Variance component model to account for sample structure in genome-wide association studies

Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly available and may be used for analysis of genome-wide association study datasets. Although genome-wide association studies (GWASs) have identified...

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Bibliographic Details
Published in:	Nature genetics Vol. 42; no. 4; pp. 348 - 354
Main Authors:	Kang, Hyun Min, Sul, Jae Hoon, Service, Susan K, Zaitlen, Noah A, Kong, Sit-yee, Freimer, Nelson B, Sabatti, Chiara, Eskin, Eleazar
Format:	Journal Article
Language:	English
Published:	New York Nature Publishing Group US 01.04.2010 Nature Publishing Group
Subjects:	631/1647/2217/2138 Agriculture Animal Genetics and Genomics Biomedical and Life Sciences Biomedicine Cancer Research Gene Function Genetics Genome-Wide Association Study Genomics Genotype & phenotype Human Genetics Humans Identification and classification Methods Models, Genetic Models, Statistical Physiological aspects Polymorphism, Single Nucleotide Population Groups - genetics Principal Component Analysis Principal components analysis Quantitative Trait Loci Software Studies technical-report United States
ISSN:	1061-4036, 1546-1718, 1546-1718
Online Access:	Get full text
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Abstract	Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly available and may be used for analysis of genome-wide association study datasets. Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
AbstractList	Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure. [PUBLICATION ABSTRACT] Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure. Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure. Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly available and may be used for analysis of genome-wide association study datasets. Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
Audience	Academic
Author	Sul, Jae Hoon Service, Susan K Zaitlen, Noah A Sabatti, Chiara Kong, Sit-yee Freimer, Nelson B Eskin, Eleazar Kang, Hyun Min
AuthorAffiliation	3 Computer Science Department, University of California, Los Angeles, California, USA 1 Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA 4 Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA 5 Department of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA 6 Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA 7 Department of Human Genetics, University of California, Los Angeles, California, USA 2 Center for Computational Medicine and Bioinformatics, The University of Michigan Medical School, Ann Arbor, Michigan, USA
AuthorAffiliation_xml	– name: 6 Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA – name: 3 Computer Science Department, University of California, Los Angeles, California, USA – name: 2 Center for Computational Medicine and Bioinformatics, The University of Michigan Medical School, Ann Arbor, Michigan, USA – name: 4 Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA – name: 7 Department of Human Genetics, University of California, Los Angeles, California, USA – name: 1 Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA – name: 5 Department of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
Author_xml	– sequence: 1 givenname: Hyun Min surname: Kang fullname: Kang, Hyun Min organization: Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Center for Computational Medicine and Bioinformatics, The University of Michigan Medical School – sequence: 2 givenname: Jae Hoon surname: Sul fullname: Sul, Jae Hoon organization: Computer Science Department, University of California – sequence: 3 givenname: Susan K surname: Service fullname: Service, Susan K organization: Center for Neurobehavioral Genetics, University of California – sequence: 4 givenname: Noah A surname: Zaitlen fullname: Zaitlen, Noah A organization: Department of Epidemiology and Biostatistics, Harvard School of Public Health – sequence: 5 givenname: Sit-yee surname: Kong fullname: Kong, Sit-yee organization: Center for Neurobehavioral Genetics, University of California – sequence: 6 givenname: Nelson B surname: Freimer fullname: Freimer, Nelson B organization: Center for Neurobehavioral Genetics, University of California – sequence: 7 givenname: Chiara surname: Sabatti fullname: Sabatti, Chiara email: sabatti@stanford.edu organization: Department of Health Research and Policy, Stanford University School of Medicine – sequence: 8 givenname: Eleazar surname: Eskin fullname: Eskin, Eleazar email: eeskin@cs.ucla.edu organization: Computer Science Department, University of California, Department of Human Genetics, University of California
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/20208533$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	Springer Nature America, Inc. 2010 COPYRIGHT 2010 Nature Publishing Group Copyright Nature Publishing Group Apr 2010 2010 Nature America, Inc. All rights reserved. 2010
Copyright_xml	– notice: Springer Nature America, Inc. 2010 – notice: COPYRIGHT 2010 Nature Publishing Group – notice: Copyright Nature Publishing Group Apr 2010 – notice: 2010 Nature America, Inc. All rights reserved. 2010
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Snippet	Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly... Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness...
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SubjectTerms	631/1647/2217/2138 Agriculture Animal Genetics and Genomics Biomedical and Life Sciences Biomedicine Cancer Research Gene Function Genetics Genome-Wide Association Study Genomics Genotype & phenotype Human Genetics Humans Identification and classification Methods Models, Genetic Models, Statistical Physiological aspects Polymorphism, Single Nucleotide Population Groups - genetics Principal Component Analysis Principal components analysis Quantitative Trait Loci Software Studies technical-report
Title	Variance component model to account for sample structure in genome-wide association studies
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