Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.


  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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 (2017).

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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 (2017).

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

Authors and Affiliations




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, David Cesarini, Benjamin M. Neale or Daniel J. Benjamin.

Ethics declarations

Competing interests

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

Additional information

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.

Life Sciences Reporting Summary

Supplementary Tables

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

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research