Skip to main content

Thank you for visiting nature.com. 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.

  • Technical Report
  • Published:

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

Nature Genetics volume 48, pages 803–810 (2016)Cite this article

Subjects

Abstract

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).

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Overview of BUHMBOX.
Figure 2: Power gain by weighting SNPs by allele frequency and effect size.
Figure 3: Power of BUHMBOX for detecting heterogeneity as a function of the number of risk loci, the number of case samples, and the proportion of samples that actually have a different phenotype (π).
Figure 4: Genetic sharing among autoimmune diseases and psychiatric disorders.
Figure 5: Statistical power of BUHMBOX to detect heterogeneity.
Figure 6: BUHMBOX results.

Similar content being viewed by others

References

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism–derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

    Article  CAS  Google Scholar 

  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. 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 

  8. Pendergrass, S.A. 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).

    Article  CAS  Google Scholar 

  9. Collins, F.S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

    Article  CAS  Google Scholar 

  10. Criswell, L.A. 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).

    Article  CAS  Google Scholar 

  11. Kendler, K.S., Neale, M.C., Kessler, R.C., Heath, A.C. & Eaves, L.J. Major depression and generalized anxiety disorder. Same genes, (partly) different environments? Arch. Gen. Psychiatry 49, 716–722 (1992).

    Article  CAS  Google Scholar 

  12. Wray, N.R., Goddard, M.E. & Visscher, P.M. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 17, 1520–1528 (2007).

    Article  CAS  Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. 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).

    Article  CAS  Google Scholar 

  17. Wray, N.R., Lee, S.H. & Kendler, K.S. Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Eur. J. Hum. Genet. 20, 668–674 (2012).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. van der Linden, M.P. 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).

    Article  Google Scholar 

  20. Wiik, A.S., van Venrooij, W.J. & Pruijn, G.J. All you wanted to know about anti-CCP but were afraid to ask. Autoimmun. Rev. 10, 90–93 (2010).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  23. Smoller, J.W., Lunetta, K.L. & Robins, J. Implications of comorbidity and ascertainment bias for identifying disease genes. Am. J. Med. Genet. 96, 817–822 (2000).

    Article  CAS  Google Scholar 

  24. Burrell, R.A., McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).

    Article  CAS  Google Scholar 

  25. Jeste, S.S. & Geschwind, D.H. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10, 74–81 (2014).

    Article  Google Scholar 

  26. Flint, J. & Kendler, K.S. The genetics of major depression. Neuron 81, 484–503 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

    Article  CAS  Google Scholar 

  33. Festen, E.A. 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).

    Article  CAS  Google Scholar 

  34. Zhernakova, A. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  36. Cotsapas, C. & Hafler, D.A. Immune-mediated disease genetics: the shared basis of pathogenesis. Trends Immunol. 34, 22–26 (2013).

    Article  CAS  Google Scholar 

  37. Onengut-Gumuscu, S. 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).

    Article  CAS  Google Scholar 

  38. Han, B. 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).

    Article  CAS  Google Scholar 

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

  40. 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 

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

    Article  Google Scholar 

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

    Google Scholar 

  43. Wei, L.J., Lin, D.Y. & Weissfeld, L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J. Am. Stat. Assoc. 84, 1065–1073 (1989).

    Article  Google Scholar 

  44. Lin, D.Y. & Sullivan, P.F. Meta-analysis of genome-wide association studies with overlapping subjects. Am. J. Hum. Genet. 85, 862–872 (2009).

    Article  CAS  Google Scholar 

  45. 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).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

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

  1. Buhm Han, Jennie G Pouget and Soumya Raychaudhuri: These authors contributed equally to this work.

  2. buhm.han@amc.seoul.kr

Authors and Affiliations

  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. Department of Medicine, Rheumatology Unit, 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

Authors
  1. Buhm Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennie G Pouget
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamil Slowikowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eli Stahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cue Hyunkyu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dorothee Diogo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinli Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yu Rang Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eunji Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peter K Gregersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Solbritt Rantapää Dahlqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jane Worthington
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Javier Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Steve Eyre
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Lars Klareskog
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tom Huizinga
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Wei-Min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Suna Onengut-Gumuscu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Stephen S Rich
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Naomi R Wray
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Soumya Raychaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Contributions

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.

Corresponding authors

Correspondence to Buhm Han or Soumya Raychaudhuri.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Power and false positive rate of the BUHMBOX and GRS approaches for different situations.

Under true pleiotropy without subgroup heterogeneity, the GRS approach appropriately demonstrated 64.8% power to detect shared genetic structure and BUHMBOX demonstrated an appropriate false positive rate of 4.3%. Under true subgroup heterogeneity without pleiotropy, the GRS approach demonstrated 100% power to detect shared genetic structure and BUHMBOX demonstrated 81.7% power to detect subgroup heterogeneity at α = 0.05. The GRS approach has power for both situations and therefore cannot distinguish subgroup heterogeneity from pleiotropy.

Supplementary Figure 2 Expected correlations with respect to heterogeneity proportion, risk allele frequencies, and odds ratios.

The plots show the expected correlations between pairs of alleles for different risk allele frequencies (RAFs) and odds ratios (ORs). (a) We assumed OR = 1.5 for both alleles. (b) We assumed RAF = 0.5 for both alleles.

Supplementary Figure 3 Power of BUHMBOX as a function of effect size.

This plot shows the power of BUHMBOX for detecting heterogeneity in terms of the mean effect size and the proportion of samples that comprise a hidden subgroup (heterogeneity proportion, π). We assume that we have 2,000 cases, 2,000 controls, and 50 associated loci. We sampled OR and RAF values from the GWAS catalog and scaled all log-transformed OR values to have a desired mean value. White lines denote 20%, 40%, 60%, and 80% power.

Supplementary Figure 4 Power of BUHMBOX in polygenic modeling.

We simulated GWAS and examined the power of BUHMBOX by including moderately significant loci up to P < 0.01. We modeled the genetic architecture of disease using rheumatoid arthritis, on the basis of results from Stahl et al. (Nat. Genet. 44, 483–489, 2012). To simulate 2,231 causal variants, we combined 71 independent known rheumatoid arthritis risk loci with an additional 2,160 loci sampled from the joint posterior distribution of RAF and OR values presented in Stahl et al. For null loci, we also used the null RAF distribution presented in Stahl et al. Given this disease model, we simulated a GWAS with 3,964 cases and 12,052 controls (sample sizes from Stahl et al.), assuming disease prevalence of 0.01. Given these GWAS results, we used only the top k GWAS loci defined by P-value threshold t and their observed OR estimates for BUHMBOX power simulations. We assumed N = 5,000 and π = 0.5 for power evaluation and tried different P-value thresholds t from 5 × 10−8 to 0.01.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Tables 1, 2, 5 and 6, and Supplementary Note. (PDF 4068 kb)

Supplementary Table 3

Detailed SNP information used for GRS and BUHMBOX analyses. (XLSX 136 kb)

Supplementary Table 4

GRS and BUHMBOX results. (XLSX 54 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, B., Pouget, J., Slowikowski, K. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat Genet 48, 803–810 (2016). https://doi.org/10.1038/ng.3572

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3572

This article is cited by

Search

Advanced search

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