Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests

• DOI: 10.1371/journal.pgen.1005502
• Publication Date: 2015-09-24T17:51:41Z
• Authors: Mattias Frånberg; Karl Gertow; Anders Hamsten; ; Jens Lagergren; Bengt Sennblad
• Keywords: Statistical power; Identification; Genome-wide Association Study; Multiple comparisons problem

Despite the success of genome-wide association studies in medical genetics, underlying genetics many complex diseases remains enigmatic. One plausible reason for this could be failure to account presence genetic interactions current analyses. Exhaustive investigations are typically infeasible because vast number possible impose hard statistical and computational challenges. There is, therefore, a need computationally efficient methods that build on models appropriately capturing interaction. We introduce new methodology where we augment interaction hypothesis with set simpler hypotheses tested, order their complexity, against saturated alternative representing This sequential testing provides an way reduce non-interacting variant pairs before final test. devise two different methods, one relies priori estimated numbers marginally associated variants correct multiple tests, second does adaptively. show our general has improved power comparison seven other and, using idea closed testing, it controls family-wise error rate. apply data from PROCARDIS coronary artery disease case/control cohort discover three distinct interactions. While analyses simulated suggest may suffice exhaustive search all ideal cases, explore strategies selecting subsets Our facilitates identification disease-relevant existing future data, which involve genes previously unknown disease. Moreover, enables construction networks provide systems biology view diseases, serving as basis more comprehensive understanding pathophysiology its clinical consequences.