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JEPEG: a summary statistics based tool for gene-level joint testing of functional variants
Donghyung Lee1, Vernell S Williamson1, T Bernard Bigdeli1
1Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Center for Biomarker Research & Personalized Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA and Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD 21205, USA.
Bioinformatics (Oxford, England)
|December 16, 2014
Summary
JEPEG software improves gene discovery in genome-wide association studies by jointly modeling multiple expression quantitative trait loci (eQTLs) and imputing unmeasured variants. This approach increases signal detection power for identifying novel or known genes.
Area of Science:
- Genetics
- Bioinformatics
- Statistical genomics
Background:
- Gene expression is regulated by expression quantitative trait loci (eQTLs).
- Prioritizing single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS) often uses univariate eQTL information.
- Joint modeling of multiple eQTLs offers increased power for detecting genotype-phenotype associations compared to univariate methods.
Purpose of the Study:
- To develop a novel software tool, JEPEG, for joint analysis of eQTLs and GWAS data.
- To impute summary statistics for unmeasured eQTLs and test their joint effect on phenotypes.
- To enhance signal detection power in GWAS by considering the combined effects of multiple eQTLs.
Main Methods:
- JEPEG software utilizes GWAS summary statistics.
- Imputes summary statistics for eQTLs not present in the original data.
- Tests the joint association of all measured and imputed eQTLs within a gene on a phenotype.
Main Results:
- JEPEG successfully imputes and tests joint eQTL effects using only GWAS summary statistics.
- Demonstrated performance on GWAS data from the Psychiatric Genomics Consortium and the Genetic Consortium for Anorexia Nervosa.
- The tool effectively increases signal detection power in genetic analyses.
Conclusions:
- JEPEG enhances GWAS by increasing signal detection power for novel and known genes, especially in smaller cohorts.
- Assists in fine-mapping complex genetic regions, such as the MHC region in schizophrenia.
- Complements existing univariate GWAS prioritization tools.
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