Nsf.

Genome-wide association studies (GWAS) facilitate largescale identification of genomic variants that are associated with complex traits. However, susceptibility loci identified by GWAS so far generally account for a limited fraction of the genotypic variation in patient populations. Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use. In this paper, we propose a new method to identify sets of loci that are collectively associated with a trait of interest. We call such sets of loci “population covering locus sets” (PoCos). The main contribution of the proposed approach is three-fold: 1) We consider all possible genotype models for each locus, thereby enabling identification of combinatorial relationships between multiple loci. 2) We use a network model to incorporate the functional relationships among genomic loci to drive the search for PoCos. 3) We develop a novel method to integrate the genotypes of multiple loci in a PoCo into a representative genotype to be used in risk assessment. We test the proposed framework in the context of risk assessment for two complex diseases, Psoriasis (PS) and Type 2Diabetes (T2D). Our results show that the proposed method significantly outperforms individual variant based risk assessment models.