PT - JOURNAL ARTICLE AU - Sarah Margaret Urbut AU - Gao Wang AU - Matthew Stephens TI - Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions AID - 10.1101/096552 DP - 2017 Jan 01 TA - bioRxiv PG - 096552 4099 - http://biorxiv.org/content/early/2017/05/09/096552.short 4100 - http://biorxiv.org/content/early/2017/05/09/096552.full AB - 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 effects among conditions. This flexible approach increases power, improves effect-size estimates, and facilitates more quantitative assessments of effect-size heterogeneity than simple “shared/condition-specific” assessments. We illustrate these features through a detailed analysis of locally-acting (“cis”) eQTLs in 44 human tissues. Our analysis identifies more eQTLs than existing approaches, consistent with improved power. More importantly, although eQTLs are often shared broadly among tissues, our more quantitative approach highlights that effect sizes can vary considerably among tissues: some shared eQTLs show stronger effects in a subset of biologically-related tissues (e.g. brain-related tissues), or in only a single tissue (e.g. testis). Our methods are widely applicable, computationally tractable for many conditions, and available at https://github.com/stephenslab/mashr.