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

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Kam-Thong,  Tony
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Karbalai,  Nazanin
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Müller-Myhsok,  Bertram
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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journal.pone.0109290.pdf
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Citation

van Leeuwen, E. M., Smouter, F. A. S., Kam-Thong, T., Karbalai, N., Smith, A. V., Harris, T. B., et al. (2014). The Challenges of Genome-Wide Interaction Studies: Lessons to Learn from the Analysis of HDL Blood Levels. PLOS ONE, 9(10): e109290. doi:10.1371/journal.pone.0109290.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0026-DB2C-F
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 SNPxSNP 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 (N-total = 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 SNPxSNP 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.