Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.
Holzinger ER., Verma SS., Moore CB., Hall M., De R., Gilbert-Diamond D., Lanktree MB., Pankratz N., Amuzu A., Burt A., Dale C., Dudek S., Furlong CE., Gaunt TR., Kim DS., Riess H., Sivapalaratnam S., Tragante V., van Iperen EPA., Brautbar A., Carrell DS., Crosslin DR., Jarvik GP., Kuivaniemi H., Kullo IJ., Larson EB., Rasmussen-Torvik LJ., Tromp G., Baumert J., Cruickshanks KJ., Farrall M., Hingorani AD., Hovingh GK., Kleber ME., Klein BE., Klein R., Koenig W., Lange LA., Mӓrz W., North KE., Charlotte Onland-Moret N., Reiner AP., Talmud PJ., van der Schouw YT., Wilson JG., Kivimaki M., Kumari M., Moore JH., Drenos F., Asselbergs FW., Keating BJ., Ritchie MD.
BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). RESULTS: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. CONCLUSIONS: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.