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.

  • Subscribe to Nature Genetics for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.


  1. 1.

    Galesloot, T. E., van Steen, K., Kiemeney, L. A., Janss, L. L. & Vermeulen, S. H. A comparison of multivariate genome-wide association methods. PLoS One 9, e95923 (2014).

  2. 2.

    Porter, H. F. & O’Reilly, P. F. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci. Rep. 7, 38837 (2017).

  3. 3.

    Maier, R. et al. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am. J. Hum. Genet. 96, 283–294 (2015).

  4. 4.

    Hu, Y. et al. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet. 13, e1006836 (2017).

  5. 5.

    Baselmans, B.M.L. et al. Multivariate genome-wide and integrated transcriptome and epigenome-wide analyses of the well-being spectrum. Preprint at bioRxiv https://doi.org/10.1101/115915 (2017).

  6. 6.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  7. 7.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

  8. 8.

    Ripke, S. et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

  9. 9.

    de Moor, M. H. M. et al. Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder. JAMA Psychiatry 72, 642–650 (2015).

  10. 10.

    Smith, D. J. et al. Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci. Mol. Psychiatry 21, 749–757 (2016).

  11. 11.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

  12. 12.

    Bolormaa, S. et al. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet. 10, e1004198 (2014).

  13. 13.

    Zhu, X. et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am. J. Hum. Genet. 96, 21–36 (2015).

  14. 14.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  15. 15.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

  16. 16.

    Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

  17. 17.

    Südhof, T. C. The presynaptic active zone. Neuron 75, 11–25 (2012).

  18. 18.

    Pin, J.-P. & Bettler, B. Organization and functions of mGlu and GABAB receptor complexes. Nature 540, 60–68 (2016).

  19. 19.

    Traynelis, S. F. et al. Glutamate receptor ion channels: structure, regulation, and function. Pharmacol. Rev. 62, 405–496 (2010).

  20. 20.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

  21. 21.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  22. 22.

    Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

  23. 23.

    Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

  24. 24.

    Wientjes, Y. C. J., Bijma, P., Veerkamp, R. F. & Calus, M. P. L. An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments. Genetics 202, 799–823 (2016).

  25. 25.

    Lee, S.H., Clark, S. & van der Werf, J. Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship. Preprint at bioRxiv https://doi.org/10.1101/119164 (2017).

  26. 26.

    Lee, S. H., Weerasinghe, W. M. S. P., Wray, N. R., Goddard, M. E. & van der Werf, J. H. J. Using information of relatives in genomic prediction to apply effective stratified medicine. Sci. Rep. 7, 42091 (2017).

  27. 27.

    Sonnega, A. et al. Cohort profile: the Health and Retirement Study (HRS). Int. J. Epidemiol. 43, 576–585 (2014).

  28. 28.

    Daetwyler, H. D., Villanueva, B. & Woolliams, J. A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395 (2008).

  29. 29.

    Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

  30. 30.

    Pasaniuc, B. & Price, A. L. Dissecting the genetics of complex traits using summary association statistics. Nat. Rev. Genet. 18,117–127 (2017).

Download references


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

Author notes

  1. A list of members and affiliations appears at the end the paper.

  2. A list of members and affiliations appears in the Supplementary Note.

  3. Patrick Turley, David Cesarini, Benjamin M. Neale and Daniel J. Benjamin contributed equally to this work.


  1. Broad Institute, Cambridge, MA, USA

    • Patrick Turley
    • , Raymond K. Walters
    •  & Benjamin M. Neale
  2. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA

    • Patrick Turley
    • , Raymond K. Walters
    •  & Benjamin M. Neale
  3. Department of Economics, Harvard University, Cambridge, MA, USA

    • Omeed Maghzian
    •  & David Laibson
  4. Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    • Aysu Okbay
  5. Department of Psychology, University of Minnesota, Minneapolis, MN, USA

    • James J. Lee
  6. Hospital for Special Surgery, New York, NY, USA

    • Mark Alan Fontana
  7. Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA

    • Tuan Anh Nguyen-Viet
    •  & Daniel J. Benjamin
  8. Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA

    • Robbee Wedow
  9. Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA

    • Robbee Wedow
  10. Department of Sociology, University of Colorado Boulder, Boulder, CO, USA

    • Robbee Wedow
  11. Department of Sociology, Harvard University, Cambridge, MA, USA

    • Meghan Zacher
  12. 23andMe, Inc., Mountain View, CA, USA

    • Nicholas A. Furlotte
    • , 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
    •  & Steven J. Pitts
  13. Institutionen för Medicinsk Epidemiologi och Biostatistik, Karolinska Institutet, Stockholm, Sweden

    • Patrik Magnusson
  14. Department of Government, Uppsala Universitet, Uppsala, Sweden

    • Sven Oskarsson
  15. Department of Economics, Stockholm School of Economics, Stockholm, Sweden

    • Magnus Johannesson
  16. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    • Peter M. Visscher
  17. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    • Peter M. Visscher
  18. National Bureau of Economic Research, Cambridge, MA, USA

    • David Laibson
    • , David Cesarini
    •  & Daniel J. Benjamin
  19. Department of Economics and Center for Experimental Social Science, New York University, New York, NY, USA

    • David Cesarini
  20. Institutet för Näringslivsforskning, Stockholm, Sweden

    • David Cesarini
  21. Department of Economics, University of Southern California, Los Angeles, CA, USA

    • Daniel J. Benjamin


  1. Search for Patrick Turley in:

  2. Search for Raymond K. Walters in:

  3. Search for Omeed Maghzian in:

  4. Search for Aysu Okbay in:

  5. Search for James J. Lee in:

  6. Search for Mark Alan Fontana in:

  7. Search for Tuan Anh Nguyen-Viet in:

  8. Search for Robbee Wedow in:

  9. Search for Meghan Zacher in:

  10. Search for Nicholas A. Furlotte in:

  11. Search for Patrik Magnusson in:

  12. Search for Sven Oskarsson in:

  13. Search for Magnus Johannesson in:

  14. Search for Peter M. Visscher in:

  15. Search for David Laibson in:

  16. Search for David Cesarini in:

  17. Search for Benjamin M. Neale in:

  18. Search for Daniel J. Benjamin in:


  1. 23andMe Research Team

  1. Social Science Genetic Association Consortium


    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.

    Competing interests

    The authors declare no competing financial interests.

    Corresponding authors

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

    Supplementary information

    1. Supplementary Text and Figures

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

    2. Life Sciences Reporting Summary

    3. Supplementary Tables

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