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

We introduce new statistical methods for analyzing genomic datasets that measure many effects in many conditions (e.g., 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/condition-specific" assessments. We illustrate these features through an analysis of locally-acting variants associated with gene expression ("cis eQTLs") in 44 human tissues. Our analysis identifies more eQTLs than existing approaches, consistent with improved power. We show that while genetic effects on expression are extensively shared among tissues, effect sizes can still vary greatly among tissues. Some shared eQTLs show stronger effects in subsets of biologically related tissues (e.g., brain-related tissues), or in only one tissue (e.g., testis). Our methods are widely applicable, computationally tractable for many conditions, and available online.