Diverse SNP selection for epistasis test prioritization

Genome-wide association studies explain a fraction of the underlying heritability of genetic diseases. Epistatic interactions between two or more loci help closing the gap and identifying those complex interactions provides a promising road to a better understanding of complex traits. Unfortunately, sheer number of loci combinations to consider and hypotheses to test prohibit the process both computationally and statistically. This is true even if only pairs of loci are considered. Epistasis prioritization algorithms have proven useful for reducing the computational burden and limiting the number of tests to perform. While current methods aim at avoiding linkage disequilibrium and covering the case cohort, none aims at diversifying the topological layout of the selected SNPs which can detect complementary variants. In this thesis, a two stage pipeline to prioritize epistasis test is proposed. In the first step, a submodular set function is optimized to select a diverse set of SNPs that span the underlying genome to (i) avoid linkage disequilibrium and (ii) pair SNPs that relate to complementary function. In the second step, selected SNPs are used as seeds to a fast epistasis detection algorithm. The algorithm is compared with the state-of-the-art method LinDen on three datasets retrieved from Wellcome Trust Case Control Consortium: type two diabates, hypertension and bipolar disorder. The results show that the pipeline drastically reduces the number of tests to perform while the number of statistically significant epistatic pairs discovered increases.