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.

A global overview of pleiotropy and genetic architecture in complex traits

An Author Correction to this article was published on 06 February 2020

This article has been updated


After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Trait-associated locus, gene and SNP pleiotropy across the genome.
Fig. 2: Within- and between-domain genetic correlations.
Fig. 3: Distribution and characterization of lead SNPs and credible SNPs of 558 traits.
Fig. 4: SNP heritability and polygenicity of 558 traits.

Data availability

All publicly available GWAS summary statistics (original) files curated in this study are accessible from the original links provided at GWAS summary statistics for 600 traits from UK Biobank performed in this study are also provided at and an archived file will be made available upon publication from

Change history

  • 06 February 2020

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


  1. 1.

    Edwards, A. O. et al. Complement factor H polymorphism and age-related macular degeneration. Science 308, 421–425 (2005).

    CAS  Article  Google Scholar 

  2. 2.

    Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    CAS  Article  Google Scholar 

  3. 3.

    Lander, E. S. Initial impact of the sequencing of the human genome. Nature 470, 187–197 (2011).

    CAS  Article  Google Scholar 

  4. 4.

    Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Henderson, P. & Stevens, C. The role of autophagy in Crohn’s disease. Cells 1, 492–519 (2012).

    Article  Google Scholar 

  6. 6.

    Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

    CAS  Article  Google Scholar 

  8. 8.

    Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    CAS  Article  Google Scholar 

  9. 9.

    Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J. & Richards, J. B. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat. Rev. Genet. 19, 110–124 (2018).

    CAS  Article  Google Scholar 

  10. 10.

    Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. & Visscher, P. M. Common disease is more complex than implied by the core gene omnigenic model. Cell 173, 1573–1580 (2018).

    CAS  Article  Google Scholar 

  12. 12.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Goh, K. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685–8690 (2007).

    CAS  Article  Google Scholar 

  14. 14.

    Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    CAS  Article  Google Scholar 

  15. 15.

    Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    CAS  Article  Google Scholar 

  16. 16.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  Google Scholar 

  17. 17.

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

    CAS  Article  Google Scholar 

  18. 18.

    Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

    CAS  Article  Google Scholar 

  19. 19.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    The GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  21. 21.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  Article  Google Scholar 

  23. 23.

    Lee, S., Abecasis, G. R., Boehnke, M. & Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95, 5–23 (2014).

    CAS  Article  Google Scholar 

  24. 24.

    van de Bunt, M., Cortes, A., Brown, M. A., Morris, A. P. & McCarthy, M. I. Evaluating the performance of fine-mapping strategies at common variant GWAS loci. PLoS Genet. 11, e1005535 (2015).

    Article  Google Scholar 

  25. 25.

    Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Speed, D. & Balding, D. J. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat. Genet. 51, 277–284 (2019).

    CAS  Article  Google Scholar 

  28. 28.

    Holland, D. et al. Beyond SNP heritability: polygenicity and discoverability estimated for multiple phenotypes with a univariate gaussian mixture model. Preprint at (2018).

  29. 29.

    Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019).

    Article  Google Scholar 

  30. 30.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  Article  Google Scholar 

  31. 31.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  Google Scholar 

  32. 32.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  33. 33.

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  Article  Google Scholar 

  34. 34.

    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  Article  Google Scholar 

  35. 35.

    Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in 700,000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  Google Scholar 

  37. 37.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  Google Scholar 

  38. 38.

    Visscher, P. M. et al. Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples. PLoS Genet. 10, e1004269 (2014).

    Article  Google Scholar 

  39. 39.

    Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).

    CAS  Article  Google Scholar 

  40. 40.

    Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  Google Scholar 

  41. 41.

    Tak, Y. G. & Farnham, P. J. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin 8, 57 (2015).

    Article  Google Scholar 

Download references


We thank all consortiums and all other individual laboratories for making GWAS summary statistics publicly available. We also thank P. Visscher and N. Wray for their thoughtful suggestions and discussions. We additionally thank A. Dale for his suggestions. This work was funded by the Netherlands Organization for Scientific Research (grant nos. NWO VICI 453-14-005 and NWO VIDI 452-12-014).

Author information




D.P. designed the study. K.W. curated the database and performed analyses. T.J.C.P. assisted with harmonization of phenotype labels of the database. S.S. performed quality control on the UK Biobank data and wrote the analysis pipeline for UKB analyses. M.U.M. assisted with the fine-mapping analyses. C.d.L. assisted with the discussion of SNP heritability estimates with different models. O.F. and O.A.A. developed software the MiXeR and assisted with the analyses. S.v.d.S. and B.M.N. discussed and provided valuable suggestions for analyses. K.W. and D.P. wrote the paper. All authors critically reviewed the paper.

Corresponding author

Correspondence to Danielle Posthuma.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Note and Supplementary Figs. 1–12

Reporting Summary

Supplementary Tables 1–26

Supplementary Tables 1–26

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Watanabe, K., Stringer, S., Frei, O. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet 51, 1339–1348 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing