 ). Given the limited genetic diversity ( Petersen et al., 2013 ) of the horse compared with human populations this is still somewhat somewhat surprising. However, a recent study of effective population size in the horse suggests that several breeds have larger effective population sizes ( Beeson et al., 2019 ) than reported in human populations ( Tenesa et al., 2007 ), and we would therefore, expect to see increased heterozygosity. Another reason for the increased number of heterozygous variants in horses is likely to be related to errors in the reference genome which, unlike the human reference genome that is based on multiple individuals, is based on a single horse ( Kalbfleisch et al., 2018 ). In the original paper from the 1,000 human genomes consortium, it was concluded that a site where every individual was homozygous for an allele not present in the reference genome was a reference genome error. This accounted for ∼1 error per 30 kb of sequence. In this study, we identified 114,733 variants that were present in every individual in this population, which are presumed to be related to an error in the reference genome or a true rare variant that is present in the individual horse sequenced for the reference genome. The length of the RefSeq equine reference genome is 2,506.95 MB, therefore, we would expect to see ∼1.37 errors per 30 kb of sequence, which may partially explain the increased number of variants in horses compared with humans.

Unsurprisingly, the degree of missingness per individual was negatively correlated with depth of coverage. This was not a linear correlation however, and beyond 10X coverage, there was minimal improvement in the degree of missingness, suggesting that 10X coverage is a reasonable target for population scale sequencing projects. The average missingness per individual varied greatly. Most of the horses with missingness >0.20 were horses that were not included in the breed analysis owing to being in the other breed category. Six of the Shetland ponies had missingness >0.20 and these were ponies with a targeted depth of coverage of ∼ 6X. The degree of missingness across the 32 autosomes and X chromosomes also varied, with the highest degree of missingness on chromosomes 12 and X. This may be related to larger uncharacterized regions on the X chromosome compared to most autosomes.

The TsTv and hetNRhom ratios are frequently utilized for quality control of sequencing data ( Guo et al., 2013 ). The TsTv ratio here was similar (1.94) to reports of the expected TsTv ratio from genome sequencing data in humans of ∼2 ( Genomes Project et al., 2010 ), which is a good indicator of SNP quality ( Guo et al., 2013 ; Wang et al., 2015 ). The slight reduction in our study compared with human studies is likely related to decreased genetic diversity and smaller effective population sizes in horses ( Petersen et al., 2013 ) compared with humans, as well as a lower quality reference genome ( Wade et al., 2009 ). The TsTv ratio varies across the genome, but in humans does not vary based on ancestry ( Wang et al., 2015 ). In the 10 target breeds, we did find that the TsTv ratio varied significantly but marginally by ancestry. This may be related to the varying depth of sequence coverage, which was not uniform across breeds. The hetNRhom ratio (2.24) was higher than the expected value of 2 based on Hardy-Weinberg equilibrium ( Guo et al., 2013 ) and there were breed differences. This is consistent with human ( Wang et al., 2015 ) and canine ( Jagannathan et al., 2019a ) reports that the hetNRhom ratio varies by ancestry. In humans, the highest median hetNRhom ratio was 2.0 in African populations with the lowest ratio of 1.4 in Asian populations; none of the populations investigated had a median hetNRhom ratio >2.0 ( Wang et al., 2015 ). However, at least one dog breed had hetNRhom ratio of 3.3 in the catalog of canine genetic variation ( Jagannathan et al., 2019a ). This is likely related to the increased levels of inbreeding in horses compared with most human populations.

The variant totals differed by breed, which is consistent with reports in different cattle breeds ( Daetwyler et al., 2014 ) and regional human populations ( Genomes Project et al., 2010 ). Previously, this has been related to effective population size ( Daetwyler et al., 2014 ), and we did see a significant association ( p < 0.0001) between the number of variants identified in each of the 10 target breeds and a report of effective population size using 54K SNP array data ( Petersen et al., 2013 ). However, this association was not seen with a more recent estimate of effective population size that used imputed genome-wide SNP data ( Beeson et al., 2019 ). This may partially be related to different breeds studied, as the Beeson et al. (2019) paper did not include effective population size estimates for the Shetland and Clydesdale breeds. Additionally, we are not accounting for the degree of relatedness between the breeds studied and the reference genome. In this study, the Thoroughbred (which is the EquCab3 reference genome breed) has the fewest variants compared to the other breeds, consistent with both the reference genome being from a Thoroughbred and having the smallest effective population size (1,784) in Beeson et al. ( Beeson et al., 2019 ). However, the Quarter Horse, which has the largest population size (6,516) ( Beeson et al., 2019 ), but is more related to the Thoroughbred ( Petersen et al., 2013 ) than other breeds, has a number of variants that is closer to the median, consistent with its close relatedness to the Thoroughbred ( Petersen et al., 2013 ), but inconsistent with the large effective population size ( Beeson et al., 2019 ). This would suggest, that while the number of variants does vary by breed, as seen in different human regional populations, the relationship between the horse breed and the reference genome appears to have had an effect in this population.

A large number of variants were shared by at least one other breed, which is not surprising given the close relatedness of the breeds investigated ( Petersen et al., 2013 ). However, there were also multiple variants with large allele frequency discrepancies between breeds. We defined a minor allele frequency <3% as rare and >10% as common due to limitations in the number of horses in each breed investigated, rather than values used in most human studies (<0.5% for rare variants and >5% for common ( Bomba et al., 2017 )). With the 534 horses here our power to detect variants present at a minor allele frequency of 3 and 0.5% in the population is 1.00 and 0.93, respectively. However, it is important to note that for the breed analysis with the least number of horses in the Clydesdales (19) our power to detect these allele frequencies is only 0.44 and 0.01, respectively. To have a power greater than 0.8 to detect all rare variants <3% or <0.5% allele frequency within a breed we would need to sequence 55 and 325 horses within that breed, respectively. We therefore, had 80% power to detect the rare variants (minor allele frequency 3%) in Quarter Horses, Shetlands and Thoroughbreds. The number of variants with marked population discrepancies was quite high, with ∼35% of variants considered rare in one breed and common in another. The reason for large allele frequency differences between populations is thought to be related to genetic drift ( Hofer et al., 2009 ) in humans. However, given that different horse breeds have been selectively bred for different traits ( Avila et al., 2018 ), it is also likely that selection at least partially accounts for some of the variants with large allele frequency discrepancies between breeds. Only ∼5% of variants were unique to a single breed, with about 10% of these being in coding regions. The ∼5% of unique variants in horses is also lower than seen in cattle where ∼31% of variants are unique to a single breed ( Daetwyler et al., 2014 ).

Variants with no homozygotes were explored to determine if there were variants present in the general population that could be embryonic lethal in homozygous form. 2,888/2,889 of these variants were present at a minor allele frequency in the general population greater than would be expected for a homozygous lethal disease (MAF > 0.10). The one variant that was rare had a MAF of 0.03 and was an intergenic in frame deletion. Using the rules of Hardy-Weinberg equilibrium we would need to sequence 1,111 horses to identify just one homozygote, therefore using this dataset alone, we cannot determine if this variant is embryonic lethal. Given that 96% of the variants with no homozygotes have an allele frequency around 0.50, it is highly unlikely that these variants are embryonic lethal; rather it is possible that these regions are related to mapping errors due to the presence of paralogs or pseudogenes, or the presence of structural variants. This is supported by the fact that all but two of the genes containing these variants were uncharacterized or equine only transcripts or olfactory receptor genes.

Identifying regions of the genome with high or low variation in the general population is critically important for the investigation of possible disease-causing variants, as regions with high genetic variability are less likely to contain disease-causing variants for fully penetrant Mendelian diseases ( Karczewski et al., 2019 ). However, our analysis unexpectantly found that genes in high variability regions had lower haploinsufficiency scores, suggesting that damaging variants are less well tolerated ( Huang et al., 2010 ) than for genes present in the low variability regions. This is likely due to the large number of genes without haploinsufficiency scores (70% of high variation region genes and 23% of low variation region genes). 65% of genes in high and 18% of genes in low variation regions had the “LOC” designation that are genes unique to the horse or are only predicted to be equivalent to a human gene, and therefore would not be included in human databases of variant constraint. This would suggest that as expected, genes in the low variability regions are more similar to human genes than genes in the high variability regions.

Overall, this is the first large-scale catalog of genetic variation in the domestic horse and will be highly useful for evaluation of background genetic variation in any future genetic study. This catalog has paved the way for future investigation of the regions of the genome that are shared or have marked MAF discrepancies across breeds. Regions with marked discrepancies between breeds, or between a breed and the population, can then be interrogated to further look for signatures of selection both across breeds, as well as within breeds. Additionally, further investigation into which of the variants that are present in all individuals are related to poor genome annotation or instead are true variants in the reference genome is needed and will lead to improvement of the horse reference genome in the future. By improving knowledge of the poorly annotated regions of the horse genome we will be able to correct these for future versions of the reference genome. This will have benefits for future phenotype-causing variant identification studies. Given the relatively unique utility of the horse as a model for human athletic related traits ( Hill et al., 2010 ; Rooney et al., 2018 ) and diseases ( Ward et al., 2004 ; McCue et al., 2008 ; McIlwraith et al., 2012 ; McCoy et al., 2013 ; Norton et al., 2016 ), an improved ability to identify phenotype-causing variants in the horse may shed light on analogous genetic diseases in humans. This is especially true for complex phenotypes such as athleticism, osteoarthritis ( McIlwraith et al., 2012 ), and exertional rhabdomyolysis ( Norton et al., 2016 ) where the limited genetic diversity in the horse ( Petersen et al., 2013 ) may further accelerate our ability to identify the true phenotype-causing variants. This improvement in the reference genome combined with an improved understanding of the background genetic variation ( Yang et al., 2014 ; Farwell et al., 2015 ; Ellingford et al., 2016 ; Hartmannová et al., 2016 ; Känsäkoski et al., 2016 ; Kojima et al., 2016 ; Noll et al., 2016 ; Smedley et al., 2016 ; Dolzhenko et al., 2017 ; Eldomery et al., 2017 ; Schneider et al., 2017 ) in the horse should vastly increase the identification of phenotype-causing variants for important equine diseases.

Data Availability Statement

The variant datasets presented in this study can be found online at: https://www.ncbi.nlm.nih.gov/bioproject/PRJEB47918 .

Ethics Statement

Ethical review and approval was not required for this animal study because this study used samples previously collected by our lab and collaborators with institutional ethics review and approval and written consent from owners for the participation of their horses this study. The remainder of the samples were publicly available.

Author Contributions

SD-A was involved in the grant-writing, study design, data analysis, and manuscript preparation. MM and JM were responsible for the grant-writing and study design. They also supervised and provided expertise for the data analysis and manuscript preparation. RS, BG, and WC assisted with developing and running the mapping and variant calling pipelines. All authors approved the manuscript prior to submission to the journal.

This work was supported by: USDA NIFA-AFRI Project 2017-67015-26296: Tools to Link Phenotype to Genotype in the Horse, The American Quarter Horse Association, and a University of Minnesota Multistate grant. Salary support for SD-A was provided by an American College of Veterinary Internal Medicine Foundation fellowship, by a T32 Institutional Training Grant in Comparative Medicine and Pathology (5T320D010993-12), by the 2019 Elaine and Bertram Klein Development Award, and a Morris Animal Foundation Postdoctoral Research Fellowship (D20EQ-403).

Conflict of Interest

Authors BG and WC own and work for IntervalBio LLC, the computational company that was compensated to develop the mapping and variant calling pipeline.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: http://www.msi.umn.edu .

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2021.758366/full#supplementary-material

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Keywords: genetic variation, whole genome sequence, variant discovery, equine, breed differences, genetics

Citation: Durward-Akhurst SA, Schaefer RJ, Grantham B, Carey WK, Mickelson JR and McCue ME (2021) Genetic Variation and the Distribution of Variant Types in the Horse. Front. Genet. 12:758366. doi: 10.3389/fgene.2021.758366

Received: 16 August 2021; Accepted: 10 November 2021; Published: 02 December 2021.

Reviewed by:

Copyright © 2021 Durward-Akhurst, Schaefer, Grantham, Carey, Mickelson and McCue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: S. A. Durward-Akhurst, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Population genetics: past, present, and future

• Published: 18 July 2020
• Volume 140 , pages 231–240, ( 2021 )

Cite this article

• Atsuko Okazaki 1 , 2 ,
• Satoru Yamazaki 3 ,
• Ituro Inoue 4 &
• Jurg Ott   ORCID: orcid.org/0000-0002-6188-1388 2  

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We present selected topics of population genetics and molecular phylogeny. As several excellent review articles have been published and generally focus on European and American scientists, here, we emphasize contributions by Japanese researchers. Our review may also be seen as a belated 50-year celebration of Motoo Kimura’s early seminal paper on the molecular clock, published in 1968.

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Introduction

In recent years, large amounts of DNA sequencing data have been generated in various projects such as 1000 Genomes (Genomes Project et al. 2010 , 2012 , 2015 ), the ALSPAC database (Fraser et al. 2013 ; Hameed et al. 2017 ), and Icelandic (Gudbjartsson et al. 2015 ), and Japanese populations (Nagasaki et al. 2015 ). Major achievements of these efforts have been as follows: (1) Larger genetic variation is observed within populations than between populations, and (2) each individual harbors large numbers of variants with low allele frequencies. These findings have long ago been predicted by population genetics and evolutionary studies. Therefore, it is instructive to look back at historic achievements in population genetics.

Excellent reviews of population genetics have been written (Chakraborty 2006 ; Charlesworth and Charlesworth 2017 ; Crow 1987 ; Crow and Kimura 1970 ) documenting the development of population genetics from early achievements by Mendel ( 1866 ), Hardy ( 1908 ), and Weinberg ( 1908 ) up to highly sophisticated theoretical developments, mostly by American, British, and Japanese scientists. Here, we review selected aspects of population genetics, genome evolution, and molecular phylogeny with an emphasis on contributions by Japanese researchers.

Historical aspects of population genetics and road to the neutral theory

Darwin’s theory of evolution through selection very well explains changes in time of heritable phenotypes. In the early 1900s, focusing on the evolution of genetic variants in the population, R. A. Fisher, S. Wright, and J. B. S. Haldane made fundamental theoretical contributions to population genetics (Provine 1971 ), Fisher in his 1922 paper (Fisher 1922 ), which was the first to introduce diffusion equations into population genetics, and Haldane in developing in 1927 (Haldane 1927 ) the approximation of change of numbers of copies of very rare mutants by branching processes. Wright ( 1938 ) developed the theory on the effects of genetic drift, that is, random changes in small populations. While his theory was supported only by a minority of scientists in an era when the molecular basis of genes had yet to be proven and the effects of genetic drift were underestimated, Wright’s theory made a great contribution to connecting Mendelian Genetics with the Darwinian theory of evolution.

More recently, it has become apparent that many molecular changes have no effects on phenotypes. Based on Wright’s drift hypothesis and Haldane’s approximation model of an advantageous mutation (Haldane 1927 ), Motoo Kimura ( 1964 ) then developed his neutral theory based on backward diffusion models, which showed the probability of fixation to zero of a variant in the population to be equal to 2  s ( N e / N ), where s is the selection coefficient, N the size of the breeding population, and N e the effective population size.

Mutations and selection are driving forces for evolution. Basically, mutations occur at random DNA bases. Harmful mutations tend to be eliminated within a short period of time and do not contribute to long-term evolution. This process is called negative or purifying selection as opposed to positive selection. Before Kimura ( 1964 ) proposed his neutral theory, there was little notion of neutral variation, although, at about the same time, Lewontin and Hubby ( 1966 ) considered the possibility of neutral mutation as a possible reason for a large amount of variation which they found in electrophoretic mobility. Still, natural selection was the mainstream hypothesis with the idea that advantageous variations in populations are the driving forces for evolution, and deleterious variations are removed in a rapid manner.

At the time, population genetics usually considered two alleles at each gene locus based on the assumption of genes being base pairs. On the other hand, Kimura and Crow ( 1964 ) assumed an infinite allele model (“neutral isoalleles”) and proposed that genetic variation in populations arises as to the balance between mutations and genetic drift. Comparing hemoglobin molecules between different organisms, Kimura ( 1968 ) postulated that amino-acid substitution rates are so high that they can only be explained by neutral mutations. In other words, mutation and random changes in a finite population can maintain considerable variation through random fixation of selectively neutral or nearly neutral mutants. In the light of current knowledge, however, Kimura’s reasoning appears somewhat flawed. For example, he argued that the “cost of natural selection” would be too high otherwise—more consideration has shown that no cost is imposed by beneficial mutations in the absence of environmental deterioration. He also used the total amount of DNA without distinguishing protein-coding regions and non-coding regions. Nonetheless, Kimura’s contributions to population genetics have been tremendous.

Together with the Darwinian selection hypothesis, the neutral theory is one of the two pillars of genome evolution. Thus, ‘survival of the luckiest, and not necessarily of the fittest’ may be a good explanation for the evolution of a great majority of genetic changes (Chakraborty 2006 ). Interestingly, Kimura ( 1969 ) also proposed the “infinite sites model”. In this model, if the mutation rate is low and the effective population size is small ( θ  = 4 N e µ « 1), a mutant variant will always appear at a different site in the genome. If so, identity by state at the variant can be regarded as identity by descent, and in this respect, the infinite sites model represents one of the bases for genome-wide association studies using SNPs as genetic markers in unrelated individuals (Sella and Barton 2019 ).

The nearly neutral theory

The evolutionary rate, λ  =  fμ , in the neutral theory ( f is the proportion of neutral mutations among all mutations in a gene, μ is the mutation rate) disregards mutations favorable to survival and simply classifies other mutations into neutral ( f ) and deleterious (1 −  f ) mutations. However, the extent of harmfulness measured by the selection coefficient, s , is a continuous quantity. Based on these ideas, Tomoko Ohta (Ohta 1973 , 1992 , 2002 ), who had built the foundation of the neutral theory with Motoo Kimura, proposed the “nearly neutral” theory, where slightly disadvantageous mutations (attenuated mutations) could persist in the population by chance if the population is small. Thus, according to her publications (Ohta 1973 , 1992 , 2002 ), a substantial fraction of changes is caused by random fixation of nearly neutral changes, a class that includes intermediates between neutral and advantageous, as well as between neutral and deleterious classes, although other population geneticists may disagree with this view (Kondrashov 1995 ; Nei 2005 ).

A difference from the neutral theory is that the nearly neutral theory allows for interactions between (1) genes having occurred through weak natural selection (or weak deleterious selection) and (2) genes without weak natural selections, and for the two types of genes to jointly contribute to evolution by opposing the action of genetic drift (Hurst 2009 ). In the nearly neutral theory, the effect of genetic drift is weakened, and slightly disadvantageous mutations are excluded from a population if the population is extremely large; if a population is small, then slightly disadvantageous mutations are kept (some are even fixed) by the effects of genetic drift. It seems that the structure of very large datasets such as 1000 Genomes or the Exome Sequencing Project 6500 can be explained by the nearly neutral theory, because there is increasing evidence that selection pressure in small populations such as mammals including humans is weaker compared to that in ancestral species, and slightly disadvantageous mutations have been accumulating in populations (Kosiol et al. 2008 ; Nelson et al. 2012 ; Nielsen et al. 2009 ; Tennessen et al. 2012 ).

Evolutionary rate of pseudogenes

In the second half of 1970, accumulated sequencing data confirmed the prediction by King and Jukes ( 1969 ) that mutation rates of synonymous variants are higher than those of non-synonymous variants, which supports the neutral theory. Kimura ( 1977 ) asserted that according to the neutral mutation-random drift hypothesis, most mutant substitutions detected among organisms should be the results of random fixation of selectively neutral or nearly neutral mutations. This conjecture was verified by the analysis of mutation rates of pseudogenes, that is, of genes with sequences similar to normal genes having lost their functions as they were duplicated to another location in the genome, and in the process, their transcription sequences were not preserved. Based on the neutral theory, Takashi Miyata calculated the replacement rates of non-synonymous variants and synonymous variants in nucleotide sequences of several pseudogenes, α and β globin, and compared them with those in their functional counterparts (Miyata and Hayashida 1981 ). Results showed that replacement rates were uniformly the same in different pseudogenes and almost equal to the mutation rate, with no other gene evolving at a faster rate. This observation clearly supported the neutral theory.

Junk DNA, a term publicized by Susumu Ohno ( 1972 ) but rarely used today (see below), contains inter-genic regions, most of which are SINEs ( S hort IN terspersed E lements) and LINEs ( L ong IN terspersed E lements). The term ‘junk DNA’ was mentioned by a few other authors in 1972 and even 9 years earlier in a paper little known to human geneticists (Ehret and De Haller 1963 ), but Ohno’s name tends to be most closely associated with this term.

Evolutionary rates of junk DNA are expected to be similar to those of synonymous mutations and pseudogenes. In mammals, most of the genome regions, likely well more than 90%, are predicted to be junk DNA. Therefore, evolutionary rates of whole genomes can be approximated as being those of junk DNA.

In 2012, the Encyclopedia of DNA elements (ENCODE) project (Consortium 2012 ) proved biochemical functions of 80% of the genome, especially outside of protein-coding regions, which was once considered junk DNA. The findings from the ENCODE project enable us to further explore the function of the human genome.

Genes and genomic duplication

In higher organisms, genomic duplication is known to be extremely important for evolution. Early on, Susumu Ohno proposed that evolution is caused by genomic duplication, which was a visionary idea at a time when large sequencing data were not yet available (Ohno 1970 ). It has been shown empirically and by theoretical considerations that the advantage of creating new copies of genomes (or individual genes) can result in higher fitness. An alternative model explaining genomic duplication is DDC ( D uplication D egeneration C omplementation) (Lynch and Conery 2000 ). In the DDC model, regulatory elements each controlling independent functions are duplicated and random null mutations in the regulatory elements through degeneration lead to sub-functionalization, where the regulatory elements complement each other to achieve the full ancestral repertoires. What is important in the process is that it does not require the help of positive selection, that is, functional diversification. In practice, it has been proposed that the selection of slightly disadvantageous mutations works with the expression level of each gene changing. Therefore, genetic duplication is predicted to proceed in a nearly neutral manner based on mutation pressure and genetic drift. In addition, “concerted evolution” in minisatellites used as markers for hyper-polymorphisms, and in other sequences such as rRNA genes can be explained well by Ohno’s theory (Hillis et al. 1991 ; Jeffreys et al. 1985 ).

Molecular phylogeny

Through evolution, currently, living organisms have descended from common ancestors. Systematic biology seeks to unravel relationships among organisms and to establish evolutionary trees. As every biology student knows, the classical approach to such discoveries is through painstaking analysis of morphological details. Depending on which of these phenotypes are considered most important, different relationships among organisms emerge.

Rather than relying on phenotypes that may or may not be heritable, molecular phylogeny relies on DNA sequences and their comparisons among organisms. Researchers with various backgrounds have made significant contributions to methods of creating phylogenetic trees and the evaluation of phylogenetic relationships. In this field, Joseph Felsenstein almost single-handedly established this field as a special branch of population genetics (Felsenstein 2004 ). For example, he introduced the maximum-likelihood method of establishing phylogenetic trees (Felsenstein 1978 ) (see below). One of his other contributions is the “Felsenstein Zone” (Huelsenbeck and Hillis 1993 ), which involves the phenomenon of “long-branch attraction”; that is, long branches will appear similar to each other and appear as sister taxa on a tree even though they do not share a common ancestry. The Zone is the set of trees on which long-branch attraction occurs. Such phenomena have been observed in many datasets and simulation analyses, and have led to the discovery of long-branch attraction, which leads to wrongly assuming phylogeny where none exists (Huelsenbeck and Hillis 1993 ). Furthermore, Felsenstein contributed greatly to molecular phylogeny by developing a program package, PHYLIP, combining various phylogenic tree estimation methods including DNAML. Thanks to his contributions, molecular phylogeny has become increasingly popular for empirical molecular evolutionists.

The development of molecular phylogeny may not seem to be related to disease gene discovery. However, it greatly contributes to such discoveries through interpretation of huge sequencing datasets obtained from the 1000 Genomes project and other projects. Generating a molecular phylogenetic tree for phylogenetic relationships between species led to the discovery of gene families (orthologs and paralogs). The coalescent theory, which examines the gene tree in a species by reversing the time, was also applied to reconstruct the demographic history of species of interest. In particular, regarding the coalescent theory, Tajima ( 1983 ) estimated nucleotide diversity based on the limited DNA polymorphic data, calculated the time of coalescence of genes sampled from a single population, and their theory applies to a few genes at the time of population splitting. Takahata and Nei ( 1985 ) further developed a coalescent theory from DNA sequencing data and theoretically showed that alleles with deep coalescences are relatively rare.

The neighbor-joining method

Many methods for creating (estimating) phylogenic trees have been developed. Historically, these methods can roughly be classified into two groups, distance matrix methods and character state methods. The former uses a distance matrix and estimates evolutionary distance such as the number of amino-acid substitutions or base substitutions based on all possible pairs of OTUs (Operational Taxonomic Units). This method was first applied to create phylogenic trees in the form of the UPGMA (Unweighted Pair Group Method with Arithmetic mean) method, where clusters of neighboring OTUs are created and connected in a stepwise fashion. The method is used not only for amino-acid or base-pair sequences but also in numerical taxonomy, which deals with expression analysis using microarray (Eisen et al. 1998 ) or trait-encoded information (Sokal and Michener 1958 ). However, since this method assumes constant evolutionary speed, it is problematic to apply to amino-acid or base-pair sequence data. To overcome this problem, distance methods were developed that did not assume a molecular clock (Fitch and Margoliash 1967 ). Masatoshi Nei and Naruya Saitou greatly improved upon this method and developed a much faster procedure (Saitou and Nei 1987 ). This method is one of the “star decomposition” methods that determine which, of a given pair of sequences, reduces length of the total tree most and combine neighboring nodes until all OTUs are included. In the neighbor-joining method, “neighbors” keep track of nodes on a tree rather than taxa or clusters of taxa. A modified distance matrix is obtained in which the separation between each pair of nodes is adjusted on the basis of their average divergence from all other nodes. The tree is constructed by joining the least-distant pair of nodes in this modified matrix. When two nodes are joined, their common ancestral node is added to the tree and the terminal nodes with their respective branches are removed from the tree. At each stage in the process, two terminal nodes are replaced by one new node. This iterative operation finds “neighbors” one after another, which creates the final phylogenetic tree. The neighbor-joining method is the most commonly used distance matrix method. Starting in 1971, Nei proposed that Nei’s distance be used for phylogenetic tree estimation, which was later incorporated into the neighbor-joining program package MEGA (Kumar et al. 1994 ; Saitou and Nei 1987 ).

The second group, character state methods, do not use a distance matrix and define characters (phenotypes) and use them for exploring tree topology. One of the examples of character state methods is the maximum-likelihood method discussed in the next section.

The maximum-likelihood method

Maximum likelihood (ML) was developed by Fisher ( 1922 ) as a method to estimate parameters in statistical models. It has several advantages over other methods, but tends to be more complicated to apply than simpler methods. In population genetics, Luigi Luca Cavalli-Sforza first applied the ML method to an approach for creating phylogenic trees based on allele frequencies (Cavalli-Sforza and Edwards 1967 ). The first use of maximum-likelihood inference of trees from molecular sequences was by Jerzy Neyman (Felsenstein 2001 ; Neyman 1971 ). Felsenstein proposed ML for creating phylogenic trees based on allele frequencies as continuous quantities (Felsenstein 1973a ), thus improving on the method previously proposed by Cavalli-Sforza, and introduced ML for estimating trees based on discrete datasets and the maximum parsimony criterion (Felsenstein 1973b ). Masami Hasegawa incorporated this approach into the MOLPHY program package and pioneered in the use of model selection methods such as AIC in comparing phylogenies (he was a member of Akaike’s institute) (Adachi and Hasegawa 1992 , 1996 ).

The ML method is the most efficient approach among all tree construction methods. For example, false-positive evidence of relationships of long branches (“long-branch attraction”) will not occur when trees are estimated by ML and the model of evolution is correct, although it can occur when the model is not correct. However, the ML method tends to be time-consuming and, for some large trees, may be impossible to apply.

Impact of variants on multifactorial disorders and missing heritability

Based on the material mentioned so far, we will now cover some topics on how progress in population genetics, genome evolution, and phylogenic studies can be applied to medical research.

Multifactorial disorders are assumed to occur through interactions between multiple genetic and environmental factors. Therefore, identifying disease susceptibility genes has been considered difficult, and detecting interactions with environmental factors even more so. Especially in the 1990s, such considerations were widespread, quite in contrast to the relative ease with which increased numbers of gene identifications for monogenic disorders have been achieved. However, there was a researcher to struggle with the solution for genetic causes of multifactorial disorders at that time. Ituro Inoue succeeded in narrowing down disease loci using linkage analysis with affected sib-pairs and constructing haplotypes of the angiotensinogen (AGT) gene using limited data (Inoue et al. 1997 ). Inoue assessed linkage disequilibrium (LD) at each site in the AGT gene and further demonstrated by in vitro functional assay that the combination between A (− 6) and T235 alleles affects the expression of the AGT gene. This study was visionary, since LD block structures had yet to be proved at that time.

After that, genome-wide association studies with large SNP data over the whole genome became available thanks to the HAPMAP project, SNP collections by Perlegen Science, LD block measurements, and construction of haplotype maps (HapMap 2005 ; Hinds et al. 2005 ). Although such genome-wide studies contributed to narrowing down locations of disease susceptibility genes, results are still insufficient for identifying many specific disease susceptibility genes, for example Moyamoya disease (Liu et al. 2011 ). A remaining challenge has been that identified susceptibility loci show only small odds ratios, and all susceptibility loci combined only explain up to 30% of most of the disease causes. These numbers are generally smaller than the heritability calculated in the previous twin studies, which is known as “missing heritability” (Manolio et al. 2009 ). Nowadays, however, methods for calculating SNP-based heritability have been developed (Yang et al. 2017 ) that come up with heritability estimates close to those obtained by classical segregation analysis, and part of the problem seems to be resolved.

Out-of-Africa hypothesis

Recent advances in sequencing technology have enabled the identification of whole genome structures at population levels. These successes have made it possible to compare current human genome sequences with ancient genomes such as Homo neanderthalensis or Denisova hominin , which greatly contributed to the understanding of the origin of Homo sapiens (Nielsen et al. 2017 ). Allan Wilson, along with Rebecca Cann and Mark Stoneking, first proposed the “out-of-Africa” hypothesis (Cann et al. 1987 ), which claims that Homo sapiens originated in Africa and then spread all over the world. They based their results on the analysis of mitochondrial DNA of various populations, which represented the first phylogenic tree of Homo sapiens . Work by Masatoshi Nei contributed to the out-of-Africa hypothesis: In the 1970s, Nei calculated heterozygosity for various protein isozymes and created phylogenic trees of Homo sapiens (Nei and Roychoudhury 1972 , 1974 ; Nielsen et al. 2017 ). An interesting finding based on this work is that genetic variation estimated by Nei’s distance or Wright’s F st is larger within populations than between populations (Lewontin 1972 ), which was later confirmed by the 1000 Genomes project. In other words, there are greater differences among individuals in a given population than between populations. However, this notion has also been challenged (Edwards 2003 ).

Relationship between recent explosive population growth and origin of deleterious variants

Numerous human genome sequence projects such as 1000 Genomes revealed that each individual harbors considerable numbers of private mutations. This fact had been proposed by Haldane in his “genetic load” theory, which predicted an association between the numbers of variants possessed over populations and survival rate (Haldane 1937 ). In his theory, he claimed that if we consider genetic load for the whole genome rather than a given locus, the fitness decrease by mutations is equal to the mutation rate, v , irrespective of the extent of selection. He also claimed that pathogenic mutations accumulate in the form of heterozygous variants unless such mutations are excluded as lethal homozygous mutations (Haldane 1937 ) (this theory is also known as the Haldane–Muller principle). The theory of genetic load was further elaborated upon by Kimura ( 1960 ); for neutral mutations, there is no load. Based on this background, for variants whose distributions differ among populations, estimating the age of each variant becomes possible, which is important for understanding the history of human evolution, as well as for developing novel methods for disease gene discovery. The mathematical theory of coalescence allowing haplotype and allele ages to be calculated was developed by John Kingman ( 2000 ), and Kimura and Ohta ( 1973 ) proposed a formula for determining allele age, − 2 x (1 −  x )/log( x ). This formula represents the expected age of a neutral mutation of frequency x in a stationary population based on a diffusion process used in classical population genetics. Although there was a discussion regarding the restrictive assumption that the age distribution of a mutant allele with population frequency x should be the same as the distribution of the time to extinction of the allele, conditional on extinction, it made a great contribution to later calculations of allele age (Fu et al. 2013 ). Calculating allele age assuming the infinite many sites of model of mutation developed Kimura and Ohta formula, it showed that about three-quarters of all protein-coding SNV predicted to be deleterious across in the past 5000 years (Fu et al. 2013 ). This attempt provides important practical information that can be prioritized variants in disease gene discovery.

Inbreeding (mating between relatives) has so far not been discussed here as it does not lead to changes in allele frequencies. It does, however, lead to a decrease in heterozygotes and a corresponding increase in homozygotes. As is well known, at a bi-allelic locus with allele frequency p , the proportion of heterozygotes is given by 2 p (1 −  p )(1 −  F ), where F is the inbreeding coefficient. In many human populations, F tends to be rather small; for example, F  = 0.00038 in the UK (Pattison 2016 ). An exception is offspring of first cousins ( F  = 1/16). For rare deleterious recessive traits with disease allele frequency p , recessive offspring of first-cousin marriages occur with probability p 2  +  p (1 −  p ) F (Haldane and Moshinsky 1939 ). Through genetic linkage of such a trait with SNPs surrounding it, rare recessive traits tend to be located in long runs of homozygous SNPs (homozygosity mapping (Lander and Botstein 1987 )). More modern approaches have been developed, for example, based on the Hamming distance between chromosomes in affected and control individuals (Imai et al. 2015 ). This approach revealed a mutation, p.H96R in the BOLA3 gene, possibly having originated in a single Japanese founder individual (Imai et al. 2016 ).

Darwinian (evolutionary) medicine

From the viewpoint of Darwinian medicine (or evolutionary medicine), which is medicine based on evolution (Williams and Nesse 1991 ), we discuss a few aspects of how discovering variants can translate into medical care.

In the 1960s, Richard Lewontin discovered in Drosophila populations that heterozygosity is more often observed than expected (Lewontin and Hubby 1966 ). He interpreted this finding as advantageous fitness of heterozygosity compared to the homozygous state of the wild type or mutant (so-called over-dominance, or balancing selection) and emphasized its importance for survival. After the establishment of the neutral theory, as described below, the importance of balancing selection for some types of variants with high allele frequencies was rediscovered. Theoretical studies on natural selection also greatly progressed and “Tajima’s D”, developed by Fumio Tajima, is computed as the difference between two measures of genetic diversity: the mean number of pairwise differences and the number of segregating sites, each scaled so that they are expected to be the same in a neutrally evolving population of constant size. This is a unique contribution to statistical genetics by Japanese researchers in that this method can assess whether a given variant scattered over the whole genome is neutral or under selection pressure (Tajima 1989 ).

Analyzing genome sequences in several populations using the techniques of next-generation sequencing reveals some signals with positive selection pressure. One such example is infection-related diseases. Regarding the natural selection for resistance of a pathogen, this was revealed by next-generation sequencing to represent the strongest positive selection pressure in human evolution; that is, the well-known balancing signals on glycoproteins and positive selection signals on TLRs (Ferrer-Admetlla et al. 2008 ). Applying the history of evolution for various pathogens to disease susceptibility research will likely identify functional variants as well as intra-cellular mechanisms and treatment for various diseases. We believe that selection pressure for ancient pathogens will affect not only infectious and auto-immune diseases but also other traits. Recently, the association between life-style diseases and natural selection has become an attractive topic. Using 40 traits from the UK Biobank, functional low-frequency variants have been revealed to be under negative selection (Gazal et al. 2018 ). An alternative suggestion has been that positive selection acts on susceptibility loci for life-style diseases. An example is the thrifty gene hypothesis. At the dawn of the era of genomic medicine, the ancient history of human evolution is a powerful tool for understanding human biology leading to improving human health.

In this outline, we deliberately emphasized contributions to population genetics by Japanese researchers—in this field, Japanese scientists have arguably carried out comprehensive fundamental work. Thus, we feel justified in presenting this short review of population genetics from a Japanese point of view.

In terms of future developments in population genetics, we expect DNA sequencing to play an ever-increasing role. In an era where human genome sequence projects are underway around the world, established population genetics principles will be applied to reveal more detailed migration history, population history, and mechanisms of selection pressure, particularly in small ethnic populations (Antonio et al. 2019 ; Lipson et al. 2020 ).

Technological advances have changed the landscape of genetic screening (Ceyhan-Birsoy et al. 2019 ). Together with epidemiological and molecular genetics studies, population genetics approaches have demonstrated the association between disease mechanisms and mutations in populations. Cystic fibrosis is one such successful example (Bell et al. 2020 ). By identifying the relationship between specific mutations and a cystic fibrosis transmembrane conductance regulator (CFTR) defect, we can improve patient care including disease monitoring and treatment decisions. In the future, improvement of patient care in more diseases can be achieved by the combination of population genetics, epidemiological studies, and molecular genetics studies.

With the huge amount of genomic information currently available, it is challenging to link genotypes to phenotypes, predict regulatory functions, and classify mutant types. Therefore, new and innovative approaches are needed for further understanding of medical biology and connections to genetic disease. One approach is to collect previously reported SNV information and create a suitable mathematical model. As an example, a study by Davis et al. ( 2016 ) describes a biophysical metric of cardiomyocyte function, which accurately predicts human cardiac phenotypes.

Another approach is based on neural networks to automatically extract relevant features from input data (Zou et al. 2019 ). Since advances in sequencing technologies provide large amounts of data, it is realistic to utilize machine learning as a tool for analysis in the field of clinical healthcare and population genetics. Although deep learning has great potential, attempts to apply it to genomics have only just begun. For example, SpliceAI, a 32-layer deep neural network (DNN) was developed for predicting de novo mutations with predicted splice-altering consequences in patients with neurodevelopmental disorders, which paves the way for the application of deep learning on complex genetic variant prediction (Jaganathan et al. 2019 ). To identify pathogenic mutations in patients with rare diseases, a DNN model was developed combining common variants derived from human and six non-human primate species. The proposed model achieved an 88% accuracy and found 14 unreported candidate genes associated with intellectual disability (Sundaram et al. 2018 ).

Finally, epidemics and pandemics of viruses and their sequences provide rich sources of information. For example, population genetic analyses of 103 SARS-CoV-2 genomes indicated the presence of two major lineages, although the implications of these evolutionary changes remained unclear (Tang et al. 2020 ).

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Acknowledgements

Helpful comments by Prof. Joseph Felsenstein on an earlier version of this manuscript are gratefully acknowledged. This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI, Grant numbers JP20K08497 and JP18K15863 (A. O.), and Grant number 19K09408 (S. Y.).

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Atsuko Okazaki

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Okazaki, A., Yamazaki, S., Inoue, I. et al. Population genetics: past, present, and future. Hum Genet 140 , 231–240 (2021). https://doi.org/10.1007/s00439-020-02208-5

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The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

• Ammar J. Alsheikh   ORCID: orcid.org/0000-0001-7125-0144 1 ,
• Sabrina Wollenhaupt 2 ,
• Emily A. King 1 ,
• Jonas Reeb 2 ,
• Sujana Ghosh 1 ,
• Lindsay R. Stolzenburg 1 ,
• Saleh Tamim 1 ,
• Jozef Lazar 1 ,
• J. Wade Davis 1 &
• Howard J. Jacob 1  

BMC Medical Genomics volume  15 , Article number:  74 ( 2022 ) Cite this article

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The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants.

To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles.

We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33).

Conclusions

This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

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A central goal of genetics is to identify the genetic underpinnings of human diseases. Advancements in human genetics and its related fields and technologies over the past decades have had a remarkable impact on our understanding of human disease pathophysiology, diagnosis and management [ 1 ]. In Mendelian disorders and rare genetic diseases this often takes the form of a loss-of-function mutation or genomic abnormality driving the disease phenotype. There are more than 5,000 diseases that belong to this category accounted for in the Online Mendelian Inheritance in Man (OMIM) database [ 2 ]. For complex diseases, there are multiple genetic and environmental factors contributing to disease risk and the identification of genetic risk factors associated with complex diseases has been rapidly accelerating with the utilization of next generation sequencing and dense array genotyping technologies in genome-wide association studies (GWAS). In a GWAS, thousands of genetic variants are genotyped in individuals which are then used to identify statistical associations between variants at certain genomic loci and a particular phenotype [ 3 ]. Since the first reported GWAS association for age-related macular degeneration [ 4 ] the use of these studies have grown exponentially, with over 200,000 genetic variants associated with more than 3000 human traits reported [ 5 ]. The remarkable growth of GWAS has created a critical need to experimentally identify and validate the disease-associated variants [ 6 , 7 ]. This barrier has hindered the translation of GWAS findings to disease biology mechanisms and hence therapies. There are seemingly very few examples of GWAS-identified genetic loci at which the causal variant and molecular mechanisms driving the association have been experimentally determined, especially considering the sheer number of genotype–phenotype associations that have been reported passing the genome-wide significance threshold.

Dissecting GWAS loci to uncover the underlying biology is a complicated multi-step process. High linkage disequilibrium (LD) between many variants often necessitates utilizing statistical fine-mapping approaches and overlapping with functional genomic annotations for prioritization of variants before experimental validation [ 3 , 8 ]. For coding variants, the target gene is identified directly from the genomic location of the variant [ 9 ]. As protein-coding regions represent only a small percentage of the human genome, more than 90% of GWAS associated variants are annotated to be within non-coding parts of the genome [ 5 ]. Experimental identification and validation of non-coding variants involves additional level of complexity as compared to coding variants requiring the application of additional approaches [ 10 , 11 ]. Moreover, the functionality of regulatory elements is often cell-type specific, which necessitates studying the mechanism in disease-relevant cell types [ 12 ].

Experimental identification and validation are critical elements in translating GWAS findings. To date there has been limited study of the number of GWAS-identified loci that have been experimentally validated. A systematic literature review of 36,676 published articles identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. We additionally curated key information from all included studies such as validated variant class, distance-to-target gene, and experimental validation methods. Our findings have value for future experimental validation studies, target gene prioritization and functional variant prediction. The approaches utilized to validate coding variants as well as current methods used to nominate candidate functional variants for functional studies are outside the scope of this manuscript and have been reviewed previously [ 8 , 9 ].

We conducted a systematic literature search and report it in compliance with the standards set forth by the 2020 PRISMA statement on the reporting of systematic reviews [ 13 ]. As a traditional keyword-based search approach would not enable us to thoroughly search for all relevant concepts and combinations, we leveraged natural language processing (NLP) and ontology-based text mining to ensure a systematic identification of relevant validation articles [ 14 , 15 ]. We defined the scope to include studies that perform validation of GWAS associated non-coding variants at least at a molecular level.

In order to build a comprehensive literature search strategy, we first identified 28 validation studies from recent reviews and published resources [ 6 , 7 , 16 ]. These index studies were evaluated to identify the optimal keywords and concepts that would be used in the systematic literature search. Figure  1 shows a flow diagram summarizing the systematic literature search approach that was employed. The systematic literature search was conducted using search and filter concepts identified by thorough manual and text mining-supported concept analysis of index articles. The initial broad search was based on four different sub-queries aimed at identifying any articles that might include experimental validation of GWAS variants. We included explicit mention of GWAS, non-coding, functional or causal variant as well as contextual mentions of non-coding concepts such as enhancers and promoters (Additional file 1 ). Queries were run on MEDLINE Full Index [ 17 ] (all MEDLINE content until February 19, 2021) using IQVIA/Linguamatics I2E KNIME nodes [ 18 ]. Concepts and various combinations were searched in title, abstract and meta-data (author keywords, Medical Subject Headings (MeSH) terms and substances) leveraging public standard life science ontologies (such as MeSH [ 19 ], NCI Thesaurus [ 20 ] or Entrez Gene [ 21 ], custom vocabularies and syntactical rules, grammatical pattern and linguistic entity classes allowing to build more generalized (comprehensive) queries, but at the same time more precise queries than standard key word search engines. The PMIDs identified by each query were combined and filtered for publication year ≥ 2007 (using “PubMed Publication Data (entrez)”). After removing duplicates, we arrived at 36,676 unique articles (Fig.  1 A). We built seven filters reflecting our key inclusion criteria to narrow down the search results: (1) filter for primary research articles and exclude other article types, (2) GWAS and/or association filter, (3) filter for any human disease, (4) filter for any human gene (RefSeq), (5) filter for explicit mention of “non-coding” or non-coding context (enhancers, intron, non-coding, microRNA, etc.), (6) filter for functional, causal, or regulatory variant or specific rsID, and (7) wet-lab experimental validation techniques (Fig.  1 B, Additional file 2 ). Filters were built using an in-house entity extraction and literature classification pipeline combining SciBite’s TERMite (TERM identification, tagging & extraction) API coupled with SciBite’s VOCabs [ 22 ] and IQVIA/Linguamatics I2E Software.

Systematic literature search and validation approach. Flow diagram demonstrating the systematic literature search strategy starting with A  broad Medline search including all potentially related articles. The search included several concepts related to GWAS, non-coding contexts and other related terms detailed in Additional file 1 . B Using text-mining of article titles, abstracts and metadata, we built seven filters to narrow down the search results which excluded 35,222 articles. Exact search terms and their combinations used in the filters are provided in Additional file 2 . C 1454 articles of interest that passed all the filters were manually screened and evaluated for eligibility. D Through manual curation an additional set of 579 articles was excluded. E 875 eligible articles that passed manual curation were annotated to identify key information from each study. F These articles proceeded to cross-referencing against the GWAS Catalog to ensure that the validated variants and their reported associated disease trait match known GWAS associations. G Cross-referencing excluded 598 articles with poor GWAS trait matches or no variant match. H The final systematic review includes 286 articles. Reasons for exclusion at each stage are shown in red on the right side and described in more detail in the main text

In total 1454 articles passed all filter criteria and were then manually reviewed by three curators (Fig.  1 C). All articles had to meet the following criteria to be considered for inclusion: (1) investigate variants associated with a human disease, (2) include experimental wet-lab molecular validation of one or more variants, (3) include putative validation of at least one non-coding variant, and (4) investigate single nucleotide polymorphisms (SNPs), excluding indels, purely coding, somatic, or rare variants. Abstracts and full texts were reviewed resulting in the exclusion of 579 articles (Fig.  1 D). Overall, this manual review identified 875 potentially relevant articles. All these articles were manually curated to confirm the rsID of the reportedly validated variants, variant class, the reported regulated gene, and the associated disease (Fig.  1 E).

We then used the information on the validated variant’s rsID and disease trait to cross validate our data with the GWAS Catalog [ 5 ] (accessed Mar 25, 2021) to confirm that each curated variant-disease association is reported in a GWAS (Fig.  1 F). Corresponding associations were identified through LD between the curated SNP and the reported GWAS Catalog SNP, and similarity between the reported GWAS trait and the traits extracted from the PubMed abstract as detailed below. Because the GWAS Catalog only reports the lead variant for each locus, and this variant is not necessarily identical to the causal variant for the association, we performed an LD expansion from each top SNP to identify additional possible causal variants. Broad ancestry as reported in the GWAS Catalog was mapped to a 1000 Genomes superpopulation following methods we described recently [ 23 ]. For each associated SNP in the GWAS Catalog, an LD expansion was performed to identify SNPs within 1 Mb with LD r 2  ≥ 0.5 in the corresponding 1000 Genomes super-population. A minor allele count threshold of 5 within the corresponding superpopulation was applied to reduce the impact of high variance LD estimates for rare variants. If it was not possible to map to a single superpopulation, LD expansion was performed using the full 1000 Genomes Phase 3 GRCh38 liftover to match the build used in the GWAS Catalog [ 24 ]. When the GWAS Catalog reported a specific risk allele, our LD expansion took this into account, such that for multiallelic SNPs we would only identify variants correlated with the reported allele. The choice of LD threshold is motivated by the goal to capture GWAS associations that could plausibly be explained by the cataloged variant and has been used elsewhere[ 25 ]. Using this methodology, it was possible to perform LD expansion for 91% of variants in the GWAS Catalog. GWAS Catalog variants for which an LD expansion was not possible were still included in the analysis but could only be matched to the reported variant rather than other possible causal variants.

GWAS Catalog Experimental Factor Ontology (EFO) terms and disease terms curated from the literature were mapped to the 2020 MeSH thesaurus vocabulary using the approach outlined previously [ 26 ]. To allow for inexact matches in MeSH terms (e.g., hypertension and systolic blood pressure), we use two similarity metrics: Lin-Resnik average similarity with a cutoff value of 0.75 [ 26 , 27 ] and odds ratio of MeSH term co-occurrence in the same PubMed article with a cutoff of 20 [ 23 ]. We count a match between an article identified in our systematic review and a GWAS study if any GWAS Catalog association satisfies the following criteria: (1) The reported variant in the GWAS Catalog has LD R 2  ≥ 0.5 to at least one curated variant, and (2) the reported trait in the GWAS Catalog has similarity to a main or manually curated disease from the PubMed abstract, meeting or exceeding the cutoff value. We excluded 347 SNPs in 311 articles from the analysis due to not being linked to a GWAS Catalog SNP. A further 292 SNPs contained within 278 articles were excluded due to a poor match between the reported GWAS trait and the trait reported in the abstract (Fig.  1 G). The final curated catalog includes 286 articles (Fig.  1 H) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 , 300 , 301 , 302 , 303 , 304 , 305 , 306 , 307 , 308 , 309 , 310 , 311 , 312 , 313 ].

Curated catalog of 309 validated GWAS non-coding variants

Several prior studies have emphasized the importance of experimental validations to uncover the biological processes underlying the statistical GWAS associations [ 3 , 6 , 7 , 314 , 315 ]. The final list of 286 articles reports 309 experimentally validated functional non-coding variants regulating 252 genes across 130 human-diseases (Additional file 3 and Fig.  2 ). Additional File 3 includes several important aspects about the included articles and variants including PubMed identifiers (PMID), variant rsID, location, class, target gene as well as disease associations and experimental validation approaches. We examined several characteristics of the validated non-coding variants in relation to GWAS catalog studies and variants. Between 2007 and 2020 there is a steady increase in the number of validation articles over time up to the 286 we report here. In contrast, the total number of published GWAS articles is 4342 versus 286 validation articles for non-coding variants (Fig.  3 A). Next, we evaluated the relationship between disease heritability explained by common SNPs and the ratio of validated variants to the total number of lead-GWAS variants. We mapped disease associations for all variants to the higher order disease categories in the MeSH terms tree structure. For heritability estimates, we considered liability scale h 2 for UK Biobank phenotypes estimated using LD Score Regression[ 316 , 317 ] which (1) mapped to a MeSH disease (2) were considered high or medium confidence and averaged the heritability across higher level MeSH to get average heritability per disease category. Using this approach, we find a statistically significant ( p  = 0.01; correlation coefficient 0.51) positive relationship between mean heritability and the ratio of validated/lead GWAS variants per disease category (Fig.  3 B). Examination of individual validated variants showed the majority of validated variants are in strong LD with and in close proximity to the GWAS variant (Fig.  3 C, D ). Allele frequencies of validated variants have slightly skewed distribution with fewer validated variants having lower allele frequencies (Fig.  3 E). Comparing the location of experimentally validated non-coding GWAS variants to GWAS lead variants, we found that validated variants are about equally likely to be located within a protein-coding gene (58% for functional variants versus 55% for GWAS lead variants). However, they are much more likely to be within 10 kb of a gene boundary (20% versus 11%) and much less likely to be more than 100 kb from the nearest gene (7% versus 16%) (Fig.  3 F). Overall, these findings quantify the persistent need for more experimental validation studies to bridge the gap between association and biology. These findings also suggest that focusing experimental validation efforts to variants in close proximity and strong LD to the lead GWAS variant would lead to the identification of a causal variant in the majority of genetic loci.

Map of 309 validated GWAS non-coding variants. The Circos plot displays the 309 experimentally validated variants studied within the 286 included articles. The outer most layer (i) shows the validated variants’ 252 target genes, (ii) the chromosomal map, (iii) the location of validated variants marked by their rsIDs, (iv) using higher order ontology mapping, we display inner links between variants associated with diseases in the same category. Disease systems that contain ten or more validated variants are displayed while those contain less than ten validated variants are grouped in “Others” category, and (v) the manually annotated validated variant class. Additional File 3 contains all variant details and annotations

Functional validation remains the bottleneck of GWAS follow-up. A Comparison of the number of published studies in the GWAS catalog and non-coding variant validation studies over time. B Relationship between the ratio of validated non-coding variants to the total GWAS variants and disease category mean heritability. C Linkage disequilibrium between reported variant in GWAS Catalog and validated variants. D Distance between validated variant and GWAS Catalog-reported variant. E Global minor allele frequency (MAF) of validated variants in 1000 genomes phase 3. F Location of experimentally validated non-coding GWAS variants in relation to all protein-coding genes compared to GWAS lead variants

Validated variants regulate 252 target genes through a variety of mechanisms

Non-coding genetic variants can exert their effect on target genes through a variety of mechanisms [ 318 , 319 , 320 ]. We divided variants into three broad categories based on their mechanism of regulation: cis-regulatory element (CRE) variants, promoter variants and variants acting through non-coding RNAs (Fig.  4 A). Promoter variants were grouped separately from other CREs because they are functionally distinct and in addition the methods utilized for their validation are different from other CREs. Below we highlight several exemplar studies validating variants across all these mechanisms and many diseases. Interestingly, the majority of non-coding variants identified in our catalog regulate genes through CREs (n = 215). These include variants in enhancers such as rs4420550- MAPK3-TAOK2 in schizophrenia [ 168 ], rs11236797- LRRC32 in inflammatory bowel disease [ 40 ], and rs9349379- EDN1 in vascular diseases [ 49 ]. Some variants exerted their effect through silencers such as rs12038474- CDC42 in endometriosis [ 130 ], rs2494737- AKT1 in endometrial carcinoma [ 37 ] and rs9508032- FLT1 in acute respiratory distress syndrome[ 267 ]. Additionally, rs12936231- GSDMB-ORMDL3-ZPBP2 seems to function through an insulator in an asthma and autoimmune disease risk locus [ 71 ].

Non-coding variants regulate 252 target genes through diverse mechanisms. A Illustration of some of the diverse mechanisms of regulation within each variant category. Examples of each mechanism from included studies are discussed in the text. B Cumulative number of validated variants grouped by non-coding variant categories over time. C We used Encode’s Biomart and hg38 to calculate the distance (in kb) between validated variants and their target gene’s closest transcription start site (TSS). Graph plots the number of variant- gene pairs grouped by variant class. Variants more than 200 kb away are plotted at 200 kb. D Distribution of CRE variants relative to their target gene. CRE = Cis-Regulatory Element, ncRNA = non-coding RNA

Variants in gene promoters can alter transcription factor binding and promoter activity. For example, rs1887428- JAK2 in inflammatory bowel disease [ 256 ], rs11789015- BARX1 in esophageal adenocarcinoma [ 88 ], rs4065275- ORMDL3 and rs8076131- ORMDL3 in asthma, [ 248 ] and rs11603334- ARAP1 in type 2 diabetes mellitus [ 34 ]. DNA methylation is an important epigenetic mechanism of gene regulation and increased DNA methylation at gene promoters can repress gene transcription [ 321 , 322 ]. We identified several validated variants that appear to alter promoter methylation including rs780093- NRBP1 in gout [ 127 ], rs143383- GDF5 in osteoarthritis [ 119 ], and rs35705950- MUC5B in idiopathic pulmonary fibrosis [ 258 ]. Alternatively, variants could alter promoter and transcription start site usage. Examples for these mechanisms in our catalog include rs922483 -BLK in systemic lupus erythematosus [ 302 ] and rs10465885- GJA5 in atrial fibrillation [ 32 ].

The third broad category by which variants from our catalog exert their regulatory effect is through non-coding RNAs [ 323 ]. microRNAs are a major and well-studied class of regulatory small non-coding RNAs. Variants in microRNAs are known to impact disease biology through post-transcriptional regulation of their target genes, primarily via 3’ untranslated region (UTR) binding [ 324 , 325 , 326 ]. GWAS variants located within microRNAs can alter their biogenesis, expression levels and/or target specificity, while variants located in target genes are capable of altering microRNA binding sites [ 326 ]. Examples of validated variants within microRNAs included in this catalog are miR-196a2 variant rs11614913 regulating SFMBT1 and HOXC8 in metabolic syndrome [ 277 ], and miR-4513 variant rs2168518 regulating GOSR2 in cardiometabolic diseases [ 51 ]. Given that microRNAs typically target hundreds to thousands of genes, it is very difficult to confidently assign target genes that are mediating the effect of a microRNA variant. On the other hand, studying variants located within mircoRNA-binding sites of target genes may yield more success in assigning underlying mechanisms [ 326 , 327 ]. There are numerous examples of such variants reported in this catalog, such as rs5068 altering regulation of NPPA by miR-425 in hypertension [ 96 ], rs1058205 altering regulation of KLK3 by miR-3162-5p and rs1010 altering regulation of VAMP8 by miR-370 in prostate cancer [ 54 ], and rs372883 altering BACH1 regulation by miR-1257 in pancreatic ductal adenocarcinoma [ 174 ]. Another important class of non-coding RNAs is long non-coding RNAs that are recognized to play an important role in biology and disease [ 328 , 329 ]. Some examples of long non-coding RNA variants in this catalog include rs6983267 in CCAT2 regulating cancer metabolism through allele-specific binding of CPSF7 [ 76 ] and rs2147578 in LAMC2-1 modulating microRNA binding to it in colorectal cancer [ 43 ]. We examined the distribution of these three broad categories of validated variants across publication dates. We observed a steady increase in the validation of promoter variants (n = 70) and variants acting through non-coding RNAs (n = 24) since 2007, but a sharp increase in the number of studies validating CRE variants around 2015. This trend persisted through 2020 to reach a total of 215 variants representing 70% of this catalog (Fig.  4 B). We also characterized the distance between each validated variant and its target gene’s closest transcription start site according to variant category. As expected, promoter variants clustered immediately upstream or downstream of their target’s transcription start site. CRE variants were more widely distributed, but nevertheless, 157 (66%) of these fell within 50 kb from their target gene TSS. A notable example of a distally acting enhancer variant > 50 kb, is the obesity FTO locus variant rs1421085 regulating IRX3 and IRX5, which are 500 kb and 1,163 kb away respectively [ 147 ]. Since the majority of variants acting through non-coding RNAs identified in our catalog were located within 3’ UTRs, this group of variants tended to cluster within 100 kb downstream of gene transcript start sites (Fig.  4 C). The dataset gave us the opportunity to examine the relationship between CRE variants and their target genes (n = 235 CRE variant-target gene pairs). Plotting the distribution of CRE variants based on their location relative to the target gene indicated that 41% of CRE variants are located within their target gene, and an additional 30% are intergenic and their target gene is the closest gene to the variant. 14% of CRE variants were intergenic and their target gene is not the closest gene, and the remaining 15% are located within a different gene than their target gene. (Fig.  4 D). These results are interesting and provide greater support for consideration of same gene and nearby genes as candidate targets for CREs. These findings are also in agreement with recent empirical data [ 330 , 331 ].

Next, using text mining, we extracted and analyzed the experimental methods that were used in each study to validate variants. We broadly classified them under six broad categories covering different types of established validation techniques and related terms: (1) gene expression, including eQTL and molecular assessment of target gene expression and allele specific regulation (n = 272 articles), (2) reporter assays, including luciferase and massively parallel reporter assays (n = 171 articles), (3) transcription factor binding, including chromatin immunoprecipitation and electrophoretic mobility shift assays (n = 175 articles), (4) in vivo or animal models (n = 104 articles), (5) genome editing, including CRISPR and TALEN (n = 96 articles), and (6) chromatin interaction, including chromosome conformation capture (n = 33 articles) [ 11 ]. We examined the number of these approaches that were utilized by the included studies and found that 189 (66%) of all articles utilized three or more approaches (Fig.  5 ). These results demonstrate the multifaceted approach needed for validation of non-coding variants [ 11 ].

Studies utilize multiple avenues in validating non-coding variants. Using text-mining of abstracts and metadata, we examined the utilization of different avenues for non-coding variant validation across 286 included articles. The six broad categories were gene expression, reporter assays, transcription factor binding, in vivo or animal models, genome editing, and chromatin interaction. The intersection size denotes the number of articles that have the combination of validation categories below it. The color denotes the number of avenues used; pink – 6, orange—5, green—4, black—3, blue—2, red—1. The upset plot shows the overlap of the variant validation avenues and the number of articles. The Set size bars on the right reflect the total number of studies that used/employed each of the categories

GWAS have seen a remarkable growth in the past decade. The impact of GWAS on human healthcare is severely limited by the bottle neck of experimental validation of disease-associated variants. Here, we report the first systematic approach to curate all experimental validation studies of non-coding GWAS variants. While there is general recognition that experimental validation of GWAS are seriously lacking [ 7 ], this systematic assessment of (1) the number of published experimentally validated non-coding variants is quantified, (2) cataloged, and (3) methods used in identified studies analyzed.

Using a comprehensive approach, we employed natural-language processing-based text mining, manual curation and GWAS catalog cross validation. We have curated 286 validation studies that include 309 putatively validated variants regulating 252 genes across 130 diseases. We then evaluated several important characteristics of the identified variants and their relation to GWAS lead variants. The ratio of validated non-coding variants to total GWAS lead variants showed a positive correlation to the mean heritability of disease groups. This relationship could indicate greater success in validating variants in diseases with higher heritability perhaps because of greater individual contribution of these variants to the overall disease susceptibility. This could also potentially represent a greater interest of scientists to pursue validation of variants in more heritable diseases and with larger effect sizes, thus leading to greater proportion of variants being validated. However, we do not have enough data to directly address this possibility. We also evaluated the relationship in LD and distance between validated variants and GWAS lead variants. We find that ~ 70% of validated variants fall within 10 kb and r 2  ≥ 0.9 with the lead GWAS variant. On one hand, this could reflect underlying genetics that most validated variants are in strong LD with lead GWAS variants and suggests that more productive research should be limited to SNPs in high LD and closer distance to lead GWAS variants. On the other hand, the status quo might be reflective of prior limits in search space already considered by scientists who performed validation studies, however we do not have data to support this possibility[ 8 ].

Next, we annotated variants into broad classes based on the mechanisms by which these non-coding variants acted. This identified several interesting patterns, such as an increase in the number of variants functioning through cis-regulatory elements over time. One explanation for this increase could be the growing awareness of the importance of these regulatory elements in human biology and disease which has led to the initiation of large projects aimed at identification, annotation and prioritization of non-coding regulatory elements [ 10 , 320 , 332 ]. Additionally, several SNP-enrichment analyses have demonstrated that GWAS variants are significantly enriched in active regulatory regions [ 314 ]. We expect this trend to continue with publications by larger consortia and projects that investigate regulatory elements in different life stages, tissues and biological conditions [ 332 ]. Interestingly, the majority of cis-regulatory element variants that we found appeared to act through transcriptional enhancers. This dominance of enhancer variants over other regulatory elements might be a result of enhancer elements having more clearly defined functions and biochemical markers (i.e., histone modification signatures) [ 333 , 334 ]. This highlights the potential for increased discovery of GWAS variants acting through silencers and insulators as our understanding of their distinct biochemical signatures is refined and assayed in disease relevant cell types [ 333 , 335 ].

Our comprehensive search and filter strategy enabled us to identify validated variants across a large number of complex human diseases and those that act through a myriad of mechanisms. Nevertheless, the systematic search was limited to the MEDLINE database. Relevant articles published in journals not indexed in this standard database for biomedical literature will be missing in our data set [ 336 , 337 ]. For quality control and to identify limitations of our search and filter approach, we analyzed the recall of our index studies throughout the entire process (Fig.  1 A–H). It is important to highlight that broadening the initial search to include non-coding contexts and association/locus instead of limiting to explicit mentions of non-coding and GWAS terms ensured identification of relevant studies that we had otherwise missed. A significant number of index articles did not explicitly mention these terms [ 48 , 78 , 134 , 143 , 147 , 171 , 178 , 210 , 230 , 256 , 302 ]. Our final broad search covered 27 out of the 28 index studies which demonstrates good search coverage. Through an iterative process, we narrowed down these results, trying to maximize the recall of index studies while maintaining a manageable number of articles for manual review. We are aware that the implemented stringent criteria bias the search to exclude true validation articles that did not mention any disease, protein or specific experimental validation terms [ 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 ]. Additionally, the tagging of the articles and normalization of concepts for filtering relies on accurate named entity recognition (NER) and ontologies. Even when using highly curated, enriched vocabularies and state-of-the-art NER routines, recall rates of at maximum 80–95% are assumed (depending on entity type). Overall, a total of 19 index studies passed all filtering stages and were included in the final catalog. Finally, the data of our curated catalog is mainly based on the publications’ abstract information. Only in cases where information was missing or unclear in the abstract did we gather data from the full text. Therefore, it is possible that information gathered from the final set of articles may be incomplete. This would have affected the experimental validation techniques analysis in particular, which was based only on abstract mining.

Construction of the catalog using controlled vocabularies for diseases, variants, genes, variant classes, and functional follow up methods is aimed to facilitate use in bioinformatics follow up analyses. We expect this resource to be useful in evaluating the performance of computational fine mapping and target prioritization methods. Quantifying the performance of these methods on real datasets has previously been hindered by a lack of true positive examples. A large dataset of true positive examples would allow researchers to computationally identify features associated with functional variation. Recent efforts to compile such true positive datasets and use them to train target prioritization methods have come with concerns about bias towards coding variation [ 16 ] or are aimed at a specific trait subset such as molecular phenotypes [ 346 ] or immune disease [ 347 ]. We expect this catalog to contribute a large number of much needed examples of functional noncoding variants in human disease and the genes on which they act. Despite this important contribution, bias towards nearby genes and variants to the top GWAS SNP is still a concern for our catalog due to the limited number of variants and genes evaluated in the cataloged studies. To generate an unbiased training set for computational methods, an ideal functional study following up on a GWAS association would consider all credible causal SNPs and their nearby genes, but studies in our catalog typically consider a more limited set of genes and SNPs. For example, eQTL variants may be shared among multiple transcripts [ 348 ], and in this scenario functional studies considering only a single gene could be misleading about the causal gene.

This review is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation. The findings of validated variants relationship to lead GWAS variants as well as to their target genes provide practical insights for future validation studies. Finally, we aim for the catalog to be a useful resource aiding in the development of prediction tools by providing a truth set of experimentally validated variants. Collectively this contributes to the overall effort to bridge the gap between genetic association and function in complex diseases.

Availability of data and materials

The data supporting the conclusions of this article is included within the article (and its additional files).

Abbreviations

Cis-regulatory element

Genome-Wide Association Study

Linkage disequilibrium

Medical subject headings

Preferred reporting items for systematic reviews and meta-analyses

Single nucleotide polymorphism

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Acknowledgements

The authors would like to thank Rainer Winnenburg of AbbVie for his assistance with higher order disease mapping and Mark Reppell of AbbVie for helpful advice pertaining to the GWAS Catalog portion of the analysis and for suggesting heritability datasets.

The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication.

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AA, SW, JL designed the study. SW, JR collected the data. AA, SW, EK, JR, SG, ST, LS analyzed the results. AA, SW, EK, JR, SG, ST, LS, JL, JWD and HJ interpreted results. AA, SW, EK wrote the manuscript. JL, JWD and HJ supervised the project. All authors read and approved the final manuscript.

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AA, SW, EK, JR, SG, ST and HJ are employees of AbbVie. LS, JWD and JL were employees of AbbVie at the time of the study.

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Supplementary Information

Additional file 1.

contains exact search terms and criteria used for creating the initial broad literature search.

Additional file 2

contains exact terms and phrases used to setup the seven filters that were used to narrow down the broad search results.

Additional file 3

contains all the validated variants and their details. The file is formatted to include separate rows for unique PMID-variant-gene triples, therefore variants that regulate multiple genes and variants that have been validated in more than one publication have more than one row in the file.

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Alsheikh, A.J., Wollenhaupt, S., King, E.A. et al. The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases. BMC Med Genomics 15 , 74 (2022). https://doi.org/10.1186/s12920-022-01216-w

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Researchers show genetic variant common among Black Americans contributes to large cardiovascular disease burden

Brigham and Women's Hospital

Researchers at Brigham and Women’s Hospital and Duke University showed that a genetic variant, present in 3-4% of self-identified Black individuals in the U.S., increases the risk for both heart failure and death and contributes to significant decreases in longevity at the population level

A genetic variant carried by 3-4 percent of self-identified Black Americans increases the risk for heart failure and death, contributing to a significant decrease in longevity at the population level, according to a new study led by researchers at Brigham and Women’s Hospital , a founding member of the Mass General Brigham healthcare system, and Duke University School of Medicine . The new research shows that individuals who carry the V142I transthyretin variant are at significantly increased risk for heart failure beginning in their 60s, with an increased risk for death beginning in their 70s. Further, the researchers showed that carriers on average died 2 to 2.5 years earlier than expected. With nearly half a million Black Americans carriers over age 50, the researchers estimate that approximately a million years of life will be lost due to this variant among currently living Black individuals who are in mid-to-late life. Results are published in JAMA .

“We believe these data will inform clinicians and patients regarding risk when these genetic findings are known, either through family screening, medical, or even commercial genetic testing,” said senior author Scott D. Solomon, MD , the Edward D. Frohlich Distinguished Chair, Professor of Medicine at Brigham and Women’s Hospital and Harvard Medical School.  “There are now several potential new therapies for cardiac amyloidosis, and understanding the magnitude of this risk, at the individual and societal level, will help determine which patients might be best suited for novel therapies.”

The V142I variant causes transthyretin, a protein in the blood, to misfold leading to deposits of abnormal amyloid protein in the heart and other parts of the body. In the heart, these deposits cause the muscle to become thick and stiffened, a condition known as cardiac amyloidosis, which can ultimately lead to heart failure. Recently, several therapies have been developed to treat cardiac amyloidosis, including therapies that: prevent the protein from misfolding, reduce the amount of protein, remove the protein, and even a gene-editing therapy that is currently undergoing clinical trials. A better understanding of the epidemiology of V142I and cardiac amyloidosis would help physicians connect patients with the appropriate treatment at the appropriate age, the researchers say.

Although the association between the V142I variant and heart failure has been previously described, precise estimates of how the variant increases risk were unclear until now. Considering approximately 48 million Americans self-identify as Black, 1.5 million across the lifespan are estimated to carry this variant. However, since effects of the variant aren’t typically seen until after age 50, the researchers focused on the risk among Black Americans in mid-to-late life.

To uncover these details, the researchers pooled data from self-reported Black participants in four NIH-funded studies in the United States (ARIC, MESA, REGARDS and Women’s Health Initiative). Altogether, the team examined data from 23,338 self-reported Black individuals, 754 (3.23 percent) of whom carried the V142I genetic variant.

They showed that V142I increased the risk for heart failure hospitalization by age 63 and the risk of death by age 72. The variant’s contribution to heart failure risk increased substantially with age but was not itself increased by other known risk factors such as diabetes and hypertension. The team also showed that female and male carriers of the variant were equally at risk, contrary to some previous studies showing that men were more affected. This suggests that women are likely underdiagnosed with the condition. The researchers estimated that individual carriers with the V142I variant live 2-2.5 years less than expected.

“Since 3-4 percent of self-identified Black individuals in the United States carry this variant, a significant number are at elevated risk for developing cardiac amyloidosis, being hospitalized for heart failure, and dying several years earlier than expected,” said first author Senthil Selvaraj, MD, an advanced heart failure physician-scientist at Duke University School of Medicine . “With our improved understanding of the risks with the variant, future efforts to increase disease awareness and ultimately connect carriers with the disease to effective therapies will be important.”

In future studies, the researchers plan to investigate why some, but not all, carriers of the V142I variant develop cardiac amyloidosis. They are also actively involved in developing and testing therapies for the disease, including the gene therapy mentioned above.

“One of the areas that will be really important going forward will be whether we can actually prevent the onset of the disease if we identify these patients earlier,” said Solomon.

Authorship:   Additional Brigham authors include Brian Claggett, and JoAnn E. Manson. Other authors include Robert J. Mentz, Svati H. Shah, Michel G. Khouri, Ani W. Manichaikul, Sadiya S. Khan, Stephen S. Rich, Thomas H. Mosley, Emily B. Levitan, Pankaj Arora, Parag Goyal, Bernhard Haring, Charles B. Eaton, Richard K. Cheng, Gretchen L. Wells, and Marianna Fontana.

Disclosures: Selvaraj receives research support from the National Heart, Lung, and Blood Institute (K23HL161348), Doris Duke Charitable Foundation (#2020061), American Heart Association (#935275), the Mandel Foundation, Duke Heart Center Leadership Council, the Institute for Translational Medicine and Therapeutics, and Foundation for Sarcoidosis Research. He has participated in advisory boards for AstraZeneca. Solomon has received research grants from Alexion, Alnylam, AstraZeneca, Bellerophon, Bayer, BMS, Cytokinetics, Eidos, Gossamer, GSK, Ionis, Lilly, MyoKardia, NIH/NHLBI, Novartis, NovoNordisk, Respicardia, Sanofi Pasteur, Theracos, US2.AI and has consulted for Abbott, Action, Akros, Alexion, Alnylam, Amgen, Arena, AstraZeneca, Bayer, Boeringer-Ingelheim, BMS, Cardior, Cardurion, Corvia, Cytokinetics, Daiichi-Sankyo, GSK, Lilly, Merck, Myokardia, Novartis, Roche, Theracos, Quantum Genomics, Cardurion, Janssen, Cardiac Dimensions, Tenaya, Sanofi-Pasteur, Dinaqor, Tremeau, CellProThera, Moderna, American Regent, Sarepta, Lexicon, Anacardio, Akros, Valo. Additional author disclosures can be found in the paper. Funding: Funding for the cohorts was provided by the National Institutes of Health.

Paper cited: Selvaraj, S et al. “Cardiovascular Burden of the V1421 Transthyretin Variant” JAMA DOI: 10.1001/jama.2024.4467

10.1001/jama.2024.4467

Article Title

Selvaraj, S et al. “Cardiovascular Burden of the V1421 Transthyretin Variant” JAMA DOI: 10.1001/jama.2024.4467

Article Publication Date

12-May-2024

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Study Suggests Genetics as a Cause, Not Just a Risk, for Some Alzheimer’s

People with two copies of the gene variant APOE4 are almost certain to get Alzheimer’s, say researchers, who proposed a framework under which such patients could be diagnosed years before symptoms.

By Pam Belluck

Scientists are proposing a new way of understanding the genetics of Alzheimer’s that would mean that up to a fifth of patients would be considered to have a genetically caused form of the disease.

Currently, the vast majority of Alzheimer’s cases do not have a clearly identified cause. The new designation, proposed in a study published Monday, could broaden the scope of efforts to develop treatments, including gene therapy, and affect the design of clinical trials.

It could also mean that hundreds of thousands of people in the United States alone could, if they chose, receive a diagnosis of Alzheimer’s before developing any symptoms of cognitive decline, although there currently are no treatments for people at that stage.

The new classification would make this type of Alzheimer’s one of the most common genetic disorders in the world, medical experts said.

“This reconceptualization that we’re proposing affects not a small minority of people,” said Dr. Juan Fortea, an author of the study and the director of the Sant Pau Memory Unit in Barcelona, Spain. “Sometimes we say that we don’t know the cause of Alzheimer’s disease,” but, he said, this would mean that about 15 to 20 percent of cases “can be tracked back to a cause, and the cause is in the genes.”

The idea involves a gene variant called APOE4. Scientists have long known that inheriting one copy of the variant increases the risk of developing Alzheimer’s, and that people with two copies, inherited from each parent, have vastly increased risk.

The new study , published in the journal Nature Medicine, analyzed data from over 500 people with two copies of APOE4, a significantly larger pool than in previous studies. The researchers found that almost all of those patients developed the biological pathology of Alzheimer’s, and the authors say that two copies of APOE4 should now be considered a cause of Alzheimer’s — not simply a risk factor.

The patients also developed Alzheimer’s pathology relatively young, the study found. By age 55, over 95 percent had biological markers associated with the disease. By 65, almost all had abnormal levels of a protein called amyloid that forms plaques in the brain, a hallmark of Alzheimer’s. And many started developing symptoms of cognitive decline at age 65, younger than most people without the APOE4 variant.

“The critical thing is that these individuals are often symptomatic 10 years earlier than other forms of Alzheimer’s disease,” said Dr. Reisa Sperling, a neurologist at Mass General Brigham in Boston and an author of the study.

She added, “By the time they are picked up and clinically diagnosed, because they’re often younger, they have more pathology.”

People with two copies, known as APOE4 homozygotes, make up 2 to 3 percent of the general population, but are an estimated 15 to 20 percent of people with Alzheimer’s dementia, experts said. People with one copy make up about 15 to 25 percent of the general population, and about 50 percent of Alzheimer’s dementia patients.

The most common variant is called APOE3, which seems to have a neutral effect on Alzheimer’s risk. About 75 percent of the general population has one copy of APOE3, and more than half of the general population has two copies.

Alzheimer’s experts not involved in the study said classifying the two-copy condition as genetically determined Alzheimer’s could have significant implications, including encouraging drug development beyond the field’s recent major focus on treatments that target and reduce amyloid.

Dr. Samuel Gandy, an Alzheimer’s researcher at Mount Sinai in New York, who was not involved in the study, said that patients with two copies of APOE4 faced much higher safety risks from anti-amyloid drugs.

When the Food and Drug Administration approved the anti-amyloid drug Leqembi last year, it required a black-box warning on the label saying that the medication can cause “serious and life-threatening events” such as swelling and bleeding in the brain, especially for people with two copies of APOE4. Some treatment centers decided not to offer Leqembi, an intravenous infusion, to such patients.

Dr. Gandy and other experts said that classifying these patients as having a distinct genetic form of Alzheimer’s would galvanize interest in developing drugs that are safe and effective for them and add urgency to current efforts to prevent cognitive decline in people who do not yet have symptoms.

“Rather than say we have nothing for you, let’s look for a trial,” Dr. Gandy said, adding that such patients should be included in trials at younger ages, given how early their pathology starts.

Besides trying to develop drugs, some researchers are exploring gene editing to transform APOE4 into a variant called APOE2, which appears to protect against Alzheimer’s. Another gene-therapy approach being studied involves injecting APOE2 into patients’ brains.

The new study had some limitations, including a lack of diversity that might make the findings less generalizable. Most patients in the study had European ancestry. While two copies of APOE4 also greatly increase Alzheimer’s risk in other ethnicities, the risk levels differ, said Dr. Michael Greicius, a neurologist at Stanford University School of Medicine who was not involved in the research.

“One important argument against their interpretation is that the risk of Alzheimer’s disease in APOE4 homozygotes varies substantially across different genetic ancestries,” said Dr. Greicius, who cowrote a study that found that white people with two copies of APOE4 had 13 times the risk of white people with two copies of APOE3, while Black people with two copies of APOE4 had 6.5 times the risk of Black people with two copies of APOE3.

“This has critical implications when counseling patients about their ancestry-informed genetic risk for Alzheimer’s disease,” he said, “and it also speaks to some yet-to-be-discovered genetics and biology that presumably drive this massive difference in risk.”

Under the current genetic understanding of Alzheimer’s, less than 2 percent of cases are considered genetically caused. Some of those patients inherited a mutation in one of three genes and can develop symptoms as early as their 30s or 40s. Others are people with Down syndrome, who have three copies of a chromosome containing a protein that often leads to what is called Down syndrome-associated Alzheimer’s disease .

Dr. Sperling said the genetic alterations in those cases are believed to fuel buildup of amyloid, while APOE4 is believed to interfere with clearing amyloid buildup.

Under the researchers’ proposal, having one copy of APOE4 would continue to be considered a risk factor, not enough to cause Alzheimer’s, Dr. Fortea said. It is unusual for diseases to follow that genetic pattern, called “semidominance,” with two copies of a variant causing the disease, but one copy only increasing risk, experts said.

The new recommendation will prompt questions about whether people should get tested to determine if they have the APOE4 variant.

Dr. Greicius said that until there were treatments for people with two copies of APOE4 or trials of therapies to prevent them from developing dementia, “My recommendation is if you don’t have symptoms, you should definitely not figure out your APOE status.”

He added, “It will only cause grief at this point.”

Finding ways to help these patients cannot come soon enough, Dr. Sperling said, adding, “These individuals are desperate, they’ve seen it in both of their parents often and really need therapies.”

Pam Belluck is a health and science reporter, covering a range of subjects, including reproductive health, long Covid, brain science, neurological disorders, mental health and genetics. More about Pam Belluck

The Fight Against Alzheimer’s Disease

Alzheimer’s is the most common form of dementia, but much remains unknown about this daunting disease..

How is Alzheimer’s diagnosed? What causes Alzheimer’s? We answered some common questions .

A study suggests that genetics can be a cause of Alzheimer’s , not just a risk, raising the prospect of diagnosis years before symptoms appear.

Determining whether someone has Alzheimer’s usually requires an extended diagnostic process . But new criteria could lead to a diagnosis on the basis of a simple blood test .

The F.D.A. has given full approval to the Alzheimer’s drug Leqembi. Here is what to know about i t.

Alzheimer’s can make communicating difficult. We asked experts for tips on how to talk to someone with the disease .

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SARS-CoV-2 Mutations and their Viral Variants

Begum cosar.

a Başkent University, Faculty of Science and Letters, Department of Molecular Biology and Genetics, Ankara, Turkey

Zeynep Yagmur Karagulleoglu

b Yıldız Technical University, Faculty of Arts and Science, Department of Molecular Biology and Genetics, İstanbul, Turkey

Ahmet Turan Ince

c Sivas Cumhuriyet University, Faculty of Medicine, Sivas, Turkey

Dilruba Beyza Uncuoglu

d Ankara University, Graduate School of Natural and Applied Sciences, Department of Biology, Ankara, Turkey

Gizem Tuncer

e Hacettepe University, Graduate School of Science and Engineering, General Biology Program, Ankara, Turkey

f HücreCELL Biotechnology Development and Commerce, Inc., Ankara, Turkey

Bugrahan Regaip Kilinc

g Kastamonu University, School of Engineering and Architecture, Department of Genetics and Bioengineering, Kastamonu, Turkey

h Kastamonu University, School of Engineering and Architecture, Department of Biomedical Engineering, Kastamonu, Turkey

Yunus Emre Ozkan

i Gebze Technical University, Faculty of Science, Department of Molecular Biology and Genetics, Kocaeli, Turkey

Hikmet Ceyda Ozkoc

j Akdeniz University, Faculty of Medicine, Department of Medical Pharmacology, Antalya, Turkey

Ibrahim Naki Demir

k Akdeniz University, Faculty of Medicine, Antalya, Turkey

Feyzanur Karagoz

Said yasin simsek, bunyamin yasar.

l Alanya Alaaddin Keykubat University, Department of Molecular Medicine, Antalya, Turkey

Mehmetcan Pala

m Sivas Cumhuriyet University, Faculty of Science, Department of Molecular Biology and Genetics, Sivas, Turkey

Aysegul Demir

n Üsküdar University, Faculty of Engineering and Natural Sciences, Department of Molecular Biology and Genetics, İstanbul, Turkey

Irem Naz Atak

o Ankara University, Faculty of Science, Department of Biology, Ankara, Turkey

Aysegul Hanife Mendi

p Gazi University, Faculty of Dentistry, Department of Basic Sciences, Division of Medical Microbiology, Ankara, Turkey

Vahdi Umut Bengi

q Gülhane Training and Research Hospital, Faculty of Dentistry, Department of Periodontology, Ankara, Turkey

Guldane Cengiz Seval

r Ankara University, School of Medicine Department of Hematology, Cebeci, Ankara, Turkey

Evrim Gunes Altuntas

s Ankara University, Biotechnology Institute, Ankara, Turkey

Pelin Kilic

t Ankara University, Stem Cell Institute, Ankara, Turkey

Devrim Demir-Dora

u Akdeniz University, Faculty of Medicine, Department of Medical Pharmacology, Antalya, Turkey

v Akdeniz University, Health Sciences Institute, Department of Gene and Cell Therapy, Antalya, Turkey

w Akdeniz University, Health Sciences Institute, Department of Medical Biotechnology, Antalya, Turkey

Mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occur spontaneously during replication. Thousands of mutations have accumulated and continue to since the emergence of the virus. As novel mutations continue appearing at the scene, naturally, new variants are increasingly observed.

Since the first occurrence of the SARS-CoV-2 infection, a wide variety of drug compounds affecting the binding sites of the virus have begun to be studied. As the drug and vaccine trials are continuing, it is of utmost importance to take into consideration the SARS-CoV-2 mutations and their respective frequencies since these data could lead the way to multi-drug combinations. The lack of effective therapeutic and preventive strategies against human coronaviruses (hCoVs) necessitates research that is of interest to the clinical applications.

The reason why the mutations in glycoprotein S lead to vaccine escape is related to the location of the mutation and the affinity of the protein. At the same time, it can be said that variations should occur in areas such as the receptor-binding domain (RBD), and vaccines and antiviral drugs should be formulated by targeting more than one viral protein.

In this review, a literature survey in the scope of the increasing SARS-CoV-2 mutations and the viral variations is conducted. In the light of current knowledge, the various disguises of the mutant SARS-CoV-2 forms and their apparent differences from the original strain are examined as they could possibly aid in finding the most appropriate therapeutic approaches.

1. Background information

Mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occur spontaneously during replication. Thousands of cumulative mutations have occurred since the emergence of the virus [ 1 ]. As novel mutations continue to emerge, naturally, new mutants are increasingly observed. Most of the mutations that occur in the SARS-CoV-2 genome have no notable effect on the spread and the virulence of the virus, and hence on the course of the disease [ 2 ]. The greatest concern about such emerging mutations is a risky change that could lead to an increase in the severity of the infection or a failure on the effects of vaccines currently being developed. This is mainly because the viral signals may escape the immune protection which originate from a preceding infection or vaccination [ 3 ]. The first occurrence of any mutation is difficult to correlate with the continuity of the alterations. Understanding the significance of the alterations may be possible through experimental studies, by showing a link between the mutation in question and a subtle change in viral biology. However, testing the effect of thousands of mutants takes considerable time and effort.

As the case with other CoVs, the SARS-CoV-2 genome contains at least 23 open reading frames (ORFs) [ 4 ]. The SARS-CoV-2 genome contains ORFs that are responsible for the production of non-structural proteins (Nsps) [ 5 ]. ORFs encode at least 4 main structural proteins: the spike (S), membrane (M), envelope (E), and nucleocapsid (N) proteins [ 6 ]. Among these, the most notable mutations are those in the gene encoding the S protein, which is associated with viral entry into the cells. There are currently around 4000 mutations in the S protein gene. There are a few mutations in the region called the receptor-binding motifs (RBMs) of the S protein, the region responsible for viral entry through its interaction with the human angiotensin-converting enzyme 2 (hACE2) receptor on the host cells [ 7 ].

In our review, we conducted a literature survey under the scope of the exponentially increasing SARS-CoV-2 mutations and the numerous viral variations as the outcome. In the light of current knowledge, we aim to elaborate SARS-CoV-2′s ever changing disguises into novel mutant forms in various locations around the world, to analyze what features of such upcoming mutants differ from its original manifestation, and to emphasize the apparent discrepancies, which may be able to, in return, possibly aid in finding solutions for developing novel therapeutic approaches.

2. An overview of SARS-CoV-2

CoVs are a group of infectious pathogens that cause a wide range of clinical conditions such as respiratory, enteric, hepatic and neurological diseases. Highly pathogenic human CoVs belong to the Coronaviridae family. CoVs are divided into four genera: alpha-CoV, beta-CoV, gamma-CoV and delta-CoV. As well-known today, SARS-CoV-2 is an RNA coronavirus responsible for the coronavirus disease 2019 (COVID-19) outbreak. Proven to be the novel pathogen of COVID-19, SARS-CoV-2 belongs to the beta-CoV genus, a linear, single-stranded RNA genome of approximately 30 kb, and the Sarbecovirus sub-gene, as seen in Table 1 [ 14 ].

Comparison of SARS-CoV, MERS-CoV and other human coronaviruses (hCoVs) by species, genome, genome length and percentage (%) similarity to the SARS-CoV-2 genome.

CoVs are enveloped viruses with positive sense RNA genomes with a single cistern of approximately 26−32 kb, which have the largest known genome size for an RNA virus. Seven CoVs – i.e., GC-V-229E, Human CoV-NL63 (hCoV-NL63), human CoV-OC43 (hCoV-OC43), human CoV-HKU1 (hCoV-HKU1), SARS-CoV, Middle East respiratory syndrome CoV (MERS-CoV), SARS-CoV-2 – have infected humans to date [ 15 ] ( Table 1 ). The estimated mutation rates of CoVs are moderate or high compared to other single-stranded positive-sense RNA (+ssRNA) viruses. The antigenic surface of SARS-CoV-2 is quite different compared to other CoVs. Both the SARS-CoV-2 and the SARS infections have many common features. Both cause respiratory diseases. They are transmitted from animals to humans as an intermediate host. Both airborne and can be transmitted via respiratory fluids, which are fine droplets released during respiration from an infected person [ 16 ]. People with the SARS-CoV-2 infection tend to transmit more rapidly than those with the SARS infection ( Table 2 ).

Percentage (%) of sequential similarity of SARS-CoV, MERS-CoV, HCoV-HKU1 and HCoV-OC43 proteins with SARS-CoV-2 proteins.

SARS-CoV had emerged as a major cause of severe lower respiratory tract infection in humans in 2002. In some studies conducted at that time, new strains and the possibility of future outbreaks were mentioned [ 22 , 23 ]. The severe and sudden symptoms resulting in atypical pneumonia with dry cough and persistent high fever in severe cases of the acute respiratory virus have revealed the importance of CoVs as potentially lethal human pathogens, and the identification of several zoonotic reservoirs has reappeared.

SARS-CoV-2 is the seventh CoV known to infect humans [ 24 ]. The world experienced its first international health emergency in the 21st century with the disease called SARS, in 2003. SARS had first started in China and soon spread to Asia, North America and Europe, causing 800 deaths in approximately 30 countries. Similarly, cases of pneumonia of unknown etiology were reported on December 31, 2019 in Wuhan, Hubei Province, China. It was identified on January 7, 2020, that the disease agent was an unprecedented CoV (2019-nCoV) in humans.

2.1. SARS-CoV-2 structural properties and the replication cycle

SARS-CoV-2 has typical features among the CoV family, belongs to the beta-CoV 2b group and is an enveloped +ssRNA virus [ 25 ]. SARS-CoV-2 encodes the basic structural proteins of S, M, E and N, as seen in Fig. 1 . Also as observed in Table 1 , the SARS-CoV, MERS-CoV, hCoV-HKU1 and hCoV-OC43 proteins have sequencing similarities with SARS-CoV-2 proteins [ 26 ].

The to-date defined surface protein structure of of SARS-CoV-2 (+ssRNA: single-stranded positive-sense RNA).

+ssRNA viruses, a large group that includes human pathogens such as SARS-CoV, replicate in the cytoplasm of the infected host cells. Replication complexes are generally associated with modified host cell membranes [ 27 ]. The SARS-CoV replication is driven by the membrane-bound viral enzyme complex. This complex is often linked to modified intracellular membranes. CoVs and other members of the Nidovirus family have a polycistronic genome, and use a variety of transcriptional and (post-) translational mechanisms to regulate their expression [ 28 , 29 ]. Post-translational modifications are covalent modifications of proteins after they are translated by ribosomes. It identifies new functional groups such as phosphate and carbohydrates, expands the chemical repertoire of 20 standard amino acids through post-translational modifications, and plays important roles in regulating the folding, stability, enzymatic activity, subcellular localization and interaction of a protein with other proteins [ 30 ]. Viruses that maintain compulsory cell life receive support from the protein synthesis mechanisms of the host cells after respiration. For this reason, after the polypeptides are synthesized, they modify protein functions by creating covalent modifications [ 31 ]. The gene encoding the replicase/transcriptase (this gene is commonly referred to as "replicase"), contains nearly two-thirds of the CoV genome, the largest known RNA genome to date. The replicase gene consists of ORFs 1a and 1b. ORF1b is expressed by a ribosomal frameshift near the 3′-terminal of the ORF1a. Thus, the SARS-CoV genome translation yields two polyproteins (pp1a and pp1ab) that are auto-proteolytically cleaved into 16 Nsps by proteases found in Nsp3 and Nsp5 [ 32 , 33 ].

2.2. Entry into the cell

The default gateway, the cellular receptor, or SARS-CoV-2 is angiotensin-converting enzyme 2 (ACE2) [ 34 , 35 ]. Both SARS-CoV-2 and SARS-CoV use hACE2 as the input receptor and human proteases as input activators. The S protein, the leading viral surface protein, mediates the entry of SARS-CoV-2 into the cell. To fulfill the function of SARS-CoV-2, the receptor binds to hACE2 via the receptor-binding domain (RBD) and is proteolytically activated by human proteases. It is thought-provoking that the recombinant hACE2 (rhACE2) significantly reduces viral utilization in human cell-derived organoids [ 36 ], possibly serving as a decoy for virus binding.

Normally, ACE2 acts in regulating blood pressure. However when the CoV binds to ACE2, a series of chemical changes occur, that effectively inter-connect the membranes around the cell and the virus, allowing the RNA of the virus to enter the cell. To enter the host cells, CoVs first bind to a cell surface receptor for viral attachment, then penetrate into the endosomes, and eventually join the viral and lysosomal membranes [ 37 , 38 ].

Protease activators have also been studied for SARS-CoV-2 entry at the receptor level. Both the transmembrane protease serine 2 (TMPRSS2) and lysosomal proteases are important for SARS-CoV-2 entry [ 39 , 40 ]. A successful viral entry requires proteolytic processing of the viral coat glycoprotein S, which is able to be carried out by TMPRSS2. Both camostat and the camostat-related agent nafamostat [ 41 ] block SARS-CoV-2 replication in human cells which express TMPRSS2. CoVs use the endo-lysosomal pathway to enter the cell before reproducing.

The CoV life cycle includes several potentially targetable steps: i) endocytic entry into host cells (via ACE2 and TMPRSS2), ii) RNA replication and transcription (helicase-containing transcription), and RNA-dependent RNA polymerase (RdRp) activation, translation and proteolytic processing of viral proteins, and iii) viron assembly and release of new viruses through exocytic systems [ 42 ] ( Fig. 2 ).

Cell entry of SARS-CoV-2, replication cycle and synthesis of viral components. 1: SARS-CoV-2 binds via the S glycoprotein to the ACE-2 receptor expressed in the host cell. 2. SARS-CoV-2 enters the cell with clathrin-coated pits. 3. The clathrin structures are separated from the main structure. 4. Endosome fusion (with dynein) takes place to release the viral RNA genome. 5. The dynein units are separated from the structure and the endosome begins to open. 6. The opening of the endosome and release of the viral RNA genome. The viral RNA genome is synthesized using host ribosomes, viral polymerase. 7. Genomic and subgenomic RNA synthesis takes place in the synthesis of viral proteins. Then, with the help of ribosomes, viral RNAs are transmitted and viral proteins are synthesized. 8. Viral components come together to form the endosomal structure, then to make up for SARS-CoV-2.

3. Mutations in the spike (S) protein

The entry of SARS-CoV-2 into the cell takes place through the S protein [ 43 ], which has an important role in viral infection and pathogenesis [ 44 ]. The S protein consists of two subdomains: i) S1, and ii) S2. The S1 protein consists of an N-terminal domain (NTD) and a C-terminal domain (CTD) ( Fig. 3 ). These two domains act as RBD and can bind various sugars and proteins [ 45 ]. S1 recognizes and binds to hACE2 receptors. S2 facilitates fusion through conformational changes [ 46 , 47 ]. While the S1 domain varies even among a single CoV species, the S2 domain is the most reserved region of the S protein.

The structure of the SARS-CoV-2 spike (S) protein. (RBD: receptor binding domain; NDT: N-terminal domain; FP: fusion protein; T.A.: transmembrane anchor and I.T.: intracelluar tail).

The S protein found in the SARS-CoV-2 genome is of great importance ACE2 receptor binding and membrane fusion of the virus, and running scientific studies on therapeutic approaches and on the formation of immune response. Therefore, mutations that occur in the S protein, especially the RBD in the S gene, should be thoroughly examined.

There are currently around 4000 mutations in the S protein gene. The well-known mutations are listed in Table 3 .

The molecular location and geographical distribution of mutations in the S gene region.

3.1. Mutations in the receptor-binding domain (RBD) of SARS-CoV-2

The S protein RBD is defined as the critical determinant of viral tropism and infectivity. Therefore, more attention should be paid to whether mutations in the RBD of circulating SARS-CoV-2 strains alter the receptor-binding affinity and cause these strains to be more contagious. RBD mutation analysis provides information about the changes in SARS-CoV-2. The RBD CoV genome in the S protein is the most variable part [ 48 ]. Six RBD amino acids are critical for binding to ACE2 receptors and determining the seven major sequences of the SARS-CoV-like virus. While analyses suggest that SARS-CoV-2 can bind human ACE2 with high-affinity, computational analyses reveal that the interaction is not so ideal and that the RBD sequence differs from those shown to be optimal for receptor binding in SARS-CoV [ 49 ]. Thus, the high-affinity binding of the SARS-CoV-2 S protein to human ACE2 is most likely the result of natural selection on an hACE2 or human-like ACE2, which allows for another emerging optimal solution for binding [ 50 ]. This is strong evidence that SARS-CoV-2 is not a product of targeted manipulation. There are 725 present non-degenerate mutations in the SARS-CoV-2 S protein. Among such, 89 mutations involved in the binding of the SARS-CoV-2 S protein and ACE2 which occurrs in the RBD. Moreover, 52 of the 89 mutations are on the CRBM, the RBD region that is in direct contact with ACE2. Many mutations on RBD such as N439 K, L452R, T478I and E484D are noted to have significant free energy changes. Mutations in the RBM take up 58 % (52 of 89) of all mutations on the RBD, potentially increasing the complexity of antiviral drug and vaccine development. This overall analysis suggests that mutations in the RBD enhance the binding of the S protein and ACE2, leading to the more infectious SARS-CoV-2 [ 2 ]. Based on the up-to-date literature survey performed in this study, we retrieved 28 different S protein variants. Out of these variants, 12 belong to the RBD region, only.

3.2. Important mutations in the RBD and other domains of the S protein

3.2.1. d614g.

The D614G (Asp614-to-Gly)) mutation was first detected in Germany and China in late January 2020 [ 55 ]. It has become a worldwide mutant thereafter [ 56 ]. D614G was determined as the most prominent sequence variation with a rate of 56 % in experiments performed on experimental animals with the SARS-CoV-2 virus isolated in Anatolia [ 57 ]. It was formed by replacing the natural form of Asp614 with Gly in the S protein [ 58 ]. The D614G strain was accompanied by two different mutations. The first was a silent cytosine thymine (CT) mutation in the Nsp3 gene at position 3.037 and the second is a CT mutation of amino acid change at position 14.409 (RdRp P323 L), resulting in an RdRp [ 51 ]. The D614G mutation increased transduction in many cell types, including lung, liver, and colon cells. It is also more resistant to proteolytic cleavage. Accordingly, it is 4–9 times more contagious [ 52 ], however not an escape mutation [ 59 ].

3.2.2. S943P

The S943P mutation was the first to occur in the S protein in Belgium. In Belgium, 23 S943P mutations were found in 284 SARS-CoV-2 S sequences, but not among the remainder of the 6,063 S sequences sampled worldwide from outside of Belgium. As a result, the AGT (S) → CCT (P) mutation emerged [ 60 ]. The S943P mutation is a result of recombination of different viruses in an infected host and has evolved significantly [ 61 , 62 ].

3.2.3. V483a

The V483a mutation was first seen in North America [ 63 ]. V483a occurred in the S1 domain RBM of the S protein found in the virus genome [ 64 ]. This mutation occurs when the hydrophobic alanine replaces the hydrophobic valine, an important amino acid residue in the RBM region of glycoprotein S at position 483, and is caused by the transition from thymine (uracil) to cytosine at the genome position 23010 [ 65 ]. Since the V483a mutation site is not in direct contact with the ACE2 receptor [ 66 ], no significant change was observed without binding to the ACE2 receptor [ 62 ]. The RNA replication rate in the resulting mutant strain causes the virus to mutate in the host, resulting in the mutant strain to have strong drug resistance.

3.2.4. E484K

The E484K mutation, which was first observed in South Africa, is a rapid spread mutation found in the variants of South Africa (B.1.351) [ 67 ] and Brazil (B.1.1.28) [ 68 ]. This mutation in the S protein suggests that the virus is further developing and may become resistant to vaccines [ 69 ].

3.2.5. COH.20G/501Y

The COH.20G/501Y variant has a 20G backbone and was identified in Columbus independent of the 20G variant available in Ohio [ 70 ]. The S N501Y mutation, located within the RBD, is of particular concern for two reasons: i) its increased affinity to ACE2 [ 71 , 72 ], and ii) that it may impact association of receptor binding neutralizing antibodies including those in the Regeneron cocktail [ 71 , 73 ].

3.2.6. L452R

The L452R mutant was first detected in Denmark in March 2020. In California, the mutant prominently spread in Los Angeles. This mutation was found in 45 % of the existing samples in California [ 74 ]. This mutation weakened antibody neutralization and increased the virus's ability to infect [ 75 ].

3.2.7. Q677

The Q677 mutation was first noticed in New Mexico and Louisiana. In some strains, its 677th amino acid glutamine (Q) has been converted to proline (P). This variant is known as Q677P. In other strains, the same amino acid has transformed into histidine (H). This variant is also named Q677H [ 75 ]. This mutation has enabled SARS-CoV-2 to enter the human cells more easily due to its Q location [ 76 ].

3.2.8. P681H

The P681H mutation has been observed worldwide as of December 31, 2020 [ 77 ]. P681H results from a loss of proline and a gain of histidine containing imidazole. It also has mutations that result in cysteine ​​residues. This potentially causes breakdown of the disulfide bridges in and around the RBD [ 77 ]. It is not thought to be associated with increased infection or spread, yet studies are ongoing [ 78 ].

3.2.9. E484Q

The E484Q mutation is caused by the change between glutamic acid (El) and glutamine (Q) at position 484. It causes an increase in ACE2 affinity in the B.1.617 double mutation strain seen in India [ 79 , 80 ].

3.2.10. K417

The K417 spike protein has been observed in several strains, mainly P.1 and B.1.351. This mutation is manifested as K417 N in the B.1.351 strain and as K417 T in the P.1 strain [ 80 , 81 ].

3.2.11. S477G/N

The S477 residue has the highest number of mutations in the RBD. It occurs as a result of amino acid changes at position 477. An increased binding affinity for hACE2 is observed with S477G and S477N, the two most frequently demonstrated mutations of S477 [ 82 ].

4. Some SARS-COV-2 variants recently associated with rapid spread

RNA viruses, one of which is SARS-CoV-2, are defined by a high mutation rate, one million times higher than their host. Viral mutagenic ability depends on several factors, including the quality of viral enzymes that replicate nucleic acids like RdRp. The mutation rate drives viral evolution and genome variability, thus allowing viruses to escape host immunity and hence develop drug resistance [ 83 ].

A number of SARS-CoV-2 variants have emerged worldwide since the COVID-19 outbreak. The fastest-spreading variants recently detected in UK, South Africa and Brazil have been the focus of attention ( Fig. 4 ). Scientists suspect that variants have the potential to affect certain mutation patterns, their infectivity, virulence and/or their ability to escape from parts of the immune system. Second, it could render vaccine-induced or naturally immune humans vulnerable to re-infection with the new variants to SARS-CoV-2, and such possible effects are still under investigation.

Countries with the fastest-spreading variants. B.1.1.7: Denmark, United States of America, France, Spain, Belgium, Netherlands, Italy, Switzerland, Ireland, Turkey, Israel, Portugal, Austria, Sweden, Australia, Finland, Germany, Norway, Nigeria, Slovakia, Ghana, India, Singapore, New Zealand, Jordan, Canada, Romania, Luxembourg, South Korea, Brazil, United Arab Emirates, Iceland, Poland, Czech Republic, Sri Lanka, Northern Macedonia, Saint Lucia, Aruba, Hong Kong, Thailand, Montenegro, Mexico, Ecuador, Bosnia and Herzegovina, Hungary, Latvia, Slovenia, Greece, Guadeloupe, Jamaica, Barbados, Kosovo, Bangladesh, Gambia, Cayman Islands, Republic of Serbia, Malaysia, Democratic Republic of the Congo, Taiwan, Pakistan, Peru, Iran, Argentina, Mayotte, Curaçao, Oman, Senegal, Kuwait, Dominican Republic, Trinidad and Tobago, South Africa, B.1.351: Mayotte, United Kingdom, Belgium, France, Netherlands, Switzerland, Mozambique, Botswana, Zambia, New Zealand, Australia, Austria, Denmark, United States of America, Turkey, Germany, Ireland, Israel, Kenya, Finland, Sweden, United Arab Emirates, Ghana, South Korea, Thailand, Spain, Canada, Portugal, Luxembourg, Singapore, Democratic Republic of the Congo, Italy, Norway, Panama, Bangladesh, P.1: Brazil, Switzerland, Colombia, Italy, Belgium, Japan, France, United States of America, Netherlands, French Guiana, Spain, South Korea, Mexico, Faroe Islands, Peru, B.1.525: Denmark, United Kingdom, Nigeria, United States of America, France, Canada, Ghana, Australia, Netherlands, Jordan, Singapore, Finland, Mayotte, Belgium, Spain. More than one mutant type is seen at once in the blackened countries or regions.

4.1. B.1.1.7, 20I/501Y.V1, VOC202012/01

The B.1.1.7 variant was first seen in UK and began to spread rapidly. After a short time, it was seen in particularly India, the Netherlands, Switzerland, France, Brazil, Finland, Belgium, Mexico, Bangladesh, Turkey, China (Bejing and Wuhan), South Korea, 62 European countries, Asia and UK [ 84 ]. The B.1.1.7 strain N5014, P681H, H69-V70 and Y144/145 have significant mutations in the deletion processes. The reason for this rapid spread is due to the N501Y mutation increasing the receptor binding affinity. The variant also has a deletion at positions 69 and 70 of the S protein [ 85 ]. Furthermore, the B.1.1.7 variant appears to have a 30 % higher mortality rate along with other variants of SARS-CoV-2 [ 86 ].

4.2. B.1.351, 20C/501Y.V2

The B.1.351 variant originated in South Africa. B.1.351 contains 9 S mutations in addition to those of D614G, including a cluster of mutations (e.g., 242-244del & R246I) in NTD, three mutations (K417N, E484K, & N501Y) in RBD, and one mutation (A701V) near the furin cleavage site [ 87 ]. There is a growing concern that these new variants could impair the efficacy of current monoclonal antibody (mAb) therapies or vaccines. This is mainly because many of the mutations reside in the antigenic supersite in NTD16,17 or in the ACE2-binding site (also known as the RBM) which is a major target of potent virus-neutralizing antibodies [ 88 ].

One of Brazil's detected variants of SARS-CoV-2 is the P.1 variant, a descendant of B.1.1.28. This a highly diverse variable, which includes the E484K, K417T and N501Y mutations, was identified in 42 % of the positive individuals [ 68 ]. Viruses that show co-mutations with the P.1 variant cause concern that they may carry a more infectious risk [ 89 ]. As a matter of fact, the inclusion of a common mutation allows it to be contaminated similar to the South African variant as well as to create more re-emerging risks.

This variant was first coined in the US in November 2020. It contains the mutations T95I, D253 G, L5F, S477N, E484K, D614G, A701V [ 90 ], spreads rapidly, and neutralization has been observed to be reduced in patients harboring this mutation [ 91 ].

4.5. B.1.525

The B.1.525 variant, which was first determined in December 2020 and identified in many countries, especially Denmark, is similar to the E484K, Q677H, F888L variants. In addition, B.1.525 is similar to the highly transferable variant B.1.1.7, which also occurs in UK, in that it includes the mutations S:69-70 and S:144 of B.1.1.7 (501Y.V1) [ 92 ]. However, further research is necessary to assess whether B.1.525 causes more contagiousness and more severe outcomes.

4.6. B.1.526

B.1.526 was first identified in New York [ 93 ]. This variant contains the mutations L5F, T95I, D253G, E484K, D614G and A701V [ 94 ]. This variant is thought to spread especially in countries with high seroprevalence. It poses a threat on therapeutic approaches because it harbors previously unseen S protein mutations. Moreover, inoculated plasma is shown to negatively affect the neutralization titer [ 95 ].

4.7. B.1.427/B.1.429

The variant B.1.427/B.1.429 first appeared in California. It spread rapidly in 25 countries in the US and onward [ 96 ]. The emergence of this mutation was triggered by the acquisition of the L452R mutation, which is markedly resistant to mAbs [ 97 , 98 ]. More research is needed to determine whether this variant, known as CAL20C, is more contagious than other forms of the virus.

4.8. B.1.617

Currently available in eight countries, the B.1.617 mutation was first seen in India in October 2020 [ 99 ]. It is the first strain where the E484Q and L425R mutations were first seen together. The effect of these mutations individually on SARS-CoV-2 is well known; however, the combined effect of these mutations still remains unknown [ 100 ].

4.9. B.1.1.298

First defined in June 2020 in a mink farm in Denmark [ 96 ], although it shows similar variations with the B.1.1.7 mutation, B.1.1.298 also contains the Y453F, I692V and M1229I mutations. Although it is reported as an escape mutation, it is seen in fewer people compared to other variants in the current situation, however it is a variant with a high mutation potential [ 101 ]. This variant has also been recently reported to cause a 4-fold increase in hACE2 affinity [ 102 ].

The P.3 variant occurs in South Africa, Brazil and the United Kingdom. It has also been reported recently in the Philippines [ 103 ]. Includes E484K, N501Y and P681H S mutations found in rapidly spreading variants such as B.1.351, P.1 and B.1.1.7 variants [ 104 , 105 ]. It is thought that it may have important effects with ACE2 receptor affinity and neutralizing antibodies in studies [ 106 ].

4.11. Lambda (C.37)

The lambda (C.37) variant, first seen in Peru in August 2020, was identified by the World Health Organization in June 2021 [107,108]. Later, it was seen in 26 countries, especially in America, Europe and Oceania [ 109 ]. C.37 variant B.1.1.7, B.1.351. and P.1 variants as a result of a deletion in the ORF1A gene [ 110 ]. It also harbors mutations Δ246-252, G75V, T76I, L452Q, F490S, D614G and T859N in the S protein. It spreads rapidly with a high prevalence [ 108 ]. This variant shows increased infectivity and immune evasion from antibodies [ 109 ] ( Table 4 ).

Comparison of the fastest-spreading variants.

5. Emergence and observation of CoV viral variants by country

Characterization of the genetic variants of SARS-CoV-2 is crucial for tracking and evaluating its spread across countries. Table 5 shows the variants of SARS-CoV-2 by country, and the changes and effects on the virus. The genomic variability of SARS-CoV-2 samples scattered around the world may be under geographically specific etiological influences. Continuous monitoring of mutations will also be crucial in tracking the movement of the virus between individuals and across geographic areas.

Coronavirus (CoV) mutations and effects by country. ( BCSIR: Bangladesh Council of Scientific and Industrial Research, NILMRC: National Institute of Laboratory Medicine and Referral Center).

After February 2020, it was observed that the viral genomes presented distinct point mutations were clearly discernible in different geographic regions. Three distinct repetitive mutations were detected in Europe and North America. The number and occurrence and the median value of virus point mutations recorded in Asia have increased over time [ 83 ]. It has been determined that the RdRp mutation at position 14408 in European viral genomes is associated with a larger number of point mutations compared to viral genomes from Asia.

Two clinical isolates from India were sequenced. Sequence analysis was performed on S protein of Indian isolates according to Chinese Wuhan isolates. Point mutations were identified in Indian isolates. One of the two isolates was found to harbor a mutation in the RBM at position 407. It has been determined that arginine (a positively charged amino acid) is replaced by isoleucine (hydrophobic amino acid) in this region. With this, a secondary change in the structure of the protein in the region has been demonstrated, and this could potentially alter the receptor binding of the virus [ 109 ].

However, given the small sample size, it is difficult to determine whether D614G is the dominant species in these countries. A recent report supports the high prevalence of D614G in Europe [ 121 ].

Three variants (H49Y, T573I and D614G) found in the Mexican population show multiple sequence alignments of SARS-CoV-2 S proteins. These variants are away from the RBD of the S protein. G614 is neutralized by a polyclonal antibody similar to D614. To date, this variant has become the dominant form, replacing the wild type (WT) according to the mutation levels in the world presented in the Nextstrain database.The H49Y variant is produced with the C/T change at the 21.707 positions. The properties of H/Y residues vary from positive to neutral charge, causing a reduction in total free energy, while D614G-substituted mutants exhibit stabilizing structure, suggesting a prevalent role in S protein evolution. Although these are minute changes due to the chemical nature of the substitution, they are expected to take place at the structural level [ 54 ].

Several common gene mutations have been observed in between the SARS-CoV-2 sequences in China. These mutations are common across countries and follow standard roles. Highlights are T4402C, G5062T, C8782T, C17373T, C20692T, T28144C, C29095T and G29868C. The T4402C mutation causing a silent mutation was recorded in the ORF1a/b gene segment. This mutation is frequently associated with the C8782T, G5062T and T28144C mutations. Similar T4402C and G5062T point mutations were observed in both, isolated in the South Korean strain [ 114 ], C8782T was the dominant mutation reported worldwide in the SARS-CoV-2 gene mutation [ 114 , 115 ]. This mutation is always associated with the ORF8 gene segment T28144C [ 117 ], coexisting with a missense point mutation. The C17373T silent mutation, which was noticed in Singapore and the US, was also observed in Wuhan [ 1 ]. C20692T was restricted to Wuhan and is present with the G29868C gene mutation of the 3′-terminal loop. The C29095T mutation of the gene coding the N protein has also been reported in the US [ 114 , 116 ].

In terms of mutation variants in the genes coding the structural proteins, typical to the European isolates, several additional mutations have been identified, including a synonym mutation in the gene M (C26750T), characteristic to the Russian isolates [ 122 ]. The double mutation, R203K and G204R, in the gene coding the N protein that had previously appeared in Europe began to spread, and quickly became dominant in Russia. The results show that the viral genome of most of the Russian isolates has evolved with the accumulation of new mutations associated with increased viral transmission. Generation of 20A seems to be one of the most common, showing the European origin of Russian isolates. This is based on mutational and phylogenetic analyses of the SARS-CoV-2 genomes isolated in Russia in March-April 2020. However, in Russia, unlike in Western Europe, the triple mutation - G28881A, G28882A and G28883C - which results in double substitution of R203K and G204R in the N protein, has spread and become the dominant form. Thus, by the end of April 2020, the double mutated R203K and G204R genome abundance was over 69.5 % and 32.6 % in Russia and in Europe, respectively [ 117 ].

In the US, the number of genomes belonging to the same subclass identified by the R203K and G204R mutations was even lower, accounting for 13.3 %. The observed variant was likely to to have emerged in Russia in early March 2020. Further spread of the variant was accompanied by the formation of new subtypes with accumulation of the characteristic mutations in the gene M (C26750T) or ORF1b (M1499I or G17964T), following subsequent divergence due to new single (mostly synonymous) mutations in the ORF1ab gene. The rapid spread of the variant with double mutations R203K and G204R in gene N may be indicative of its adaptability and ability to increase the transmission rate rather than modulate the virulence [ 117 ].

The sequencing of three SARS-CoV-2 genomes were reported in Bangladesh. Evidence reveals the first signs in Bangladesh in May-June 2020, followed by constant human-to-human transmission, thus leading to sampled infections. Compared to hCoV-19/Wuhan/WIV04/2019 for the BCSIR-NILMRC-006 strain, eight mutations were found, including Nsp2_G339S, N_R203K, N_G204R, Nsp3_Q172R, S_D614G, Nsp2_I120F, Nsp12_P323L. Six mutations were found in BCSIR-NILMRC-007, S_D614G, N_R203K, N_G204R, Nsp12_K59N, Nsp2_I120F and Nsp12_P323L. Genomic mutations S_D614G, N_R203K, N_G204R, NSP2_I120F, Nsp12_P323L, and Nsp3_P822S were observed in BCSIR-NILMRC-008. A unique mutation, Nsp2_V480I, was observed in the BCSIR-NILMRC-006 genome sequence compared to the genome sequences found in GISAID CoVsurver (GISAID Initiative_CoVsurver_files) [ 98 ].

According to mutation analysis, 59 of the 80 isolates from Turkey in the S protein 23.403A > G (D614G) signed contained the mutation, and this clearly manifested itself to be a frequent mutation (73 %). Most samples with the D614G mutation were strongly associated with two other mutations in the ORF1ab region (3037 C > T and 14.408C > T). These co-occurring mutations have recently been identified as being characteristic to one of the major SARS-CoV-2 variants occurring in Europe. It is assumed that the 14,408C > T (P4715 L) and 3037 C > T (F106 F) variants in ORF1ab occur at high frequency and are associated, resulting in mutations in RdRP/Nsp12 and Nsp3 gene. RdRP/Nsp12 is a key component of the replication/transcription mechanism, and therefore the leucine mutation at position 4715 of RdRP/Nsp12 could potentially affect its function. Moreover, the proline to leucine mutation has been consistently observed as a common mutation in Europe (51.6 %) and North America (58.1 %). C3037T, A23403G and C14408T are the most common mutations found in the isolates from Turkey (73 %) [ 112 ].

The three-dimensional crystaline structure of the s2m RNA element of the SARS-CoV-2 indicates that the mutated guanosine 19 in Australian isolates is critical in tertiary contacts to form an RNA base quartet containing two adjacent G-C pairs (G19, C20, G28 and C31). Since s2m plays an important role in viral RNA to replace host protein synthesis, it is assumed that the degradation of s2m can significantly alter viral viability or infectivity. The s2m sequence of CoVs is highly conserved, and spontaneous changes in this motif are likely due to recombination as mutation is not expected. Due to the high frequency of recombination events occurring in CoVs, RNA recombination can either improve the adaptation process to its new host, such as to humans, or cause unpredictable changes in virulence during infection [ 123 ].

The single amino acid mutation was observed in the virus’s main proteinase (M pro ) of the SARS-CoV-2 Vietnam isolate, R60C, and in the RdRp of the SARS-CoV-2 Indian isolate, A408 V. In silico findings have revelaed that both strains showed 2 mutations to reduce the stability of the protein. Molecular Dynamics (MD) simulation studies on M pro also confirmed that point mutation affects the stability of proteins and binding of the inhibitor. In silico studies found that the M pro catalytic active amino was found to be surrounded by a strand (142-145, 175-200), short helix (40-43, 46-50) and beta leaf regions (25-27, 164-167). The R60C mutant is found in the helix adjacent to the short helix (H2) forming the catalytic channel. A loss of conserved ionic interaction between arginine amide nitrogen and the carboxylic oxygen atom of aspartic acid at position 48 of the catalytic channel was observed [ 118 ].

In UK, the first variant to be investigated in December 2020 was named VUI-202012/01. According to a recent study, this variant is progressing faster than the other existing variants. Cases have been detected in approximately 60 different local government districts. Due to the S protein, changes in the binding properties to host ACE2 receptors can cause the SARS-CoV-2 virus to become more rapid in its spread among humans. The R-value for this variant is thought to be increased by 0.4, or 70 %. According to the data obtained so far, there is no evidence that this variant has a higher probability of causing serious illness or a higher mortality rate [ 119 ].

South Africa was the most severely affected region in Africa, with more than 56,000 extreme natural deaths (about 950 per million population) by December 2020. Three mutations of this new strain (K417 N, E484 K and N501Y) are in the key regions of the RBD. Two, E484 K and N501Y, are within the RBM, which is the main functional motif that interfaces with the hACE2 receptor. The N501Y mutation was recently identified in a new strain (B.1.1.7) in UK and there is some preliminary evidence that this may be more contagious. The E484K mutation is so rare that it is present in <0.02 % of sequences from outside of South Africa. E484 resides in the RBM and interacts with the K31 interaction hotspot residue of hACE2. This is the most striking difference in the RBD-hACE2 complex between SARS-CoV-2 and SARS-CoV, and benefits SARS-CoV-2′s improved binding affinity to hACE2. While all the effects of this new lineage in South Africa have yet to be determined, these findings highlight the importance of coordinated molecular surveillance systems around the world [ 120 ].

6. What the future holds

Since the SARS-CoV-2 virus first emerged, a wide variety of drug compounds affecting the binding sites of the virus have been being studied. Drug trials and vaccine studies are continuing. However, considering the frequency of mutation of the SARS-CoV-2 virus in all drug and vaccine studies, it is necessary to try multiple therapeutic combinations in different mutation types and to compare such studies, preventing possible pathways before the virus mutates. The lack of effective therapeutic and preventive strategies against hCoVs necessitates drug and treatment research. It has previously been shown that designing a broad-spectrum inhibitor in a conservative target is a viable method for developing anti-CoV therapeutics, given the high rates of mutation and recombination observed in viral replication.

The SARS-CoV-2/B.1.1.7 variant has been detected in the US and more than 30 countries, predominantly in England. The B.1.1.7 variant, which exhibits rapid growth and transmission, has the potential to affect healthcare, pandemic management and prevention. However, B.1.1.7, which is transmitted more efficiently than other SARS-CoV-2 variants, has been suggested to be a no neutralization escape variant for existing vaccines and infection. In addition, mAbs specific to the RBD showed full activity against the variant. However, all this shows that the development of SARS-CoV-2 and the emergence of new variants which serve for the immune system escape mechanism are becoming more likely. All this information indicates that our fight against SARS-CoV-2 may still continue in the next 10 years. Large-scale studies on different mutant types in various geographic regions around the world are not yet in the desired intensity. Conducting related studies in increased numbers will pave the way for the efficacy of therapeutic approaches to be developed for the virus in question. Different therapeutic approaches against SARS-CoV-2 have been shown according to different types of CoVs (SARS-CoV, MERS-CoV, etc.), which are similar to SARS-CoV-2, in terms of the location and effectiveness of variation.

If different types of viruses have different serological characteristics, a different vaccine for each subtype will be more effective in preventing COVID-19. Epidemiological studies should be conducted in different countries to understand the pathogenicity course of these subtypes.

The reason why the mutations in glycoprotein S lead to vaccine escape is related to the location of the mutation and the affinity of the protein. However, more evidence is necessary to better understand whether the variants will respond to the vaccines. It probably suggests a situation where we would have to give more than one vaccine, of which the options will possibly vary over time. At the same time, it can be said that variations should be mostly occuring in areas such as the RBD, and vaccines and antiviral drugs should be formulated by targeting more than one viral protein. With the current vaccine developments, antibodies are produced against many regions in the S protein. A single change is unlikely to make the vaccine less effective. However, this can happen as more mutations emerge over time.

Laboratory experiments will be necessary to understand if and how the genomic changes in SARS-CoV-2 may or may not be linked to increases in cases. Nevertheless, many studies have suggested that the new strain does not cause a more severe illness. We must practice active surveillance to detect changes in SARS-CoV-2 as they occur.

7. Discussion

It has been reported that 7 CoVs, including SARS-CoV-2, infect humans in the CoV family with a +ssRNA genome of approximately 30 kb. The rest are SARS-CoV, MERS-CoV, hCoV-NL63, hCoV-229E, hCoV-HKU1 and hCoV-OC43. When the percentage (%) similarity in the sequencing of SARS-CoV, MERS-CoV, hCoV-HKU1 and hCoV-OC43 proteins with SARS-CoV-2 proteins is examined, it is understood that the strain with the highest similarity to SARS-CoV-2 is SARS-CoV.

The S glycoprotein RBD is a critical determinant for viral tropism and infectivity. Mutations in this region will change the affinity of the RBD and show the different infective consequences of the strains. The fact that the most variable region of the CoV family is the RBD causes different strains to emerge and such strains already show different infective profiles. The binding of the SARS-CoV-2 S protein with a high affinity to the ACE-2 receptor is a result of natural selection.

The excess of SARS-CoV-2 S mutations poses a great difficulty in the SARS-CoV-2 targeted therapy and vaccination processes. Mutations, which are one of the largest obstacles in the development of antiviral drug and vaccine formulations, have a crucial role in the preparation, administration and follow-up of vaccines and antiviral drugs.

RNA viruses that exhibit a higher mutation rate than what the host allows them, may escape host immunity and develop drug resistance. This mutation rate drives viral evolution and genome change. Clearly distinguishable mutations of viral genomes have emerged in different geographies. The presence of such mutations is supported by clinical findings. The D614G, S943P and V483a mutations, viral protein mutants, and the emergence of viral strains due to block mutation, play an important role in CoV evolution. Recombination contributes significantly to the viral evolution in the current pandemic. Since viruses mutate during replication, the effect of the antibody concentration produced prior to infection can also be lost. A single amino acid change associated with the mutation rate is effective in the emergence of a new variant with the same epitope. Also, the increase or decrease of hydrogen bonds in receptor interactions is associated with changes in affinity.

The presence of the SARS-CoV-2 strains can be attributed to the heterogeneity of the COVID-19 cases in different regions. Analysis with genomic sequencing has shown that SARS-CoV-2 has transformed into a less contagious strain that affects a number of COVID-19 cases in different regions. The time when different SARS-CoV-2 strains become dominant in a country or a region may indicate the time it will need to overcome the peak of COVID-19 cases. Prospective epidemiological studies of the strains should be conducted to confirm these assumptions. To modulate virus pathogenicity, potential drugs targeting that site can be designed depending on the localization of a given mutation.

Contributors’ statement

All authors contributed to the study conception and design, while Pelin KILIC additionally conducted the overall supervision of the review. Material preparation, data collection and analysis were performed by Begum COSAR, Zeynep Yagmur KARAGULLEOGLU, Sinan UNAL, Ahmet Turan INCE, Dilruba Beyza UNCUOGLU, Gizem TUNCER, Bugrahan Regaip KILINC, Yunus Emre OZKAN, Hikmet Ceyda OZKOC, Ibrahim Naki DEMIR, Ali EKER, Feyzanur KARAGOZ, Said Yasin SIMSEK, Bunyamin YASAR, Mehmetcan PALA, Aysegul DEMIR, Irem Naz ATAK, Aysegul Hanife MENDI, Vahdi Umut BENGI, Guldane CENGIZ SEVAL, Evrim GUNES ALTUNTAS, Devrim DEMIR-DORA and Pelin KILIC, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding statement

The authors declare no specific funding for this work

Compliance with ethical standards

Not applicable.

Consent to participate

Consent for publication, declaration of competing interest.

The authors declare there are no competing interests.

Acknowledgements

The preparation of this review was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK), Project # 18AG020 and TÜBİTAK Intern Researchers (STAR) Program #2247-C.

Dr. Devrim Demir Dora has graduated from Hacettepe University Faculty of Pharmacy in 1995 and got her Msc and PhD degree from Hacettepe University Graduate School of Health Sciences Pharmaceutical Biotechnology Program. She has worked as a Research Assistant at Hacettepe University Faculty of Pharmacy, Pharmaceutical Biotechnology Department from 1996 to 2005. Between 2005 and 2007, she has worked at Turkish Ministry of Health, General Directorate of Pharmaceuticals and Pharmacy, Quality Control Department, and she has participated in GMP Inspection team especially for the biotechnology derived products. She has worked as an Assistant Professor between 2007 and 2010 at Ege University Faculty of Pharmacy Department of Pharmaceutical Biotechnology and since 2010 at Akdeniz University Faculty of Medicine Department of Medical Pharmacology, Medical Biotechnology and Gene and Cell Therapy. Besides her academic duties, Dr. Demir Dora has been worked as ‘Advisory Board Member’ for approval of biotechnological/biosimilar medicinal products since 2009 at Turkish Medicines and Medical Devices Agency. Her areas of interest are recombinant protein production, regulation of biotechnological and biosimilar products, development of biopharmaceuticals, nanotechnology, advanced therapy medicinal products, gene therapy medicinal products, development of non viral nucleic acid delivery systems for gene therapy, cancer therapy, bacterial transformation, quorum sensing mechanism and genetic competence. She has ‘Bacterial Transformation Kit’ patent and three patent applications about non-viral gene delivery system for treatment of breast cancer and pseudomonas infection.

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May 13, 2024

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Genetic analyses reveal new viruses on the horizon

by German Cancer Research Center

Suddenly they appear, and like the SARS-CoV-2 coronavirus, can trigger major epidemics: Viruses that nobody had on their radar. They are not really new, but they have changed genetically. In particular, the exchange of genetic material between different virus species can lead to the sudden emergence of threatening pathogens with significantly altered characteristics.

This is suggested by current genetic analyses carried out by an international team of researchers. Virologists from the German Cancer Research Center (DKFZ) were in charge of the large-scale study, published in the journal PLOS Pathogens .

"Using a new computer-assisted analysis method, we discovered 40 previously unknown nidoviruses in various vertebrates from fish to rodents, including 13 coronaviruses," reports DKFZ group leader Stefan Seitz. With the help of high-performance computers, the research team, which also includes Chris Lauber's working group from the Helmholtz Center for Infection Research in Hanover, has sifted through almost 300,000 data sets . According to virologist Seitz, the fact that we can now analyze such huge amounts of data at once opens up completely new perspectives.

Virus research is still in its relative infancy. Only a fraction of all viruses occurring in nature are known, especially those that cause diseases in humans, domestic animals and crops. The new method therefore promises a quantum leap in knowledge with regard to the natural virus reservoir. Stefan Seitz and his colleagues sent genetic data from vertebrates stored in scientific databases through their high-performance computers with new questions. They searched for virus-infected animals in order to obtain and study viral genetic material on a large scale. The main focus was on so-called nidoviruses, which include the coronavirus family.

Nidoviruses, whose genetic material consists of RNA (ribonucleic acid), are widespread in vertebrates. This species-rich group of viruses has some common characteristics that distinguish them from all other RNA viruses and document their relationship. Otherwise, however, nidoviruses are very different from each other, i.e., in terms of the size of their genome.

One discovery is particularly interesting with regard to the emergence of new viruses: In host animals that are simultaneously infected with different viruses, a recombination of viral genes can occur during virus replication.

"Apparently, the nidoviruses we discovered in fish frequently exchange genetic material between different virus species, even across family boundaries," says Seitz. And when distant relatives "crossbreed," this can lead to the emergence of viruses with completely new properties. According to Seitz, such evolutionary leaps can affect the aggressiveness and dangerousness of the viruses, but also their attachment to certain host animals.

"A genetic exchange, as we have found in fish viruses, will probably also occur in mammalian viruses," explains Seitz. Bats, which—like shrews—are often infected with a large number of different viruses, are considered a true melting pot. The SARS-CoV-2 coronavirus probably also developed in bats and jumped from there to humans.

After gene exchange between nidoviruses, the spike protein with which the viruses dock onto their host cells often changes. Chris Lauber, first author of the study, was able to show this by means of family tree analyses. Modifying this anchor molecule can significantly change the properties of the viruses to their advantage—by increasing their infectiousness or enabling them to switch hosts.

A change of host, especially from animals to humans, can greatly facilitate the spread of the virus, as the corona pandemic has emphatically demonstrated. Viral "game-changers" can suddenly appear at any time, becoming a massive threat, and—if push comes to shove—triggering a pandemic. The starting point can be a single double-infected host animal.

The new high-performance computer process could help to prevent the spread of new viruses. It enables a systematic search for virus variants that are potentially dangerous for humans, explains Seitz.

The DKFZ researcher sees another important possible application with regard to his special field of research, virus-associated carcinogenesis: "I could imagine that we could use the new High Performance Computing (HPC) to systematically examine cancer patients or immunocompromised people for viruses. We know that cancer can be triggered by viruses, the best-known example being human papillomaviruses. But we are probably only seeing the tip of the iceberg so far. The HPC method offers the opportunity to track down viruses that, previously undetected, nestle in the human organism and increase the risk of malignant tumors."

Journal information: PLoS Pathogens

Provided by German Cancer Research Center

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