Included in eqtl theme

Altmetric score 21 (top 5%)

Author: Sarah Margaret Urbut
Editor:
Research area: genomics
Type:

Open peer-review

Review content is open, signing review is optional.

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


Created on 15th February 2017

Sarah Margaret Urbut ; Gao Wang ; Matthew Stephens


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 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 (data from GTEx project). 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; transformed-fibroblasts). Our methods are widely applicable, computationally tractable for many conditions, and available at https://github.com/stephenslab/mashr.

Show more

Review Summary

This paper has 0 completed reviews and 0 reviews in progress.

# Status Date



Name:
Email: