PT  - JOURNAL ARTICLE
AU  - Serhan Yilmaz
AU  - Oznur Tastan
AU  - A. Ercument Cicek
TI  - SPADIS: An Algorithm for Selecting Predictive and Diverse SNPs in GWAS
AID  - 10.1101/256677
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 256677
4099  - http://biorxiv.org/content/early/2018/05/17/256677.short
4100  - http://biorxiv.org/content/early/2018/05/17/256677.full
AB  - Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified 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 SNPs on SNP-SNP networks have been proven successful in 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. In this paper, 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. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor 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 SNP-SNP network in the context of SNP selection problem for the first time, which yields improvements in regression performance across all methods. SPADIS is available at http://ciceklab.cs.bilkent.edu.tr/spadis