Dataset Information

GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis.

ABSTRACT:

Motivation

Genome-wide association studies are beginning to elucidate how our genetic differences contribute to susceptibility and severity of disease. While computational tools have previously been developed to support various aspects of genome-wide association studies, there is currently a need for informatics solutions that facilitate the integration of data from multiple sources.

Results

Here we present GWAS Analyzer, a database driven web-based tool that integrates genotype and phenotype data, association analysis results and genomic annotations from multiple public resources. GWAS Analyzer contains features for browsing these interrelated data, exploring phenotypic values by family or genotype, and filtering association results based on multiple criteria. The utility of the tool has been demonstrated by a genome-wide association study of human in vitro susceptibility to bacterial infection. GWAS Analyzer facilitated management of large sets of phenotype and genotype data, analysis of phenotypic variation and heritability, and most importantly, generation of a refined set of candidate single nucleotide polymorphisms (SNPs). The tool revealed a SNP that was experimentally validated to be associated with increased cell death among Salmonella infected HapMap cell lines.

SUBMITTER: Fong C

PROVIDER: S-EPMC2820681 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Publications

GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis.

Fong Christine C Ko Dennis C DC Wasnick Michael M Radey Matthew M Miller Samuel I SI Brittnacher Mitchell M

Bioinformatics (Oxford, England) 20100106 4

<h4>Motivation</h4>Genome-wide association studies are beginning to elucidate how our genetic differences contribute to susceptibility and severity of disease. While computational tools have previously been developed to support various aspects of genome-wide association studies, there is currently a need for informatics solutions that facilitate the integration of data from multiple sources.<h4>Results</h4>Here we present GWAS Analyzer, a database driven web-based tool that integrates genotype a ...[more]

PMID: 20053839

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Project description:Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.

Project description:The coupling of electronic medical records (EMR) with genetic data has created the potential for implementing reverse genetic approaches in humans, whereby the function of a gene is inferred from the shared pattern of morbidity among homozygotes of a genetic variant. We explored the feasibility of this approach to identify phenotypes associated with low frequency variants using Vanderbilt's EMR-based BioVU resource. We analyzed 1,658 low frequency non-synonymous SNPs (nsSNPs) with a minor allele frequency (MAF)<10% collected on 8,546 subjects. For each nsSNP, we identified diagnoses shared by at least 2 minor allele homozygotes and with an association p<0.05. The diagnoses were reviewed by a clinician to ascertain whether they may share a common mechanistic basis. While a number of biologically compelling clinical patterns of association were observed, the frequency of these associations was identical to that observed using genotype-permuted data sets, indicating that the associations were likely due to chance. To refine our analysis associations, we then restricted the analysis to 711 nsSNPs in genes with phenotypes in the On-line Mendelian Inheritance in Man (OMIM) or knock-out mouse phenotype databases. An initial comparison of the EMR diagnoses to the known in vivo functions of the gene identified 25 candidate nsSNPs, 19 of which had significant genotype-phenotype associations when tested using matched controls. Twleve of the 19 nsSNPs associations were confirmed by a detailed record review. Four of 12 nsSNP-phenotype associations were successfully replicated in an independent data set: thrombosis (F5,rs6031), seizures/convulsions (GPR98,rs13157270), macular degeneration (CNGB3,rs3735972), and GI bleeding (HGFAC,rs16844401). These analyses demonstrate the feasibility and challenges of using reverse genetics approaches to identify novel gene-phenotype associations in human subjects using low frequency variants. As increasing amounts of rare variant data are generated from modern genotyping and sequence platforms, model organism data may be an important tool to enable discovery.

Dataset Information

GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis.

Motivation

Results

Publications

GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis.

Similar Datasets

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