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Abstract Details

Activity Number: 62
Type: Topic Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #306781
Title: Covariate-Modulated Local False Discovery Rate for Incorporating SNP Annotations in Genome-Wide Association Studies
Author(s): Rong Zablocki*+ and Richard A. Levine and Wesley Thompson
Companies: University of California at San Diego and San Diego State University/JCGS and University of California at San Diego
Address: ACE, 8110 La Jolla Shore Dr., La Jolla, CA, 92037, United States
Keywords: GWAS ; CM-LFDR ; SNP ; mixture model ; annotation ; heritability

Traditional genome-wide association studies (GWAS) evidenced missing heritability arising from a large number of small polygenic effects in heritable complex phenotypes; the Bonferoni correction was over conservative. Schork et al. (2012) proposed a novel approach incorporating the concept of covariate-modulated local false discovery rate (CM-LFDR) into the mixture model of a weighted linear combination of a null density of single nucleotide polymorphism (SNP) without trait-association, and a non-null density with trait-association; information regarding the allocation and linkage disequilibrium (LD) of SNPs were handled as covariates. Hence, a common genetic architecture across diverse complex traits emerged; the regulatory and exonic variation within protein coding genes explained more phenotypic variance per SNP, and replicated at higher rates across independent samples. The goal of the current study is to improve the model performance genetically and/or computationally by adding other potential covariates (e.g., annotations) and seek alternative computational strategies (e.g., variational approximation).

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