An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations.

ABSTRACT: Genome-wide association studies have revealed individual genetic variants associated with phenotypic traits such as disease risk and gene expressions. However, detecting pairwise interaction effects of genetic variants on traits still remains a challenge due to a large number of combinations of variants (∼10(11) SNP pairs in the human genome), and relatively small sample sizes (typically <10(4)). Despite recent breakthroughs in detecting interaction effects, there are still several open problems, including: (1) how to quickly process a large number of SNP pairs, (2) how to distinguish between true signals and SNPs/SNP pairs merely correlated with true signals, (3) how to detect nonlinear associations between SNP pairs and traits given small sample sizes, and (4) how to control false positi

SUBMITTER: Lee S 

PROVIDER: S-EPMC4876555 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature