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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Vydané v:	Nature genetics Ročník 51; číslo 1; s. 187 - 195
Hlavní autori:	Urbut, Sarah M., Wang, Gao, Carbonetto, Peter, Stephens, Matthew
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.01.2019 Nature Publishing Group
Predmet:	38 45 45/23 45/43 45/91 631/208/212 639/705/531 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
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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 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.
AbstractList	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.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. 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. 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. 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.
Author	Stephens, Matthew Urbut, Sarah M. Wang, Gao Carbonetto, Peter
AuthorAffiliation	2 Department of Human Genetics, University of Chicago, Chicago, IL, USA 1 Pritzker School of Medicine, Growth & Development Training Program, University of Chicago, Chicago, IL, USA 4 Research Computing Center, University of Chicago, Chicago, IL, USA 3 Department of Statistics, University of Chicago, Chicago, IL, USA
AuthorAffiliation_xml	– name: 3 Department of Statistics, University of Chicago, Chicago, IL, USA – name: 1 Pritzker School of Medicine, Growth & Development Training Program, University of Chicago, Chicago, IL, USA – name: 2 Department of Human Genetics, University of Chicago, Chicago, IL, USA – name: 4 Research Computing Center, University of Chicago, Chicago, IL, USA
Author_xml	– sequence: 1 givenname: Sarah M. orcidid: 0000-0002-1135-9647 surname: Urbut fullname: Urbut, Sarah M. organization: Pritzker School of Medicine, Growth & Development Training Program, University of Chicago, Department of Human Genetics, University of Chicago – sequence: 2 givenname: Gao orcidid: 0000-0001-9336-402X surname: Wang fullname: Wang, Gao organization: Department of Human Genetics, University of Chicago – sequence: 3 givenname: Peter orcidid: 0000-0003-1144-6780 surname: Carbonetto fullname: Carbonetto, Peter organization: Department of Human Genetics, University of Chicago, Research Computing Center, University of Chicago – sequence: 4 givenname: Matthew orcidid: 0000-0001-5397-9257 surname: Stephens fullname: Stephens, Matthew email: mstephens@uchicago.edu organization: Department of Human Genetics, University of Chicago, Department of Statistics, University of Chicago
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/30478440$$D View this record in MEDLINE/PubMed
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Copyright	The Author(s), under exclusive licence to Springer Nature America, Inc. 2018 Copyright Nature Publishing Group Jan 2019
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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.
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Snippet	We introduce new statistical methods for analyzing genomic data sets that measure many effects in many conditions (for example, gene expression changes under... We introduce new statistical methods for analyzing genomic datasets that measure many effects in many conditions (e.g., gene expression changes under many...
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SubjectTerms	38 45 45/23 45/43 45/91 631/208/212 639/705/531 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
Title	Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions
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