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

An Author Correction to this article was published on 25 June 2019

A Publisher Correction to this article was published on 30 May 2019

This article has been updated


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.

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Fig. 1: Bias in standard errors from ignoring sampling variation in \(\hat{{\boldsymbol{\Sigma }}}\) and
Fig. 2: Evaluation of MTAG’s standard error when there is sample overlap.
Fig. 3: Cohorts included in GWAS meta-analyses for DEP, NEUR, and SWB.
Fig. 4: Manhattan plots of GWAS and MTAG results.
Fig. 5: Regression-based test of the replicability of MTAG-identified loci.
Fig. 6: Predictive power of GWAS- and MTAG-based polygenic scores.
Fig. 7: Biological annotation for DEP using the bioinformatics tool DEPICT.

Change history

  • 25 June 2019

    In the version of the paper initially published, no competing interests were declared. The ‘Competing interests’ statement should have stated that B.M.N. is on the Scientific Advisory Board of Deep Genomics. The error has been corrected in the HTML and PDF versions of the article.

  • 30 May 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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We thank J. Beauchamp, P. Koellinger, Ö. Sandewall, C. Shulman, and R. de Vlaming for helpful comments and P. Bowers, E. Kong, T. Kundu, S. Lee, H. Li, R. Li, and R. Royer for research assistance. This research was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC). The study was supported by the Ragnar Söderberg Foundation (E9/11, M.J.; E42/15, D.C.), the Swedish Research Council (421-2013-1061, M.J.), the Jan Wallander and Tom Hedelius Foundation (M.J.), an ERC Consolidator Grant (647648 EdGe, P. Koellinger), the Pershing Square Fund of the Foundations of Human Behavior (D.L.), the National Science Foundation’s Graduate Research Fellowship Program (DGE 1144083, R.W.), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, and T32-AG000186-23 to D. Wise at NBER; P30-AG012810 (D.L.) to NBER; R01-AG042568-02 (D.J.B.) to the University of Southern California; and 1R01-MH107649-03 (B.M.N.), 1R01-MH101244-02 (B.M.N.), and 5U01-MH109539-02 (B.M.N.) to the Broad Institute at Harvard and MIT. This research has also been conducted using the UK Biobank Resource under application number 11425. We thank the research participants and employees of 23andMe for making this work possible. We also thank K. Mullan Harris and Add Health for early access to the data used in our replication and prediction analyses. A full list of acknowledgements is provided in the Supplementary Note.

Author information





B.M.N., D.J.B., D.C., and P.T. oversaw the study. The theory underlying MTAG was conceived of and developed by P.T., with contributions from B.M.N., D.J.B., D.C., D.L., O.M., P.M.V., and R.K.W. O.M., P.T., and R.K.W. performed the simulations and developed the MTAG software. P.T. and P.M.V. designed the analyses comparing the observed MTAG gains to theoretical expectations. A.O., M.Z., R.W., M.A.F., O.M., and T.A.N.-V. had major roles in data analyses. J.J.L. designed and executed the bioinformatics analyses. N.A.F. conducted the analysis of the data from 23andMe. D.J.B., D.C., and P.T. coordinated the writing of the manuscript. P.M., S.O., and M.J. also contributed to the writing. All authors provided input and revisions for the final manuscript.

Corresponding authors

Correspondence to Patrick Turley or David Cesarini or Benjamin M. Neale or Daniel J. Benjamin.

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Competing interests

B.M.N. is on the Scientific Advisory Board of Deep Genomics.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

23andMe Research Team:

Michelle Agee12, Babak Alipanahi12, Adam Auton12, Robert K. Bell12, Katarzyna Bryc12, Sarah L. Elson12, Pierre Fontanillas12, Nicholas A. Furlotte12, David A. Hinds12, Bethann S. Hromatka12, Karen E. Huber12, Aaron Kleinman12, Nadia K. Litterman12, Matthew H. McIntyre12, Joanna L. Mountain12, Carrie A. M. Northover12, J. Fah Sathirapongsasuti12, Olga V. Sazonova12, Janie F. Shelton12, Suyash Shringarpure12, Chao Tian12, Joyce Y. Tung12, Vladimir Vacic12, Catherine H. Wilson12 and Steven J. Pitts12

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 4, 6–8, 10–13, 15–18, 30 and 31, and Supplementary Note.

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Supplementary Tables 1–3, 5, 9, 14 and 19–29.

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Turley, P., Walters, R.K., Maghzian, O. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 50, 229–237 (2018).

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