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Title: iFunMed: Integrative functional mediation analysis of GWAS and eQTL studies
Abstract

Genome‐wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome‐wide functional annotation data is providing unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Although there have been many advances in incorporating prior information into prioritization of trait‐associated variants in GWAS, functional annotation data have played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence for association. To address this, we develop a novel mediation framework,iFunMed, to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data.iFunMedextends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant‐level summary statistics. Data‐driven computational experiments convey how informative annotations improve single‐nucleotide polymorphism (SNP) selection performance while emphasizing robustness ofiFunMedto noninformative annotations. Application to Framingham Heart Study data indicates thatiFunMedis able to boost detection of SNPs with mediation effects that can be attributed to regulatory mechanisms.

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PAR ID:
10114247
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Genetic Epidemiology
Volume:
43
Issue:
7
ISSN:
0741-0395
Page Range / eLocation ID:
p. 742-760
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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