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

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 KE; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M; Laibson, David I; Cesarini, David; Neale, Benjamin M.; Benjamin, Daniel J.

doi:10.1038/s41588-017-0009-4

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

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Author(s): Patrick Turley ¹ ^, ² , Raymond K. Walters ¹ ^, ² , Omeed Maghzian ³ , Aysu Okbay ⁴ , James J. Lee ⁵ , Mark Alan Fontana ⁶ , Tuan Anh Nguyen-Viet ⁷ , Robbee Wedow ⁸ ^, ⁹ ^, ¹⁰ , Meghan Zacher ¹¹ , Nicholas A. Furlotte ¹² , 23andMe Research Team, Social Science Genetic Association Consortium, Patrik Magnusson ¹⁵ , Sven Oskarsson ¹⁶ , Magnus Johannesson ¹⁷ , Peter M. Visscher ¹⁸ ^, ¹⁹ , David Laibson ³ ^, ²⁰ , David Cesarini ²⁰ ^, ²¹ ^, ²² , Benjamin M. Neale ¹ ^, ² , Daniel J. Benjamin ⁷ ^, ²⁰ ^, ²³

Publication date (Electronic): 01 January 2018

Journal: Nature genetics

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Abstract

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 ( N _eff = 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.

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Most cited references 16

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Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Hans D Daetwyler, Beatriz Villanueva, John A. Woolliams (2008)

Background The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.

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Identification of 15 genetic loci associated with risk of major depression in individuals of European descent

Craig L. Hyde, Mike Nagle, Chao Tian … (2016)

Despite strong evidence supporting the heritability of Major Depressive Disorder, previous genome-wide studies were unable to identify risk loci among individuals of European descent. We used self-reported data from 75,607 individuals reporting clinical diagnosis of depression and 231,747 reporting no history of depression through 23andMe, and meta-analyzed these results with published MDD GWAS results. We identified five independent variants from four regions associated with self-report of clinical diagnosis or treatment for depression. Loci with pval<1.0×10−5 in the meta-analysis were further analyzed in a replication dataset (45,773 cases and 106,354 controls) from 23andMe. A total of 17 independent SNPs from 15 regions reached genome-wide significance after joint-analysis over all three datasets. Some of these loci were also implicated in GWAS of related psychiatric traits. These studies provide evidence for large-scale consumer genomic data as a powerful and efficient complement to traditional means of ascertainment for neuropsychiatric disease genomics.

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Dissecting the genetics of complex traits using summary association statistics

Bogdan Pasaniuc, Alkes L. Price (2016)

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat. Genet.

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 7 November 2017

Publication date (Electronic): 01 January 2018

Publication date (Print): February 2018

Publication date PMC-release: 01 July 2018

Volume: 50

Issue: 2

Pages: 229-237

Affiliations

[1 ]Broad Institute, Cambridge, Massachusetts, United States

[2 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, Massachusetts, United States

[3 ]Department of Economics, Harvard University, Cambridge, Massachusetts, United States

[4 ]Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

[5 ]Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States

[6 ]Hospital for Special Surgery, New York, New York, United States

[7 ]Center for Economic and Social Research, University of Southern California, Los Angeles, California, United States

[8 ]Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States

[9 ]Institute of Behavioral Science, University of Colorado Boulder, Boulder, Colorado, United States

[10 ]Department of Sociology, University of Colorado Boulder, Boulder, Colorado, United States

[11 ]Department of Sociology, Harvard University, Cambridge, Massachusetts, United States

[12 ]23andMe, Inc., Mountain View, California, United States

[15 ]Institutionen för Medicinsk Epidemiologi och Biostatistik, Karolinska Institutet, Stockholm, Sweden

[16 ]Department of Government, Uppsala Universitet, Uppsala, Sweden

[17 ]Department of Economics, Stockholm School of Economics, Stockholm, Sweden

[18 ]Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia

[19 ]Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia

[20 ]National Bureau of Economic Research, Cambridge, Massachusetts, United States

[21 ]Department of Economics and Center for Experimental Social Science, New York University, New York, New York, United States

[22 ]Institutet för Näringslivsforskning, Stockholm, Sweden

[23 ]Department of Economics, University of Southern California, Los Angeles, California, United States

Author notes

Correspondence to P.T. ( paturley@ 123456broadinstitute.org ), B.M.N. ( bneale@ 123456broadinstitute.org ), D.C. ( dac12@ 123456nyu.edu ), or D.J.B. ( daniel.benjamin@ 123456gmail.com )

[*]

These authors contributed equally

[13]

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.

[14]

A list of members of the Social Science Genetic Association Consortium can be found in section 10 of Supplementary Note.

Article

Manuscript ID: NIHMS918509

DOI: 10.1038/s41588-017-0009-4

PMC ID: 5805593

PubMed ID: 29292387

SO-VID: bb5f0c67-8e29-433a-b3e9-8b16de093bc5

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

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Most cited references 16

Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Identification of 15 genetic loci associated with risk of major depression in individuals of European descent

Dissecting the genetics of complex traits using summary association statistics

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