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

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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 ; Psaty, Bruce M. ; Cupples, L. Adrienne ; Järvelin, Marjo-Riitta ; Ripatti, Samuli ; Isaacs, Aaron ; Müller-Myhsok, Bertram ; Karssen, Lennart C. ; van Duijn, Cornelia M. (2014)
  • Publisher: Public Library of Science
  • Journal: PLoS ONE, volume 9, issue 10 (issn: 1932-6203, eissn: 1932-6203)
  • Related identifiers: doi: 10.1371/journal.pone.0109290, pmc: PMC4203717
  • Subject: Molecular Biology | Lipids | Research Article | Biology and Life Sciences | Research and Analysis Methods | Medicine | Gene Identification and Analysis | Lipid Analysis | Genetic Epidemiology | Q | Epidemiology | R | Genetics | Science | Biochemistry | Bioinformatics | Medicine and Health Sciences | Macromolecular Structure Analysis | Genetic Interactions | Database and Informatics Methods

textabstractGenome-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 SNP6SNP 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 · 1028 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 SNP6SNP 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.