Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.

ABSTRACT: Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tis

SUBMITTER: Wu M 

PROVIDER: S-EPMC6455106 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature