SPADIS: an algorithm for selecting predictive and diverse SNPs in GWAS

Yilmaz, Serhan and Taştan, Öznur and Cicek, A. Ercument (2021) SPADIS: an algorithm for selecting predictive and diverse SNPs in GWAS. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18 (3). pp. 1208-1216. ISSN 1545-5963 (Print) 1557-9964 (Online)

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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. 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 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.
Item Type: Article
Uncontrolled Keywords: GWAS; Hi-C; Phenotype prediction; SNP selection; SNP-SNP networks; submodular function
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Öznur Taştan
Date Deposited: 04 Sep 2022 11:42
Last Modified: 04 Sep 2022 11:42

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