Mixture SNPs effect on phenotype in genome-wide association studies.

ABSTRACT:

Background

Recently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs. The HBM models the SNP effects using a mixture distribution of a point mass at zero and a normal distribution, where the point mass corresponds to those non-associative SNPs.

Results

We e

SUBMITTER: Wang L 

PROVIDER: S-EPMC4417323 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature