SPADIS: GWAS çalışmalarında açıklayıcı ve çeşitli SNPS seçimi

Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identfied in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected Single Nucleotide Polymorphisms (SNPs) on SNP-SNP networks has been proven successful finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. To this end, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor (1 - 1/e) approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identifies more candidate genes and runs faster. We also investigate the use of Hi-C data to construct a SNP SNP network in the context of SNP selection problem for the first time, which yields improvements in regression performance across all methods.