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

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...

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Bibliographic Details
Published in:Nature genetics Vol. 51; no. 1; pp. 187 - 195
Main Authors: Urbut, Sarah M., Wang, Gao, Carbonetto, Peter, Stephens, Matthew
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.01.2019
Nature Publishing Group
Subjects:
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Agriculture
Animal Genetics and Genomics
Assessments
Biomedical and Life Sciences
Biomedicine
Brain
Cancer Research
Data processing
Estimates
Gene expression
Gene Expression - genetics
Gene Expression Profiling - statistics & numerical data
Gene Expression Regulation - genetics
Gene Function
Gene mapping
Genetic effects
Genetics
Genomics - statistics & numerical data
Heterogeneity
Human Genetics
Human tissues
Humans
Polymorphism, Single Nucleotide - genetics
Power
Principal components analysis
Quantitative trait loci
Quantitative Trait Loci - genetics
Statistical methods
Statistics
technical-report
Tissue analysis
Tissues
ISSN:1061-4036, 1546-1718, 1546-1718
Online Access:Get full text
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Description
Summary: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 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 (for example, brain-related tissues), or in only one tissue (for example, testis). Our methods are widely applicable, computationally tractable for many conditions and available online. Multivariate adaptive shrinkage (mash) is a method for estimating and testing multiple effects in multiple conditions. When applied to GTEx data, mash can be used to analyze sharing of eQTL effects by examining variation in effect sizes.
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Author contributions S.M.U. and M.S. conceived of the project and developed the statistical methods. S.M.U. implemented the comparisons with simulated data. S.M.U. and G.W. performed the analyses of the GTEx data, and additional analyses. S.M.U., G.W. and M.S. implemented the software, with contributions from P.C. S.M.U. and M.S. wrote the manuscript, with input from G.W. and P.C. P.C. and G.W. prepared the online code and data resources.
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-018-0268-8