Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions.

ABSTRACT: We introduce new statistical methods for analyzing genomic data sets that measure many effects in many conditions (for example, gene expression changes under many treatments). These new methods improve on existing methods by allowing for arbitrary correlations in effect sizes among conditions. This flexible approach increases power, improves effect estimates and allows for more quantitative assessments of effect-size heterogeneity compared to simple shared or condition-specific assessments. We illustrate these features through an analysis of locally acting variants associated with gene expression (cis expression quantitative trait loci (eQTLs)) in 44 human tissues. Our analysis identifies more eQTLs than existing approaches, consistent with improved power. We show that although genetic eff

SUBMITTER: Urbut SM 

PROVIDER: S-EPMC6309609 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature