Multi-trait analysis of genome-wide association summary statistics using MTAG

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms ( N eff = 354,862), neuroticism ( N = ...

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Vydáno v:	Nature genetics Ročník 50; číslo 2; s. 229 - 237
Hlavní autoři:	Turley, Patrick, Walters, Raymond K., Maghzian, Omeed, Okbay, Aysu, Lee, James J., Fontana, Mark Alan, Nguyen-Viet, Tuan Anh, Wedow, Robbee, Zacher, Meghan, Furlotte, Nicholas A., Magnusson, Patrik, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M., Laibson, David, Cesarini, David, Neale, Benjamin M., Benjamin, Daniel J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.02.2018 Nature Publishing Group
Témata:	631/208/205/2138 631/208/212 631/378 Accuracy Agriculture Algorithms Animal Genetics and Genomics Bioinformatics Biomedical and Life Sciences Biomedicine Cancer Research Comparative analysis Computational biology Data Interpretation, Statistical Datasets as Topic - statistics & numerical data Depression - epidemiology Depression - genetics Diagnostic Self Evaluation Economic models Gene Function Genetic aspects Genetic Association Studies - methods Genetic Association Studies - statistics & numerical data Genome-wide association studies Genome-Wide Association Study - methods Genome-Wide Association Study - statistics & numerical data Genomes Genomics Health - statistics & numerical data Human Genetics Humans Loci Mental depression Meta-analysis Meta-Analysis as Topic Multifactorial Inheritance Neurosis Neuroticism Phenotype Polygenic inheritance Polymorphism, Single Nucleotide Psychological factors Quantitative Trait Loci - genetics Statistical analysis Statistical methods Statistics Well being Sweden
ISSN:	1061-4036, 1546-1718, 1546-1718
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Abstract	We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms ( N eff = 354,862), neuroticism ( N = 168,105), and subjective well-being ( N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. MTAG is a new method for joint analysis of summary statistics from genome-wide association studies of different traits. Applying MTAG to summary statistics for depressive symptoms, neuroticism and subjective well-being increased discovery of associated loci as compared to single-trait analyses.
AbstractList	We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations. We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N.sub.eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms ( N eff = 354,862), neuroticism ( N = 168,105), and subjective well-being ( N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. MTAG is a new method for joint analysis of summary statistics from genome-wide association studies of different traits. Applying MTAG to summary statistics for depressive symptoms, neuroticism and subjective well-being increased discovery of associated loci as compared to single-trait analyses. We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N.sub.eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. MTAG is a new method for joint analysis of summary statistics from genome-wide association studies of different traits. Applying MTAG to summary statistics for depressive symptoms, neuroticism and subjective well-being increased discovery of associated loci as compared to single-trait analyses. We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff= 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N-eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.
Audience	Academic
Author	Wedow, Robbee Walters, Raymond K. Visscher, Peter M. Neale, Benjamin M. Johannesson, Magnus Turley, Patrick Furlotte, Nicholas A. Maghzian, Omeed Cesarini, David Magnusson, Patrik Nguyen-Viet, Tuan Anh Laibson, David Oskarsson, Sven Benjamin, Daniel J. Lee, James J. Okbay, Aysu Fontana, Mark Alan Zacher, Meghan
AuthorAffiliation	10 Department of Sociology, University of Colorado Boulder, Boulder, Colorado, United States 15 Institutionen för Medicinsk Epidemiologi och Biostatistik, Karolinska Institutet, Stockholm, Sweden 6 Hospital for Special Surgery, New York, New York, United States 17 Department of Economics, Stockholm School of Economics, Stockholm, Sweden 21 Department of Economics and Center for Experimental Social Science, New York University, New York, New York, United States 18 Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia 19 Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia 7 Center for Economic and Social Research, University of Southern California, Los Angeles, California, United States 16 Department of Government, Uppsala Universitet, Uppsala, Sweden 22 Institutet för Näringslivsforskning, Stockholm, Sweden 12 23andMe, Inc., Mountain View, California, United States 3 Department of Economics, Harvard University, Cambr
AuthorAffiliation_xml	– name: 19 Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia – name: 21 Department of Economics and Center for Experimental Social Science, New York University, New York, New York, United States – name: 22 Institutet för Näringslivsforskning, Stockholm, Sweden – name: 9 Institute of Behavioral Science, University of Colorado Boulder, Boulder, Colorado, United States – name: 15 Institutionen för Medicinsk Epidemiologi och Biostatistik, Karolinska Institutet, Stockholm, Sweden – name: 4 Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands – name: 20 National Bureau of Economic Research, Cambridge, Massachusetts, United States – name: 23 Department of Economics, University of Southern California, Los Angeles, California, United States – name: 7 Center for Economic and Social Research, University of Southern California, Los Angeles, California, United States – name: 5 Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States – name: 17 Department of Economics, Stockholm School of Economics, Stockholm, Sweden – name: 3 Department of Economics, Harvard University, Cambridge, Massachusetts, United States – name: 12 23andMe, Inc., Mountain View, California, United States – name: 8 Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States – name: 1 Broad Institute, Cambridge, Massachusetts, United States – name: 10 Department of Sociology, University of Colorado Boulder, Boulder, Colorado, United States – name: 18 Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia – name: 2 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, Massachusetts, United States – name: 11 Department of Sociology, Harvard University, Cambridge, Massachusetts, United States – name: 6 Hospital for Special Surgery, New York, New York, United States – name: 16 Department of Government, Uppsala Universitet, Uppsala, Sweden
Author_xml	– sequence: 1 givenname: Patrick surname: Turley fullname: Turley, Patrick email: paturley@broadinstitute.org organization: Broad Institute, Analytic and Translational Genetics Unit, Massachusetts General Hospital – sequence: 2 givenname: Raymond K. orcidid: 0000-0001-8422-6530 surname: Walters fullname: Walters, Raymond K. organization: Broad Institute, Analytic and Translational Genetics Unit, Massachusetts General Hospital – sequence: 3 givenname: Omeed surname: Maghzian fullname: Maghzian, Omeed organization: Department of Economics, Harvard University – sequence: 4 givenname: Aysu surname: Okbay fullname: Okbay, Aysu organization: Department of Complex Trait Genetics, Vrije Universiteit Amsterdam – sequence: 5 givenname: James J. orcidid: 0000-0001-6547-5128 surname: Lee fullname: Lee, James J. organization: Department of Psychology, University of Minnesota – sequence: 6 givenname: Mark Alan surname: Fontana fullname: Fontana, Mark Alan organization: Hospital for Special Surgery – sequence: 7 givenname: Tuan Anh surname: Nguyen-Viet fullname: Nguyen-Viet, Tuan Anh organization: Center for Economic and Social Research, University of Southern California – sequence: 8 givenname: Robbee orcidid: 0000-0002-3108-7087 surname: Wedow fullname: Wedow, Robbee organization: Institute for Behavioral Genetics, University of Colorado Boulder, Institute of Behavioral Science, University of Colorado Boulder, Department of Sociology, University of Colorado Boulder – sequence: 9 givenname: Meghan surname: Zacher fullname: Zacher, Meghan organization: Department of Sociology, Harvard University – sequence: 10 givenname: Nicholas A. surname: Furlotte fullname: Furlotte, Nicholas A. organization: 23andMe, Inc – sequence: 13 givenname: Patrik orcidid: 0000-0002-7315-7899 surname: Magnusson fullname: Magnusson, Patrik organization: Institutionen för Medicinsk Epidemiologi och Biostatistik, Karolinska Institutet – sequence: 14 givenname: Sven surname: Oskarsson fullname: Oskarsson, Sven organization: Department of Government, Uppsala Universitet – sequence: 15 givenname: Magnus orcidid: 0000-0001-8759-6393 surname: Johannesson fullname: Johannesson, Magnus organization: Department of Economics, Stockholm School of Economics – sequence: 16 givenname: Peter M. orcidid: 0000-0002-2143-8760 surname: Visscher fullname: Visscher, Peter M. organization: Institute for Molecular Bioscience, University of Queensland, Queensland Brain Institute, University of Queensland – sequence: 17 givenname: David surname: Laibson fullname: Laibson, David organization: Department of Economics, Harvard University, National Bureau of Economic Research – sequence: 18 givenname: David surname: Cesarini fullname: Cesarini, David email: dac12@nyu.edu organization: National Bureau of Economic Research, Department of Economics and Center for Experimental Social Science, New York University, Institutet för Näringslivsforskning – sequence: 19 givenname: Benjamin M. surname: Neale fullname: Neale, Benjamin M. email: bneale@broadinstitute.org organization: Broad Institute, Analytic and Translational Genetics Unit, Massachusetts General Hospital – sequence: 20 givenname: Daniel J. orcidid: 0000-0002-2642-5416 surname: Benjamin fullname: Benjamin, Daniel J. email: daniel.benjamin@gmail.com organization: Center for Economic and Social Research, University of Southern California, National Bureau of Economic Research, Department of Economics, University of Southern California
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29292387$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-347099$$DView record from Swedish Publication Index (Uppsala universitet) https://research.hhs.se/esploro/outputs/journalArticle/Multi-trait-analysis-of-genome-wide-association-summary/991001480485506056$$DView record from Swedish Publication Index http://kipublications.ki.se/Default.aspx?queryparsed=id:137625690$$DView record from Swedish Publication Index (Karolinska Institutet)
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Copyright	The Author(s) 2018 COPYRIGHT 2018 Nature Publishing Group Copyright Nature Publishing Group Feb 2018
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-3 A list of members of the Social Science Genetic Association Consortium can be found in section 10 of Supplementary Note. A list of members of the 23andMe Research Team can be found at the end the paper. CONTRIBUTOR LIST FOR THE 23andMe RESEARCH TEAM: Michelle Agee, Babak Alipanahi, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, David A. Hinds, Bethann S. Hromatka, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Matthew H. McIntyre, Joanna L. Mountain, Carrie A.M. Northover, J. Fah Sathirapongsasuti, Olga V. Sazonova, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Vladimir Vacic, Catherine H. Wilson, and Steven J. Pitts. These authors contributed equally
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Snippet	We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different... We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping...
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Title	Multi-trait analysis of genome-wide association summary statistics using MTAG
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