The Challenges of Genome-Wide Interaction Studies : Lessons to Learn from the Analysis of HDL Blood Levels

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van Leeuwen , E M , Smouter , F A S , Kam-Thong , T , Karbalai , N , Smith , A V , Harris , T B , Launer , L J , Sitlani , C M , Li , G , Brody , J A , Bis , J C , White , C C , Jaiswal , A , Oostra , B A , Hofman , A , Rivadeneira , F , Uitterlinden , A G , Boerwinkle , E , Ballantyne , C M , Gudnason , V , Psaty , B M , Cupples , L A , Jaervelin , M-R , Ripatti , S , Isaacs , A , Mueller-Myhsok , B , Karssen , L C & van Duijn , C M 2014 , ' The Challenges of Genome-Wide Interaction Studies : Lessons to Learn from the Analysis of HDL Blood Levels ' , PLoS One , vol. 9 , no. 10 , e109290 . https://doi.org/10.1371/journal.pone.0109290

Title: The Challenges of Genome-Wide Interaction Studies : Lessons to Learn from the Analysis of HDL Blood Levels
Author: van Leeuwen, Elisabeth M.; Smouter, Francoise A. S.; Kam-Thong, Tony; Karbalai, Nazanin; Smith, Albert V.; Harris, Tamara B.; Launer, Lenore J.; Sitlani, Colleen M.; Li, Guo; Brody, Jennifer A.; Bis, Joshua C.; White, Charles C.; Jaiswal, Alok; Oostra, Ben A.; Hofman, Albert; Rivadeneira, Fernando; Uitterlinden, Andre G.; Boerwinkle, Eric; Ballantyne, Christie M.; Gudnason, Vilmundur; Psaty, Bruce M.; Cupples, L. Adrienne; Jaervelin, Marjo-Riitta; Ripatti, Samuli; Isaacs, Aaron; Mueller-Myhsok, Bertram; Karssen, Lennart C.; van Duijn, Cornelia M.
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Hjelt Institute
Date: 2014-10-20
Language: eng
Number of pages: 13
Belongs to series: PLoS One
ISSN: 1932-6203
URI: http://hdl.handle.net/10138/160797
Abstract: Genome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP×SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value<1 · 10-8 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30,011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP×SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS.
Subject: 3142 Public health care science, environmental and occupational health
HDL blood levels
GWAS
SNPs
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