Risk Prediction Modeling of Sequencing Data Using a Forward Random Field Method.

ABSTRACT: With the advance in high-throughput sequencing technology, it is feasible to investigate the role of common and rare variants in disease risk prediction. While the new technology holds great promise to improve disease prediction, the massive amount of data and low frequency of rare variants pose great analytical challenges on risk prediction modeling. In this paper, we develop a forward random field method (FRF) for risk prediction modeling using sequencing data. In FRF, subjects' phenotypes are treated as stochastic realizations of a random field on a genetic space formed by subjects' genotypes, and an individual's phenotype can be predicted by adjacent subjects with similar genotypes. The FRF method allows for multiple similarity measures and candidate genes in the model, and adaptively

SUBMITTER: Wen Y 

PROVIDER: S-EPMC4759688 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature