Postgenome-wide Association Studies Research Articles

Genetic and functional research strategies of non-syndromic cleft lip with or without cleft palate in the post genome-wide association study era

The emergence of genome-wide association studies (GWAS) has greatly promoted the genetic research of non-syndromic cleft lip with or without cleft palate (NSCL/P). There have been more than 40 regions concerning NSCL/P identified by GWAS, whereas specific susceptible loci and their potential function remains unclear. In the post-GWAS era, precise localization of susceptible loci in candidate regions and exploration of underlying biological mechanism will contribute to further understanding of genetic etiology of NSCL/P. The present article reviewed the genetic and functional research strategies of NSCL/P in post-GWAS era.

Genome‐wide association study on Alzheimer’s disease related cognitive, fluid, and neuropathology biomarkers in the African American population

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder that progresses over time, but its exact cause remains unknown. While risk genomic loci for AD‐related biomarkers have been identified in previous studies, these findings have mostly been based on European cohorts. As a result, there is limited research on the African American (AA) population. To address this research gap, we conducted a Genome‐Wide Association Study (GWAS) using whole genome sequencing (WGS) data from Alzheimer’s Disease Sequencing Project (ADSP) to identify AD‐related variants in the AA population.MethodWGS data and harmonized phenotypes from the cognitive, fluid biomarker, and neuropathology domains were downloaded from the ADSP database. Quality control (QC) was performed on the WGS data through removing high missing call rate SNPs, rare genetic variants, genetically closely related subjects, subjects with high missing call rate, and genomic loci that failed the Hardy‐Weinberg equilibrium among cognitive normal cohort. After QC 9,815,631 SNPs and 33,324 subjects were preserved. A total of 5,397 subjects in AA population were selected for the subsequent GWAS analysis. Scalable and Accurate Implementation of Generalized mixed model (SAIGE) was used to perform association tests between genetic variants and 39 phenotypic traits controlled for age, sex, and the first 10 principal components. Functional mapping and annotation (FUMA) was used to perform the post GWAS annotations.ResultThirty‐nine phenotypic traits from cognitive, fluid biomarker, and neuropathology domains were screened but only the “infarcts and lacunes”, “PD braak (dichotomous)”, and “whole brain vascular disease” of the neuropathology data had significant GWAS signals. We identified one risk locus for each of the phenotypic traits: lead SNP rs7604487 significantly associated with “infarcts and lacunes”; lead SNP rs7093080 significantly associated with “PD braak (dichotomous)”; and lead SNP rs623123 significantly associated with “whole brain vascular disease” (Figure 1). These associations were insignificant in the ADSP cohort with European ancestry.ConclusionOur GWAS on AD related biomarkers identified three genetic loci associated with multiple neuropathological processes among the AA population. These findings have a potential to contribute to a better understanding of the AD genetic risk in the AA population. It warrants further investigation to replicate the discovery in independent cohorts.

Gene and schizophrenia in the pregenome and postgenome-wide association studies era: a bibliometric analysis and network visualization.

This study aimed to perform a bibliometric analysis on genetic studies in schizophrenia in the pregenome-wide association studies (GWAS) and post-GWAS era. We searched the literature on genes and schizophrenia using the Scopus database. The documents increased with time, especially after the human genome project and International HapMap Project, with the highest citation in 2008. The top occurrence author keywords were discovered to be different in the pre-GWAS and post-GWAS eras, reflecting the progress of genetic studies connected to schizophrenia. Emerging keywords highlighted a trend towards an application of precision medicine, showing an interplay of environmental exposures as well as genetic factors in schizophrenia pathogenesis, progression, and response to therapy. In conclusion, the gene and schizophrenia literature has grown rapidly after the human genome project, and the temporal variation in the author keywords pattern reflects the trend of genetic studies related to schizophrenia in the pre-GWAS and post-GWAS era.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects.

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

REALGAR: a web app of integrated respiratory omics data.

In the post genome-wide association study (GWAS) era, omics techniques have characterized information beyond genomic variants to include cell and tissue type-specific gene transcription, transcription factor binding sites, expression quantitative trait loci (eQTL) and many other biological layers. Analysis of omics data and its integration has in turn improved the functional interpretation of disease-associated genetic variants. Over 170000 transcriptomic and epigenomic datasets corresponding to studies of various cell and tissue types under specific disease, treatment and exposure conditions are available in the Gene Expression Omnibus resource. Although these datasets are valuable to guide the design of experimental validation studies to understand the function of disease-associated genetic loci, in their raw form, they are not helpful to experimental researchers who lack adequate computational resources or experience analyzing omics data. We sought to create an integrated re-source of tissue-specific results from omics studies that is guided by disease-specific knowledge to facilitate the design of experiments that can provide biologically meaningful insights into genetic associations. We designed the Reducing Associations by Linking Genes and omics Results web app to provide multi-layered omics information based on results from GWAS, transcriptomic, epigenomic and eQTL studies for gene-centric analysis and visualization. With a focus on asthma datasets, the integrated omics results it contains facilitate the formulation of hypotheses related to airways disease-associated genes and can be addressed with experimental validation studies. The REALGAR web app is available at: http://realgar.org/. The source code is available at: https://github.com/HimesGroup/realgar. Supplementary data are available at Bioinformatics online.

GWAS to Identify Novel QTNs for WSCs Accumulation in Wheat Peduncle Under Different Water Regimes.

Water-soluble carbohydrates (WSCs) play a vital role in water stress avoidance and buffering wheat grain yield. However, the genetic architecture of stem WSCs’ accumulation is partially understood, and few candidate genes are known. This study utilizes the compressed mixed linear model-based genome wide association study (GWAS) and heuristic post GWAS analyses to identify causative quantitative trait nucleotides (QTNs) and candidate genes for stem WSCs’ content at 15 days after anthesis under different water regimes (irrigated, rainfed, and drought). Glucose, fructose, sucrose, fructans, total non-structural carbohydrates (the sum of individual sugars), total WSCs (anthrone based) quantified in the peduncle of 301 bread wheat genotypes under multiple environments (E01-E08) pertaining different water regimes, and 14,571 SNPs from “35K Axiom Wheat Breeders” Array were used for analysis. As a result, 570 significant nucleotide trait associations were identified on all chromosomes except for 4D, of which 163 were considered stable. A total of 112 quantitative trait nucleotide regions (QNRs) were identified of which 47 were presumable novel. QNRs qWSC-3B.2 and qWSC-7A.2 were identified as the hotspots. Post GWAS integration of multiple data resources prioritized 208 putative candidate genes delimited into 64 QNRs, which can be critical in understanding the genetic architecture of stem WSCs accumulation in wheat under optimum and water-stressed environments. At least 19 stable QTNs were found associated with 24 prioritized candidate genes. Clusters of fructans metabolic genes reported in the QNRs qWSC-4A.2 and qWSC-7A.2. These genes can be utilized to bring an optimum combination of various fructans metabolic genes to improve the accumulation and remobilization of stem WSCs and water stress tolerance. These results will further strengthen wheat breeding programs targeting sustainable wheat production under limited water conditions.

In silico genome-wide gene-based association analysis reveals new genes predisposing to coronary artery disease.

Genome-wide association study (GWAS) have identified more than 300 single nucleotide polymorphisms at 163 independent loci associated with coronary artery disease (CAD). However, there is no full understanding about the causal genes for CAD and the mechanisms of their action. We aimed to perform a post GWAS analysis to identify genes whose polymorphism may influence the risk of CAD. Using the UK Biobank GWAS summary statistics, we performed a gene-based association analysis. We found 63 genes significantly associated with CAD due to their within-gene polymorphisms. Many of these genes are well known. Some known CAD genes such as FURIN and SORT1 did not show the gene-based association because their variants had low GWAS signals or gene-based association was inflated by the strong GWAS signal outside the gene. For several known CAD genes, we demonstrated that their effects could be explained not only or not at all by their own variants but by the variants within the neighboring genes controlling their expression. Using several bioinformatics techniques, we suggested potential mechanisms underlying gene-CAD associations. Three genes, CDK19, NCALD, and ARHGEF12 were not previously associated with CAD. The role of these genes should be clarified in further studies.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes.

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

Polygenicity in Psychiatry—Like It or Not, We Have to Understand It

The subjective and symptomatic basis of psychiatric diagnoses—coupled with high rates of comorbidity, clinical heterogeneity, and the overwhelming complexity of the human brain—has greatly hindered a rigorous biological formulation necessary for rational therapeutic development. Despite this, among the most immutable characteristics of psychiatric disorders is that they run in families. It is now clear that nearly all psychiatric traits have a substantial heritable component that appears to contribute to most of the disease liability ( 1 Gandal M.J. Leppa V. Won H. Parikshak N.N. Geschwind D.H. The road to precision psychiatry: Translating genetics into disease mechanisms. Nat Neurosci. 2016; 19: 1397-1407 Crossref PubMed Scopus (118) Google Scholar ). There is substantial optimism that identifying the underlying genetic risk factors contributing to this heritability will provide a rigorous, quantitative foothold that can advance our understanding of disease mechanisms ( 2 Hyman S.E. Wringing biological insight from polygenic signals. Biol Psychiatry. 2021; 89: 8-10 Google Scholar ). After years of false starts, the last decade has seen tremendous gains in the identification of now hundreds of robust disease-associated genetic variants. This success can be attributed to the move from candidate gene to genome-wide studies, coupled with rigorous statistical analyses and parallel increases in study sample size owing to international collaborative efforts ( 3 Sullivan P.F. Geschwind D.H. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell. 2019; 177: 162-183 Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar ). Based on these advances, the number of replicated, genome-wide significant disease loci are expected to rise exponentially in the coming years. As we enter the post–genome-wide association study (GWAS) era, the next substantial challenge is how to interpret such genetic risk within an individual and at a population level ( 1 Gandal M.J. Leppa V. Won H. Parikshak N.N. Geschwind D.H. The road to precision psychiatry: Translating genetics into disease mechanisms. Nat Neurosci. 2016; 19: 1397-1407 Crossref PubMed Scopus (118) Google Scholar ).

Genomewide Mapping of Selection Signatures and Genes for Extreme Feather Pecking in Two Divergently Selected Laying Hen Lines.

Simple SummaryFeather pecking is a behavior frequently occurring in commercial layer flocks. It often leads to skin injuries and cannibalism. Besides economic losses, severe animal welfare problems cannot be ignored. Previous research has shown that the trait is heritable. Thus breeding against feather pecking is possible, but phenotyping in a commercial environment is economically unfeasible at the moment because of the lack of proper techniques. Therefore, understanding the genetic background of the trait is mandatory to establish a genomic breeding program. This would require genotypic information of the hens, which is feasible under practical conditions. In the present study, we used different methods to identify regions in the genome that influence feather pecking and extreme feather pecking. We found one trait associated with the genomic region. The use of genotypic information from this region in terms of selection against the undesired behavior may help to improve animal welfare in layer flocks.Feather pecking (FP) is a longstanding serious problem in commercial flocks of laying hens. It is a highly polygenic trait and the genetic background is still not completely understood. In order to find genomic regions influencing FP, selection signatures between laying hen lines divergently selected for high and low feather pecking were mapped using the intra-population iHS and the inter-population FST approach. In addition, the existence of an extreme subgroup of FP hens (EFP) across both selected lines has been demonstrated by fitting a mixture of negative binomial distributions to the data and calculating the posterior probability of belonging to the extreme subgroup (pEFP) for each hen. A genomewide association study (GWAS) was performed for the traits pEFP and FP delivered (FPD) with a subsequent post GWAS analysis. Mapping of selection signatures revealed no clear regions under selection. GWAS revealed a region on Chromosome 1, where the existence of a QTL influencing FP is likely. The candidate genes found in this region are a part of the GABAergic system, which has already been linked to FP in previous studies. Despite the polygenic nature of FP, selection on these candidate genes may reduce FP.

Lymphocyte DNA methylation mediates genetic risk at shared immune-mediated disease loci

BackgroundDefining regulatory mechanisms through which noncoding risk variants influence the cell-mediated pathogenesis of immune-mediated disease (IMD) has emerged as a priority in the post–genome-wide association study era.ObjectivesWith a focus on rheumatoid arthritis, we sought new insight into genetic mechanisms of adaptive immune dysregulation to help prioritize molecular pathways for targeting in this and related immune pathologies.MethodsWhole-genome methylation and transcriptional data from isolated CD4+ T cells and B cells of more than 100 genotyped and phenotyped patients with inflammatory arthritis, all of whom were naive to immunomodulatory treatments, were obtained. Analysis integrated these comprehensive data with genome-wide association study findings across IMDs and other publicly available resources.ResultsWe provide strong evidence that disease-associated DNA variants regulate cis-CpG methylation in CD4+ T and/or B cells at 37% RA loci. Using paired, cell-specific transcriptomic data and causal inference testing, we identify examples where site-specific DNA methylation in turn mediates gene expression, including FCRL3 in both cell types and ORMDL3/GSDMB, IL6ST/ANKRD55, and JAZF1 in CD4+ T cells. A number of genes regulated in this way highlight mechanisms common to RA and other IMDs including multiple sclerosis and asthma, in turn distinguishing them from osteoarthritis, a primarily degenerative disease. Finally, we corroborate the observed effects experimentally.ConclusionsOur observations highlight important mechanisms of genetic risk in RA and the wider context of immune dysregulation. They confirm the utility of DNA methylation profiling as a tool for causal gene prioritization and, potentially, therapeutic targeting in complex IMD.

Identification of genetic markers associated with milk production traits in Chinese Holstein cattle based on post genome-wide association studies

With the rapid development of dairy industry, the breeding process of dairy cows has been accelerated. In previous genome-wide association studies (GWAS), a large number of genetic markers have been reported which may contribute to the selection of Holstein populations with superior milk-producing traits, but they remain to be further verified before practical application. In this study, 90 single nucleotide polymorphisms (SNPs) were selected, which were reported to be significantly associated with five milk production traits, including 305-day milk yield (305MY), 305-day milk fat percent (305FC), 305-day milk protein percent (305PC), 305-day milk fat yield (305FY) and 305-day milk protein yield (305PY). Effective 305-day data and fresh DNA samples were obtained from 295 healthy cows with gestational age of 1–4. Matrix-assisted laser desorption/ionization—time of flight (MALDI-TOF) was used to perform precise genotyping of these loci, followed by site association and haplotype analysis. Results showed that 36 out of 90 loci were supported to be used as genetic markers. In particular, several novel and effective haplotypes were also presented. Overall, our results verified tens of useful markers and provided a basis for further development of breeding strategies.

Genome-Wide Identification of N6-Methyladenosine (m6A) SNPs Associated With Rheumatoid Arthritis.

Previous large scale genome-wide association studies (GWAS) have identified more than one hundred susceptibility loci for rheumatoid arthritis (RA) (Stahl et al., 2010; Eyre et al., 2012; Okada et al., 2012, 2014b). One major issue in the post GWAS era is to identify functional (causal) variants in the disease-associated loci. Recently, whole-exome sequencing studies have identified several missense mutations for RA (Mitsunaga et al., 2013; Okada et al., 2014a). Nevertheless, few studies have focused on potentially functional variants which could affect N6-methyladenosine (m6A) methylation. N6-methyladenosine (m6A) has been discovered as the first example of reversible RNA methylation and is a pervasive modification in the mRNA of all eukaryotes. It plays critical roles in regulating many fundamental biological processes such as gene expression control (Meyer and Jaffrey, 2014) and regulation of mRNA stability (Wang et al., 2014) and homeostasis (Edupuganti et al., 2017). Evidence has increasingly shown that m6A modification modulates almost all aspects of RNA processing such as nuclear export, translatability, splicing, and miRNA processing (Visvanathan and Somasundaram, 2018). Thus, m6A modification adds a new layer of gene expression regulation, and it has been implicated in T cell differentiation, homeostasis, and response to HIV infection (Li et al., 2018). Moreover, it was found to be involved in the etiology of various diseases (Visvanathan and Somasundaram, 2018). Given the vital function of m6A modification in gene expression regulation and immune response, it is possible that m6A is involved in the etiology of RA. Recent studies have suggested that SNPs would affect m6A by altering the RNA sequences of the target sites or key flanking nucleotides (Zheng et al., 2018). It means that m6A-associated SNPs (m6A-SNPs) may have regulatory potential to affect gene expression and mRNA stability and homeostasis, which may consequently affect disease such as RA. However, what is the relationship between m6A-SNPs and RA is still unclear. Thus, we examined the effect of the m6A-SNPs on RA in a large scale GWAS. Then, to test whether the RA-associated m6A-SNPs have potential regulatory effects on gene expression and whether these disruptions may contribute to RA, we performed eQTL and differential expression analyses using whole genome data of about 10.5 million SNPs and 21,323 mRNAs from 26 RA cases and 17 controls, as well as public data.

Statistical methods to detect novel genetic variants using publicly available GWAS summary data

We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.

Using postgenome-wide association study analysis; Vars2-Pic3ca-AKT is novel putative interactive pathway associated with conotruncal heart defects

Background: Genome-wide association studies (GWASs) have identified 43-associated variants with conotruncal heart defects (CTD) at P ≤ 5E-7, P ≤ 5E-6, and P ≤ 5E-5 till January 2018. Despite the GWASs still provide a growing number of associated genetic variants, the identification of their pathogenic mechanisms remains a major challenge in human genetics requiring data mining efforts and predictive models for accurate interpretation of noncoding variants especially. Hence, I applied a post-GWAS analysis approach to identify the possible mechanism of action of noncoding variants associated with CTD according to the latest update of the GWAS catalog. Method: I aimed in this study to conduct a post-GWAS analysis of the most associated noncoding variants with CTD to elucidate its probable pathogenic mechanisms. Through three GWAS traits belong to CTDs, it was used in a variety of integrated and computational algorithms to collect the available information about the selected noncoding variants and its associated partners. Results: It was found differential histone modification, motif binding, and gene expression among the CTD-associated single nucleotide polymorphisms. Through intensive analysis, it was also found that both the linked rs2517582 and rs17189763 haplotype might lead to alteration in the Pic3ca-AKT signal pathway due to a change of CTCF-binding affinity and upregulation of VARS2 gene. Conclusion: It was concluded that the indirect effect of the upregulation VARS2 gene might associates with CTD. Consequently, phosphoinositide 3-kinases/AKT (pic3ca/AKT) pathway has a pivotal role in the signal system of heart morphogenesis.

HaploR: an R package for querying web-based annotation tools.

We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

HaploR: an R package for querying web-based annotation tools

We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

HaploR: an R-package for querying web-based annotation tools

We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

Genetic effects of FASN, PPARGC1A, ABCG2 and IGF1 revealing the association with milk fatty acids in a Chinese Holstein cattle population based on a post genome-wide association study.

BackgroundA previous genome-wide association study deduced that one (ARS-BFGL-NGS-39328), two (Hapmap26001-BTC-038813 and Hapmap31284-BTC-039204), two (Hapmap26001-BTC-038813 and BTB-00246150), and one (Hapmap50366-BTA-46960) genome-wide significant single nucleotide polymorphisms (SNPs) associated with milk fatty acids were close to or within the fatty acid synthase (FASN), peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PPARGC1A), ATP-binding cassette, sub-family G, member 2 (ABCG2) and insulin-like growth factor 1 (IGF1) genes. To further confirm the linkage and reveal the genetic effects of these four candidate genes on milk fatty acid composition, genetic polymorphisms were identified and genotype-phenotype associations were performed in a Chinese Holstein cattle population.ResultsNine SNPs were identified in FASN, among which SNP rs41919985 was predicted to result in an amino acid substitution from threonine (ACC) to alanine (GCC), five SNPs (rs136947640, rs134340637, rs41919992, rs41919984 and rs41919986) were synonymous mutations, and the remaining three (rs41919999, rs132865003 and rs133498277) were found in FASN introns. Only one SNP each was identified for PPARGC1A, ABCG2 and IGF1.Association studies revealed that FASN, PPARGC1A, ABCG2 and IGF1 were mainly associated with medium-chain saturated fatty acids and long-chain unsaturated fatty acids, especially FASN for C10:0, C12:0 and C14:0. Strong linkage disequilibrium was observed among ARS-BFGL-NGS-39328 and rs132865003 and rs134340637 in FASN (D´ > 0.9), and among Hapmap26001-BTC-038813 and Hapmap31284-BTC-039204 and rs109579682 in PPARGC1A (D´ > 0.9). Subsequently, haplotype-based analysis revealed significant associations of the haplotypes encompassing eight FASN SNPs (rs41919999, rs132865003, rs134340637, rs41919992, rs133498277, rs41919984, rs41919985 and rs41919986) with C10:0, C12:0, C14:0, C18:1n9c, saturated fatty acids (SFA) and unsaturated fatty acids (UFA) (P = 0.0204 to P < 0.0001).ConclusionOur study confirmed the linkage between the significant SNPs in our previous genome-wide association study and variants in FASN and PPARGC1A. SNPs within FASN, PPARGC1A, ABCG2 and IGF1 showed significant genetic effects on milk fatty acid composition in dairy cattle, indicating their potential functions in milk fatty acids synthesis and metabolism. The findings presented here provide evidence for the selection of dairy cows with healthier milk fatty acid composition by marker-assisted breeding or genomic selection schemes, as well as furthering our understanding of technological processing aspects of cows’ milk.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0418-x) contains supplementary material, which is available to authorized users.

SNPTracker: A Swift Tool for Comprehensive Tracking and Unifying dbSNP rs IDs and Genomic Coordinates of Massive Sequence Variants.

The reference single nucleotide polymorphism (rs) ID in dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) is a key resource identifier, which is widely used in human genetics and genomics studies. However, its application is often complicated by the varied IDs of different versions. Here, we developed a user-friendly tool, SNPTracker, for comprehensively tracking and unifying the rs IDs and genomic coordinates of massive sequence variants at a time. It worked perfectly, and had much higher accuracy and capacity than two alternative utilities in our proof-of-principle examples. SNPTracker will greatly facilitate genetic data exchange and integration in the postgenome-wide association study era.