Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data.

ABSTRACT: Genetic data consists of a wide range of marker types, including common, low-frequency, and rare variants. Multiple genetic markers and their interactions play central roles in the heritability of complex disease. In this study, we propose an algorithm that uses a stratified variable selection design by genetic architectures and interaction effects, achieved by a dataset-adaptive W-test. The polygenic sets in all strata were integrated to form a classification rule. The algorithm was applied to the Critical Assessment of Genome Interpretation 4 bipolar challenge sequencing data. The prediction accuracy was 60% using genetic markers on an independent test set. We found that epistasis among common genetic variants contributed most substantially to prediction precision. However, the sample si

SUBMITTER: Wang MH 

PROVIDER: S-EPMC5561515 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature