The challenges of genome-wide interaction studies: Lessons to learn from the analysis of HDL blood levels

Elisabeth M. Van Leeuwen*, Françoise A.S. Smouter, Tony Kam-Thong, Nazanin Karbalai, Albert V. Smith, Tamara B. Harris, Lenore J. Launer, Colleen M. Sitlani, Guo Li, Jennifer A. Brody, Joshua C. Bis, Charles C. White, Alok Jaiswal, Ben A. Oostra, Albert Hofman, Fernando Rivadeneira, Andre G. Uitterlinden, Eric Boerwinkle, Christie M. Ballantyne, Vilmundur GudnasonBruce M. Psaty, L. Adrienne Cupples, Marjo Riitta Järvelin, Samuli Ripatti, Aaron Isaacs, Bertram Müller-Myhsok, Lennart C. Karssen, Cornelia M. Van Duijn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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 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.

Original languageEnglish
Article numbere109290
JournalPLoS ONE
Volume9
Issue number10
DOIs
Publication statusPublished - 20 Oct 2014

Bibliographical note

Funding Information:
Framingham Heart Study (FHS). The Framingham Heart Study (FHS), funded by the National Heart Lung and Blood Institute, is an observational population-based cohort study composed of three generations of Framingham (MA) residents predominately of European descent. The Original cohort (N = 5,209) was enrolled in 1948. The children and spouses of the Original cohort comprise the Offspring cohort (N = 5,124), which was enrolled in 1971–1975 . The Third Generation (N = 4,095) consists mostly of the children of the Offspring cohort, and was enrolled in 2002 to 2005 . All participants were examined every 4–8 years. DNA for surviving participants was collected in the late 1990s and early 2000s (1995–2005). Cholesterol and genetic data from 3,464 Offspring subjects and 3,569 Third Generation subjects contribute to this paper.

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