The challenges of genome-wide interaction studies: lessons to learn from the analysis of HDL blood levels.
van Leeuwen EM., Smouter FAS., Kam-Thong T., Karbalai N., Smith AV., Harris TB., Launer LJ., Sitlani CM., Li G., Brody JA., Bis JC., White CC., Jaiswal A., Oostra BA., Hofman A., Rivadeneira F., Uitterlinden AG., Boerwinkle E., Ballantyne CM., Gudnason V., Psaty BM., Cupples LA., Järvelin M-R., Ripatti S., Isaacs A., Müller-Myhsok B., Karssen LC., van Duijn CM.
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.