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- Title
The Challenges of Genome-Wide Interaction Studies: Lessons to Learn from the Analysis of HDL Blood Levels.
- Authors
van Leeuwen, Elisabeth M.; Smouter, Françoise 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
- 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.
- Subjects
HIGH density lipoproteins; SINGLE nucleotide polymorphisms; GRAPHICS processing units; REGRESSION analysis; META-analysis
- Publication
PLoS ONE, 2014, Vol 9, Issue 10, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0109290