There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10−4) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10−3). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10−4) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).

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  1. 1.

    et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607–618 (2011).

  2. 2.

    et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).

  3. 3.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  4. 4.

    et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47, 839–846 (2015).

  5. 5.

    , , , & Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism–derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

  6. 6.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

  7. 7.

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

  8. 8.

    et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet. 9, e1003087 (2013).

  9. 9.

    & A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

  10. 10.

    et al. Analysis of families in the Multiple Autoimmune Disease Genetics Consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes. Am. J. Hum. Genet. 76, 561–571 (2005).

  11. 11.

    , , , & Major depression and generalized anxiety disorder. Same genes, (partly) different environments? Arch. Gen. Psychiatry 49, 716–722 (1992).

  12. 12.

    , & Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 17, 1520–1528 (2007).

  13. 13.

    International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

  14. 14.

    et al. New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis. Int. J. Epidemiol. 44, 1706–1721 (2015).

  15. 15.

    et al. Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat. Neurosci. 18, 953–955 (2015).

  16. 16.

    , , , & Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

  17. 17.

    , & Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Eur. J. Hum. Genet. 20, 668–674 (2012).

  18. 18.

    et al. Diagnostic misclassification reduces the ability to detect linkage in inflammatory bowel disease genetic studies. Gut 49, 773–776 (2001).

  19. 19.

    et al. Value of anti–modified citrullinated vimentin and third-generation anti–cyclic citrullinated peptide compared with second-generation anti–cyclic citrullinated peptide and rheumatoid factor in predicting disease outcome in undifferentiated arthritis and rheumatoid arthritis. Arthritis Rheum. 60, 2232–2241 (2009).

  20. 20.

    , & All you wanted to know about anti-CCP but were afraid to ask. Autoimmun. Rev. 10, 90–93 (2010).

  21. 21.

    et al. Diagnostic shifts during the decade following first admission for psychosis. Am. J. Psychiatry 168, 1186–1194 (2011).

  22. 22.

    et al. Subtypes of medulloblastoma have distinct developmental origins. Nature 468, 1095–1099 (2010).

  23. 23.

    , & Implications of comorbidity and ascertainment bias for identifying disease genes. Am. J. Med. Genet. 96, 817–822 (2000).

  24. 24.

    , , & The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).

  25. 25.

    & Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10, 74–81 (2014).

  26. 26.

    & The genetics of major depression. Neuron 81, 484–503 (2014).

  27. 27.

    & Heterogeneity of autoimmune diseases: pathophysiologic insights from genetics and implications for new therapies. Nat. Med. 21, 730–738 (2015).

  28. 28.

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

  29. 29.

    et al. Genetic variants at CD28, PRDM1 and CD2/CD58 are associated with rheumatoid arthritis risk. Nat. Genet. 41, 1313–1318 (2009).

  30. 30.

    et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).

  31. 31.

    International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).

  32. 32.

    et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N. Engl. J. Med. 359, 2767–2777 (2008).

  33. 33.

    et al. A meta-analysis of genome-wide association scans identifies IL18RAP, PTPN2, TAGAP, and PUS10 as shared risk loci for Crohn's disease and celiac disease. PLoS Genet. 7, e1001283 (2011).

  34. 34.

    et al. Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci. PLoS Genet. 7, e1002004 (2011).

  35. 35.

    et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

  36. 36.

    & Immune-mediated disease genetics: the shared basis of pathogenesis. Trends Immunol. 34, 22–26 (2013).

  37. 37.

    et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).

  38. 38.

    et al. Fine mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity. Am. J. Hum. Genet. 94, 522–532 (2014).

  39. 39.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  40. 40.

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

  41. 41.

    & Genetic basis of complex genetic disease: the contribution of disease heterogeneity to missing heritability. Curr. Epidemiol. Rep. 1, 220–227 (2014).

  42. 42.

    An asymptotic χ2 test for the equality of two correlation matrices. J. Am. Stat. Assoc. 65, 904–912 (1970).

  43. 43.

    , & Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J. Am. Stat. Assoc. 84, 1065–1073 (1989).

  44. 44.

    & Meta-analysis of genome-wide association studies with overlapping subjects. Am. J. Hum. Genet. 85, 862–872 (2009).

  45. 45.

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

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This work was supported in part by funding from the US National Institutes of Health (NIH) (1R01AR063759 (S.R.), 1R01AR062886 (S.R.), 1UH2AR067677-01 (S.R.), and U19AI111224-01 (S.R.)) and Doris Duke Charitable Foundation grant 2013097. B.H. is supported by the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea (2016-0717) and the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI14C1731). J.G.P. is supported by Fulbright Canada, the Weston Foundation, and Brain Canada through the Canada Brain Research Fund. K.S. is supported by an NIH training grant (T32HG002295). N.R.W. is supported by the Australian National Health and Medical Research Council (1087889 and 1078901). This research uses resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF) and supported by grant U01DK062418.

Author information

Author notes

    • Buhm Han
    •  & Jennie G Pouget

    These authors contributed equally to this work.


  1. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Buhm Han
    • , Jennie G Pouget
    • , Kamil Slowikowski
    • , Dorothee Diogo
    • , Xinli Hu
    •  & Soumya Raychaudhuri
  2. Department of Convergence Medicine, University of Ulsan College of Medicine and Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.

    • Buhm Han
  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Buhm Han
    • , Kamil Slowikowski
    • , Dorothee Diogo
    • , Xinli Hu
    •  & Soumya Raychaudhuri
  4. Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA.

    • Buhm Han
    • , Kamil Slowikowski
    • , Dorothee Diogo
    • , Xinli Hu
    •  & Soumya Raychaudhuri
  5. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

    • Jennie G Pouget
  6. Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.

    • Jennie G Pouget
  7. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

    • Jennie G Pouget
  8. Bioinformatics and Integrative Genomics, Harvard University, Cambridge, Massachusetts, USA.

    • Kamil Slowikowski
  9. Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA.

    • Eli Stahl
  10. Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.

    • Cue Hyunkyu Lee
    • , Yu Rang Park
    •  & Eunji Kim
  11. Harvard–MIT Division of Health Sciences and Technology, Boston, Massachusetts, USA.

    • Xinli Hu
  12. Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic of Korea.

    • Yu Rang Park
  13. Department of Chemistry, Seoul National University, Seoul, Republic of Korea.

    • Eunji Kim
  14. Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, Manhasset, New York, USA.

    • Peter K Gregersen
  15. Department of Public Health and Clinical Medicine, Rheumatology, Umeå University, Umeå, Sweden.

    • Solbritt Rantapää Dahlqvist
  16. Arthritis Research UK Centre for Genetics and Genomics, Musculoskeletal Research Centre, Institute for Inflammation and Repair, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.

    • Jane Worthington
    •  & Steve Eyre
  17. National Institute for Health Research, Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK.

    • Jane Worthington
    •  & Steve Eyre
  18. Instituto de Parasitología y Biomedicina López-Neyra, Consejo Superior de Investigaciones Científicas, Granada, Spain.

    • Javier Martin
  19. Rheumatology Unit, Department of Medicine, Karolinska Institutet and Karolinska University Hospital Solna, Stockholm, Sweden.

    • Lars Klareskog
    •  & Soumya Raychaudhuri
  20. Department of Rheumatology, Leiden University Medical Centre, Leiden, the Netherlands.

    • Tom Huizinga
  21. Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA.

    • Wei-Min Chen
    • , Suna Onengut-Gumuscu
    •  & Stephen S Rich
  22. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.

    • Naomi R Wray
  23. Institute of Inflammation and Repair, University of Manchester, Manchester, UK.

    • Soumya Raychaudhuri


  1. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

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


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B.H. and S.R. conceived the statistical approach and organized the project. B.H., J.G.P., and S.R. led and coordinated analyses and wrote the initial manuscript. E.S. and N.R.W. provided guidance on the statistical approach. K.S., C.H.L., D.D., X.H., Y.R.P., and E.K. contributed to the implementation of specific analyses and offered feedback on the statistical methodologies. P.K.G., S.R.D., J.W., J.M., S.E., L.K., S.R., and T.H. contributed RA samples and insight on the clinical implications to RA. W.-M.C., S.O.-G., and S.S.R. contributed T1D samples and insight on clinical implications to T1D. The Major Depressive Disorder Working Group contributed MDD samples and insight on the clinical implications to MDD. All authors contributed to the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Buhm Han or Soumya Raychaudhuri.

Integrated supplementary information

Supplementary information

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  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–4, Supplementary Tables 1, 2, 5 and 6, and Supplementary Note.

Excel files

  1. 1.

    Supplementary Table 3

    Detailed SNP information used for GRS and BUHMBOX analyses.

  2. 2.

    Supplementary Table 4

    GRS and BUHMBOX results.

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