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Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

The ‘discovery’ stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur ‘outside’ the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.

Hearing loss is one of the top contributors to years lived with disability and is a risk factor for dementia. Molecular evidence on the cellular origins of hearing loss in humans is growing. Here, we performed a genome-wide association meta-analysis of clinically diagnosed and self-reported hearing impairment on 723,266 individuals and identified 48 significant loci, 10 of which are novel. A large proportion of associations comprised missense variants, half of which lie within known familial hearing loss loci. We used single-cell RNA-sequencing data from mouse cochlea and brain and mapped common-variant genomic results to spindle, root, and basal cells from the stria vascularis, a structure in the cochlea necessary for normal hearing. Our findings indicate the importance of the stria vascularis in the mechanism of hearing impairment, providing future paths for developing targets for therapeutic intervention in hearing loss. publishedVersion

Summary This protocol describes the application of the “omnigenic” model of the genetic architecture of complex traits to identify novel “core” genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with “core-like” characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020)., Graphical abstract, Highlights • A protocol to identify core modules by integrating gene expression data and GWAS data • Core modules serve as the basis for identifying genes influencing complex traits • Disease gene identification by module integration with publicly available databases, This protocol describes the application of the “omnigenic” model of the genetic architecture of complex traits to identify novel “core” genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with “core-like” characteristics.

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P, GWAS have enhanced our understanding for the genetic basis of breast cancer, but the majority of them were performed for European ancestry populations. Here, the authors use a cross-ancestry approach and report seven new variants associated with breast cancer risk among women of African ancestry.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

SummaryIn earlier work, we have developed and evaluated an alternative approach to the analysis of GWAS data, based on a statistic called the PPLD. More recently, motivated by a GWAS for genetic modifiers of the X-linked Mendelian disorder Duchenne Muscular Dystrophy (DMD), we adapted the PPLD for application to time-to-event (TE) phenotypes. Because DMD itself is relatively rare, this is a setting in which the very large sample sizes generally assembled for GWAS are simply not attainable. For this reason, statistical methods specially adapted for use in small data sets are required. Here we explore the behavior of the TE-PPLD via simulations, comparing the TE-PPLD with Cox Proportional Hazards analysis in the context of small to moderate sample sizes. Our results will help to inform our approach to the DMD study going forward, and they illustrate several respects in which the TE-PPLD, and by extension the original PPLD, offer advantages over regression-based approaches to GWAS in this context.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

Background Genomic studies increasingly integrate expression quantitative trait loci (eQTL) information into their analysis pipelines, but few tools exist for the visualization of colocalization between eQTL and GWAS results. Those tools that do exist are limited in their analysis options, and do not integrate eQTL and GWAS information into a single figure panel, making the visualization of colocalization difficult. Results To address this issue, we developed the intuitive and user-friendly R package eQTpLot. eQTpLot takes as input standard GWAS and cis-eQTL summary statistics, and optional pairwise LD information, to generate a series of plots visualizing colocalization, correlation, and enrichment between eQTL and GWAS signals for a given gene-trait pair. With eQTpLot, investigators can easily generate a series of customizable plots clearly illustrating, for a given gene-trait pair: 1) colocalization between GWAS and eQTL signals, 2) correlation between GWAS and eQTL p-values, 3) enrichment of eQTLs among trait-significant variants, 4) the LD landscape of the locus in question, and 5) the relationship between the direction of effect of eQTL signals and the direction of effect of colocalizing GWAS peaks. These clear and comprehensive plots provide a unique view of eQTL-GWAS colocalization, allowing for a more complete understanding of the interaction between gene expression and trait associations. Conclusions eQTpLot provides a unique, user-friendly, and intuitive means of visualizing eQTL and GWAS signal colocalization, incorporating novel features not found in other eQTL visualization software. We believe eQTpLot will prove a useful tool for investigators seeking a convenient and customizable visualization of eQTL and GWAS data colocalization. Availability and implementation the eQTpLot R package and tutorial are available at https://github.com/RitchieLab/eQTpLot

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual's sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer. Keywords: machine learning; support vector machines; genetic algorithms; genome-wide association studies; single nucleotide polymorphism; pathways analysis

Graphical abstract, Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed “Hierarchical structural CoMponent analysis of Gene-Gene Interactions” (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.

Previous research suggests a genetic overlap between nonsyndromic cleft lip with or without cleft palate (NSCL/P) and cancer. The aim of the present study was to identify common genetic risk loci for NSCL/P and cancer entities that have been reported to co-occur with orofacial clefting. This was achieved through the investigation of large genome-wide association study datasets. Investigations of 12 NSCL/P single nucleotide polymorphisms (SNPs) in 32 cancer datasets, and 204 cancer SNPs in two NSCL/P datasets, were performed. The SNPs rs13041247 (20q12) and rs6457327 (6p21.33) showed suggestive evidence for an association with both NSCL/P and a specific cancer entity. These loci harbor genes of biological relevance to oncogenesis (MAFB and OCT4, respectively). This study is the first to characterize possible pleiotropic risk loci for NSCL/P and cancer in a systematic manner. The data represent a starting point for future research by identifying a genetic link between NSCL/P and cancer.

Late-onset Alzheimer's Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer's disease.

Genetic factors play an important role in contributing to variation in plasma lipid levels across the human population. Large genome-wide association studies (GWAS) in humans have identified more than 100 loci significantly associated with plasma levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), and triglycerides. To prioritize genes that are involved in cholesterol metabolism, we developed a systematic approach to leverage genome-wide liver transcriptomic and proteomic data from multiple mouse reference populations along with human lipid GWAS data. We constructed global co-expression networks from twelve distinct mouse liver datasets, encompassing more than 800 unique mice and identified a conserved module of genes highly enriched for cholesterol biosynthesis. Based on replication across datasets and presence in transcript and protein, we prioritized 112 unique genes. Intersection of these 112 prioritized genes with human GWAS data for LDL, HDL, TC, and triglycerides identified 54 genes to be within 100 kilobases of a significant or suggestive significant single nucleotide polymorphism (SNP). Out of the 54 identified genes that overlap with human GWAS data, 29 have well documented biological roles in cholesterol metabolism, such as LDLR , PCSK9 , and INSIG1 . With the 25 identified genes with no described role in cholesterol metabolism, we tested for transcriptional regulation to cholesterol and performed a functional screen by siRNA knockdown. From this analysis, we identified twelve genes that show transcriptional regulation to cholesterol levels and nine genes that are able to modulate cholesterol metabolism when targeted in vitro with siRNA. Five genes out of the 25 prioritized genes show both transcriptional regulation to cholesterol and ability to modulate cholesterol metabolism in vitro. One of these genes, Sestrin1 , we validate in vivo and in vitro as a modulator of cholesterol metabolism. Collectively, through a systematic approach we have identified 25 highly prioritized genes that have no documented role in cholesterol metabolism, five genes are transcriptionally regulated by cholesterol and influence cholesterol metabolism.

Background Nonsyndromic cleft palate only (nsCPO) is a common and multifactorial form of orofacial clefting. In contrast to successes achieved for the other common form of orofacial clefting, that is, nonsyndromic cleft lip with/without cleft palate (nsCL/P), genome wide association studies (GWAS) of nsCPO have identified only one genome wide significant locus. Aim of the present study was to investigate whether common variants contribute to nsCPO and, if so, to identify novel risk loci. Methods We genotyped 33 SNPs at 27 candidate loci from 2 previously published nsCPO GWAS in an independent multiethnic sample. It included: (i) a family-based sample of European ancestry (n = 212); and (ii) two case/control samples of Central European (n = 94/339) and Arabian ancestry (n = 38/231), respectively. A separate association analysis was performed for each genotyped dataset, and meta-analyses were performed. Results After association analysis and meta-analyses, none of the 33 SNPs showed genome-wide significance. Two variants showed nominally significant association in the imputed GWAS dataset and exhibited a further decrease in p-value in a European and an overall meta-analysis including imputed GWAS data, respectively (rs395572: PMetaEU = 3.16 × 10−4; rs6809420: PMetaAll = 2.80 × 10−4). Conclusion Our findings suggest that there is a limited contribution of common variants to nsCPO. However, the individual effect sizes might be too small for detection of further associations in the present sample sizes. Rare variants may play a more substantial role in nsCPO than in nsCL/P, for which GWAS of smaller sample sizes have identified genome-wide significant loci. Whole-exome/genome sequencing studies of nsCPO are now warranted.

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

Although a number of genome-wide association studies (GWASs) of late-onset Alzheimer's disease (LOAD) have been carried out, there have been little GWAS data on East Asian populations.To discover the novel susceptibility loci of LOAD, we carried out a GWAS using 816 LOAD cases and 7992 controls with a replication analysis using an independent panel of 1011 LOAD cases and 7212 controls in a Japanese population. In addition, we carried out a stratified analysis by APOE-ε4 status to eliminate the established effect of APOE region.Our data indicated that 18p11.32 (rs1992269, P = 9.77 × 10(-7)), CNTNAP2 (rs802571, P = 1.26 × 10(-6)), and 12q24.23 (rs11613092, P = 6.85 × 10(-6)) were suggestive loci for susceptibility to LOAD.We identified three suggestive loci for susceptibility to LOAD in a Japanese population. Among these, rs802571, located at intron 1 of CNTNAP2, was considered to be a plausible candidate locus from a functional perspective.

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

Objective: In this study, we have tried to reveal molecular mechanisms underlying “shared genetic variants” and developed a strategy to identify candidate mechanisms for shared aetiology of a pair of diseases, to uncover biological relationships between quantitative traits or related neurodegenerative diseases. Methods: Genetic variants were collected from GWAS catalog, belonged to multiple disease association studies. Meta-analysis was performed by using Metal (a whole genome association analysis toolset), and normalized them for their different sample sizes. LD analysis was done with Haploreg DB V.4.0. Subsequently, the ENSEMBL variant database was used as a reference database. Additionally, these shared SNPs were interpreted with Regulome DB V.1.1 and finally ranked the variant lists according to predicted functional consequences attributes. Afterwards evidences were collected from gene expression studies, patents, knock-out studies and other literature. Results: Pair-wise analysis also revealed that AD and PD have the largest number of shared disease-associated loci. Additionally, tau locus is discovered in a very novel and unique perspective of stress induced shared pathology of AD and PD, which provides suggestive evidence that the molecular mechanisms influencing aetiology and progression of selective neurodegenerative diseases are at least partly interrelated. Conclusion: Genetic overlap between these diseases suggests that genomic locus should be considered to investigate the effects of GWAS variants rather than individual genetic variants, particularly to investigate shared pathology.

In Box 4 of the originally published article, the text describing the Miami plot in part c of the figure contained some minor errors. Specifically, during pre-publication revision of the article, the authors updated the illustrative Miami plot (shown in figure part c) from that of reference 80 (Randall et al. PLoS Genet. (2013)) to a more recent study in reference 82 (Winkler et al. PLoS Genet. (2015)). The box text has now been updated to reflect that change. In paragraph 2, the trait has been updated from "hip circumference adjusted for body mass index" to "waist-to-hip ratio adjusted for body mass index (under 50 years old)" and it has been clarified that female GWAS data are shown on the top half of the plot with male data at the bottom. The original two citations of reference 80 in the Box 4 text have been updated to reference 82. Finally, a typographical artefact was corrected on the Y axis of the Miami plot, whereby the labels '14' and '16' in the top half of the plot we originally both shown as '12'. None of these corrections alter the overall illustrative point that genetic architectures for traits can differ between males and females, which was a conclusion of both reference 80 and reference 82.

Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.

Schizophrenia is a highly heritable psychiatric disorder with unclear aetiology. Recent genome-wide association studies (GWAS) in European populations have reported numerous susceptibility variants, while GWAS in East Asians also identified several risk loci but with fewer independent replications. Here we focus on nine single nucleotide polymorphisms (SNPs) which have shown genome-wide significant associations with schizophrenia in previous Han Chinese GWAS, and we tend to replicate the associations in four independent samples of East Asian origin including a total of 3977 cases and 5589 controls. The results showed that rs10489202 in MPC2 (BRP44) is significantly associated with schizophrenia in these East Asian replication samples (one-tailed P=5.75×10(-3), OR=1.12), and further meta-analysis after including previous GWAS data yielded a genome-wide significant association (two-tailed P=1.11×10(-10), OR=1.19), adding further support for the involvement of this locus in the genetic risk of schizophrenia, and future studies regarding the underlying molecular mechanisms of the risk association are necessary.

Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005–0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women’s Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r2, specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r2 (squared Pearson correlation between imputed and experimental genotypes).With a desired dosage r2 of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03–0.05, 0.01–0.03, 0.005–0.01, and 0.001–0.005) passed the post-imputation filter. The average dosage r2 for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005–0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.

To detect fully penetrant rare recessive variants that could constitute Mendelian subentities of complex diseases, we propose a novel strategy, the HBD-GWAS strategy, which can be applied to genome-wide association study (GWAS) data. This strategy first involves the identification of inbred individuals among cases using the genome-wide SNP data and then focuses on these inbred affected individuals and searches for genomic regions of shared homozygosity by descent that could harbor rare recessive disease-causing variants. In this second step, analogous to homozygosity mapping, a heterogeneity lod-score, HFLOD, is computed to quantify the evidence of linkage provided by the data. In this paper, we evaluate this strategy theoretically under different scenarios and compare its performances with those of linkage analysis using affected sib-pair (ASP) data. If cases affected by these Mendelian subentities are not enriched in the sample of cases, the HBD-GWAS strategy has almost no power to detect them, unless they explain an important part of the disease prevalence. The HBD-GWAS strategy outperforms the ASP linkage strategy only in a very limited number of situations where there exists a strong allelic heterogeneity. When several rare recessive variants within the same gene are involved, the ASP design indeed often fails to detect the gene, whereas, by focusing on inbred individuals using the HBD-GWAS strategy, the gene might be detected provided very large samples of cases are available.

Genome-wide associations studies (GWAS) have become routine in human genetics research; however the application of GWAS to pharmacogenomics research is just beginning. As these studies make their way into pharmacogenetics, there are several considerations specific to pharmacogenetics that should be made. In this manuscript, we discuss these considerations and provide concrete examples with GWAS data for an asthma clinical trial using parent-offspring trios.

The establishment of high-throughput single-nucleotide polymorphism (SNP)-typing technologies has enabled astonishing progress to be made in genome-wide association studies (GWAS), and various novel genetic factors associated with complex diseases have been discovered. Our organization has created a public repository database (DB) to achieve a continuous and intensive management of GWAS data and to facilitate data sharing among researchers. In the GWAS DB, information on study design, quality control protocols, allele frequencies, genotype frequencies and statistical genetic analysis results are stored as publicly available data and can be accessed freely, whereas individual genotyping data and raw data are stored as restricted data and can only be accessed with authorization. All data are presented by a graphic viewer, which is designed to be user friendly for researchers who are not familiar with GWAS to accelerate disease-related studies. Furthermore, the DB allows users to compare various study results obtained by different institutions and on different platforms. The same data are also managed as a distributed annotation system to call up useful data from other DBs and to superimpose them on the GWAS data for help in interpretation. The DB is accessible at https://gwas.lifesciencedb.jp/.

BACKGROUND: In the past decade many Genome-wide Association Studies (GWAS) were performed that discovered new associations between single-nucleotide polymorphisms (SNPs) and various phenotypes. Imputation methods are widely used in GWAS. They facilitate the phenotype association with variants that are not directly genotyped. Imputation methods can also be used to combine and analyse data genotyped on different genotyping arrays. In this study we investigated the imputation quality and efficiency of two different approaches of combining GWAS data from different genotyping platforms. We investigated whether combining data from different platforms before the actual imputation performs better than combining the data from different platforms after imputation. METHODS: In total 979 unique individuals from the AMC-PAS cohort were genotyped on 3 different platforms. A total of 706 individuals were genotyped on the MetaboChip, a total of 757 individuals were genotyped on the 50K gene-centric Human CVD BeadChip, and a total of 955 individuals were genotyped on the HumanExome chip. A total of 397 individuals were genotyped on all 3 individual platforms. After pre-imputation quality control (QC), Minimac in combination with MaCH was used for the imputation of all samples with the 1,000 genomes reference panel. All imputed markers with an r2 value of

Our previous genome-wide association study (GWAS) identified two susceptibility loci for congenital heart disease (CHD) in Han Chinese. Here we identify additional loci by testing promising associations in an extended 3-stage validation consisting of 6,053 CHD cases and 7,410 controls. We find GW significant (P5.0 × 10(-8)) evidence of 4 additional CHD susceptibility loci at 4q31.22 (rs1400558, upstream of EDNRA, Pall=1.63 × 10(-9)), 9p24.2 (rs7863990, close to SMARCA2, Pall=3.71 × 10(-14)), 12q24.13 (rs2433752, upstream of TBX3 and TBX5, Pall=1.04 × 10(-10)) and 20q12 (rs490514, in PTPRT, Pall=1.20 × 10(-13)). Moreover, the data from previous European GWAS supports that rs490514 is associated with the risk of CHD (P=3.40 × 10(-3)). These results enhance our understanding of CHD susceptibility.

Estimates from large scale genome sequencing studies indicate that each human carries up to 20 genetic variants that are predicted to results in loss of function (LOF) of protein-coding genes. While some are known disease-causing variants or common, tolerated, LOFs in non-essential genes, the majority remain of unknown consequence. We explore the possibility of using imputed GWAS data from large biorepositories such as the electronic medical record and genomics (eMERGE) consortium to determine the effects of rare LOFs. Here, we show that two hypocholesterolemia-associated LOF mutations in the PCSK9 gene can be accurately imputed into large-scale GWAS datasets which raises the possibility of assessing LOFs through genomics-linked medical records.

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.

Cleft lip and/or cleft palate (CL/P) collectively are well known as being amongst the most common birth defects but we still have difficulty explaining why the majority of cases occur. In general, sporadic cases with no family history may be more related to environmental risks, while the presence of one or more affected relative in the same family strongly suggests that genetic factors are the main contributor. Orofacial clefts can occur in conjunction with other defects (syndromic CL/P) or as an isolated defect (non-syndromic - NSCL/P). CL/P syndromes have been studied intensively and appear to have a stronger genetic aetiology. Here we report on the relationship between syndromic and NSCL/P as a phenotypic spectrum resulting from coding or non-coding mutations respectively. We review certain abnormalities that are most frequently associated with CL/P, including dental, heart, brain, skin and certain types of cancer and examine some of the genes that are involved. We include the outcome of recent NSCL/P GWAS data and we will discuss how the genes at these loci might contribute towards clarifying the genetics of CL/P.

An active subnetwork is a group of interconnected genes that show condition-specific differences. It has been observed that the gene products that have alterations associated with a disease of interest, incline to be part of the subnetworks among the overall interaction network. Hence, the integration of the interaction data with the genotypic data underlying disease states facilitates the separation of the subnetworks perturbed in a given disorder from the rest of the network. In the literature, active subnetwork search is used to discover disease related regulatory pathways, dysregulated genes, functional modules, cancer markers, to classify diseases, and to predict response to treatment. In this study, a genetic algorithm based method is developed for active subnetwork search and applied to WTCCC Rheumatoid Arthritis genome-wide association study dataset. The relevance of the identified subnetworks against the disease is compared in terms of biological pathways. Our results show that the proposed method works well in detecting the significant RA associated subnetworks, and it is also applicable to recognize subnetworks of other complex diseases.

Genome-wide association studies (GWAS) offer an excellent opportunity to identify the genetic variants underlying complex human diseases. Successful utilization of this approach requires a large sample size to identify single nucleotide polymorphisms (SNPs) with subtle effects. Meta-analysis is a cost-efficient means to achieve large sample size by combining data from multiple independent GWAS; however, results from studies performed on different populations can be variable due to various reasons, including varied linkage equilibrium structures as well as gene-gene and gene-environment interactions. Nevertheless, one should expect effects of the SNP are more similar between similar populations than those between populations with quite different genetic and environmental backgrounds. Prior information on populations of GWAS is often not considered in current meta-analysis methods, rendering such analyses less optimal for the detecting association. This article describes a test that improves meta-analysis to incorporate variable heterogeneity among populations. The proposed method is remarkably simple in computation and hence can be performed in a rapid fashion in the setting of GWAS. Simulation results demonstrate the validity and higher power of the proposed method over conventional methods in the presence of heterogeneity. As a demonstration, we applied the test to real GWAS data to identify SNPs associated with circulating insulin-like growth factor I concentrations.

The detection of epistasis among genetic markers is of great interest in genome-wide association studies (GWAS). In recent years, much research has been devoted to find disease-associated epistasis in GWAS. However, due to the high computational cost involved, most methods focus on specific epistasis models, making the potential loss of power when the underlying epistasis models are not examined in these analyses. In this work, we propose a computational efficient approach based on complete enumeration of two-locus epistasis models. This approach uses a two-stage (screening and testing) search strategy and guarantees the enumeration of all epistasis patterns. The implementation is done on graphic processing units (GPU), which can finish the analysis on a GWAS data (with around 5,000 subjects and around 350,000 markers) within two hours. Source code is available at http://bioinformatics.ust.hk/BOOST.html∖#GBOOST . This work demonstrates that the complete compositional epistasis detection is computationally feasible in GWAS.

Leveraging models of cell regulation and GWAS data in integrative network-based association studies

Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case-control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (R-T(2)) for the post-imputation QCs in publications. European Journal of Human Genetics (2012) 20, 572-576; doi:10.1038/ejhg.2011.231; published online 21 December 2011

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.

Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.

Genome-wide association studies (GWAS) have had great success in identifying common genetic determinants of disease. One of the challenges posed by GWAS is the analysis of the large amount of data generated. This review aims to provide the non-geneticists with an overview of the different steps entailed in analysis of GWAS data, with an emphasis on popular bioinformatics tools available. GWAS data generation, analysis, and interpretation will be covered.

Purpose: Multiple melanoma GWAS have identified significant associations between single nucleotide polymorphisms (SNPs) and melanoma. As multiple variants within a given genetic locus may contribute to disease risk in different individuals, a novel computational approach to analyzing GWAS data by ‘wide-locus’ region has been shown to increase analytical power, and, in other diseases, has led to the discovery of new susceptibility loci. To date, this approach has not been applied to the study of germline genetic risk factors for melanoma. We applied this method to publically available melanoma GWAS data to identify novel germline risk factors for melanoma. Methods: We re-analyzed data from the M.D. Anderson Cancer Center melanoma GWAS study (dbGap accession: phs000187.v1.p1). In the original study, 1,977 cases and 1,047 controls of European ancestry were genotyped on an Illumina OMNI1-Quad chip. We age-matched cases and controls using “age-censoring” to account for differences in melanoma risk across different age groups. Biostatistical analysis of ‘wide-loci’ was performed across all SNPs to evaluate risk loci based on the influence of neighboring SNPs. The re-analysis was performed by individuals blinded to specific results of the original study. Genome-wide significance for each wide locus was based on number of regions examined, and was set at p Results: Using the ‘wide-locus’ approach, we identified 21 risk loci on 16 chromosomes that met genome-wide significance. Firstly, we replicated all of the genome-wide significant findings of the original, single-SNP analysis, identifying specific SNPs at HERC2 and wide loci at MC1R and AFG3L1 that met genome-wide significance. Secondly, we identified additional SNPs not replicated to genome-wide significance in the original analysis but previously discovered in other populations. These additional SNPs reside in the MC1R, OCA2, CPNE7, DBNDD1, and AFG3L1 loci. Finally, we identified 15 new candidate loci meeting genome-wide significance, with the most significant findings located at KLK12 (associated with carcinogenesis in other cancers), PDE8 (involved with cAMP intra-cellular signaling and immune activation), and LY86 (involved in immune cell activation). Conclusion: Applying ‘wide-locus’-based statistical analysis to melanoma GWAS data replicated the findings of prior single-SNP analyses and identified multiple, new candidate risk loci worthy of further investigation. Citation Format: Rachel M. Cymerman, Benedetta Bigio, Martin P. Seybold, David Polsky, Knut M. Wittkowski. A novel computational re-analysis of published GWAS data suggests new risk loci for melanoma susceptibility. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4627. doi:10.1158/1538-7445.AM2015-4627

The field of stroke genetics continues to make substantive advances. Large-scale partnerships have led to meta-analyses in ischemic and hemorrhagic stroke, which are yielding reproducible genetic risk factors. Most ischemic stroke risk factors seem to be specific to type, for example, large-vessel stroke. Progress is being made in understanding the pathophysiology of single-gene stroke syndromes. Genetics is also being used to potentially advance pharmacotherapeutics. In the genome-wide association study (GWAS) era, several themes are emerging. First, heritability seems to vary by ischemic stroke subtype. Using complex trait analysis on a GWAS data set of >3000 individuals, Bevan et al1 found a 37.9% heritability for ischemic stroke overall, but the heritability varied widely by subtype from a high of 40.3% for large-vessel stroke to a low of 16.1% for small-vessel stroke.1 Second, genetic risk factors seem to be subtype-selective. Third, GWAS have yielded more reliable discoveries than candidate gene studies uninformed by GWAS. In the previously cited study by Bevan,1 no candidate gene previously reported as associated with ischemic stroke could be replicated using GWAS data, but 3 loci from related cardiovascular GWAS were significant even after rigorous correction for multiple comparisons. In the area of ischemic stroke genetics, a partnership between the International Stroke Genetics Consortium and the Wellcome Trust Case Control Consortium 2, which included GWAS data from 5859 cases and 6281 controls, replicated associations …

The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.

Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn's disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with those of standard GWAS analysis; and (ii) how variants in the models used by the prediction tools compare with variants previously reported as eQTLs. We find that all approaches produce highly correlated results when applied to the same GWAS data, although for a subset of genes, mostly in the major histocompatibility complex, the approaches strongly disagree. We also observe that most associations detected by these methods occur near known GWAS risk loci. PrediXcan and MetaXcan's models for predicting expression more consistently recapitulate known effects of genotype on expression, suggesting they are more robust than FUSION. Application of these transcriptome imputation approaches to summary statistics from meta-analyses in Crohn's disease and type 1 diabetes detects 53 significant expression-Crohn's disease associations and 154 significant expression-type 1 diabetes associations, providing insight into biology underlying these diseases. We conclude that while current implementations of transcriptome imputation typically detect fewer associations than GWAS, they nonetheless provide an interesting way of interpreting association signals to identify potentially causal genes, and that PrediXcan and MetaXcan generally produce more reliable results than FUSION.

Background With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data. Methods Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance. Results The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study. Conclusions The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.

Background Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data. Methods We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (

Through their current policy on data sharing, the National Institutes of Health (NIH) are inadvertently placing a serious and potentially insuperable burden upon non-US researchers who perform patient-based genomics studies in collaboration with US institutions. Because this policy could adversely affect future transnational scientific collaborations, we explore some of its likely consequences and suggest possible courses of remedial action wherever feasible.