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.

Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS

Abstract

Psychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case–case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case–control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case–case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case–control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genetic distance between cases and/or controls of SCZ, BIP and MDD.
Fig. 2: Power and type I error of CC-GWAS.
Fig. 3: Type I error of CC-GWAS due to differential tagging of a causal stress test SNP.
Fig. 4: Case–control effect sizes at CC-GWAS loci for SCZ, BIP and MDD.
Fig. 5: Independent replication of CC-GWAS results.

Data availability

CC-GWAS results generated in the present study for eight psychiatric disorders and three autoimmune diseases are available for public download at https://data.broadinstitute.org/alkesgroup/CC-GWAS/. GWAS results for BC are available at http://bcac.ccge.medschl.cam.ac.uk/bcacdata/. GWAS results for ADHD, AN, ASD, BIP, MDD51, OCD, SCZ15, SCZ versus BIP and TS are available at https://www.med.unc.edu/pgc/results-and-downloads/. GWAS results for MDD18 are available at https://datashare.is.ed.ac.uk/handle/10283/3203. GWAS results for SCZ16 are available at https://walters.psycm.cf.ac.uk/. GWAS results for CD and UC are available at https://www.ibdgenetics.org/downloads.html. GWAS results for RA are available at http://www.sg.med.osaka-u.ac.jp/tools.html. GWAS results for coronary artery disease are available at http://www.cardiogramplusc4d.org/data-downloads/. eQTL data from 13 GTEx version 7 brain tissues and meta-analysis of eQTL effects in brain tissues are available at https://cnsgenomics.com/software/smr/#DataResource. Access to the UK Biobank resource is available by application (http://www.ukbiobank.ac.uk).

Code availability

CC-GWAS software (R package) is available at https://github.com/wouterpeyrot/CCGWAS. R software is available at https://www.r-project.org/ (version 3.5.1 was used). LD score regression software is available at https://github.com/bulik/ldsc (version 1.0.0 was used). SMR software is available at https://cnsgenomics.com/software/smr/ (version 1.02 was used). PLINK 1.9 software is available at www.cog-genomics.org/plink/1.9/ (version 1.90b6.7 was used).

References

  1. 1.

    Lee, S. H. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

    CAS  PubMed  Google Scholar 

  2. 2.

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

  3. 3.

    Lee, P. H. et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).

    Google Scholar 

  4. 4.

    Ruderfer, D. M. et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715 (2018).

    CAS  PubMed Central  Google Scholar 

  5. 5.

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

    CAS  PubMed  Google Scholar 

  6. 6.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Qi, G. & Chatterjee, N. Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits. PLoS Genet. 14, e1007549 (2018).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Nieuwboer, H. A., Pool, R., Dolan, C. V., Boomsma, D. I. & Nivard, M. G. GWIS: genome-wide inferred statistics for functions of multiple phenotypes. Am. J. Hum. Genet. 99, 917–927 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Bhattacharjee, S. et al. A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am. J. Hum. Genet. 90, 821–835 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Han, B. & Eskin, E. Interpreting meta-analyses of genome-wide association studies. PLoS Genet. 8, e1002555 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Byrne, E. M. et al. Conditional GWAS analysis identifies putative disorder-specific SNPs for psychiatric disorders. Mol. Psychiatry https://doi.org/10.1038/s41380-020-0705-9 (2020).

  15. 15.

    Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    CAS  PubMed  Google Scholar 

  20. 20.

    Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS Revealing the complex genetic architecture of obsessive–compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018)..

  23. 23.

    Yu, D. et al. Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies. Am. J. Psychiatry 176, 217–227 (2019).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Yang, J., Wray, N. R. & Visscher, P. M. Comparing apples and oranges: equating the power of case–control and quantitative trait association studies. Genet. Epidemiol. 34, 254–257 (2010).

    PubMed  Google Scholar 

  25. 25.

    Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    O’Connor, L. J. et al. Extreme polygenicity of complex traits is explained by negative selection. Am. J. Hum. Genet. 105, 456–476 (2019).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015)..

  29. 29.

    Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    CAS  PubMed  Google Scholar 

  30. 30.

    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  PubMed  Google Scholar 

  31. 31.

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

    Google Scholar 

  32. 32.

    Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    PubMed  Google Scholar 

  34. 34.

    Hoefs, S. J. G. et al. NDUFA2 complex I mutation leads to Leigh disease. Am. J. Hum. Genet. 82, 1306–1315 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017).

    CAS  PubMed  Google Scholar 

  36. 36.

    Ikeda, M. et al. Genome-wide association study detected novel susceptibility genes for schizophrenia and shared trans-populations/diseases genetic effect. Schizophr. Bull. 45, 824–834 (2019).

    PubMed  Google Scholar 

  37. 37.

    Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Lam, M. et al. Pleiotropic meta-analysis of cognition, education, and schizophrenia differentiates roles of early neurodevelopmental and adult synaptic pathways. Am. J. Hum. Genet. 105, 334–350 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    CAS  PubMed  Google Scholar 

  40. 40.

    Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    CAS  PubMed  Google Scholar 

  43. 43.

    Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).

    Google Scholar 

  45. 45.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Hill, W. D. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiatry 24, 169–181 (2019).

    CAS  PubMed  Google Scholar 

  47. 47.

    Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Moore, D. L., Apara, A. & Goldberg, J. L. Krüppel-like transcription factors in the nervous system: novel players in neurite outgrowth and axon regeneration. Mol. Cell. Neurosci. 47, 233–243 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Yanagi, M. et al. Expression of Krüppel-like factor 5 gene in human brain and association of the gene with the susceptibility to schizophrenia. Schizophr. Res. 100, 291–301 (2008).

    PubMed  Google Scholar 

  51. 51.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Nolte, I. M. et al. Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study. Eur. J. Hum. Genet. 25, 877–885 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Marigorta, U. M. & Navarro, A. High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet. 9, e1003566 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Palmer, C. & Pe’er, I. Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies. PLoS Genet. 13, e1006916 (2017).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Sullivan, P. F. & Geschwind, D. H. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell 177, 162–183 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

  59. 59.

    Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Gazal, S., Marquez-Luna, C., Finucane, H. K. & Price, A. L. Reconciling S-LDSC and LDAK functional enrichment estimates. Nat. Genet. 51, 1202–1204 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Golan, D., Lander, E. S. & Rosset, S. Measuring missing heritability: inferring the contribution of common variants. Proc. Natl Acad. Sci. USA 111, E5272–E5281 (2014).

    CAS  PubMed  Google Scholar 

  62. 62.

    Bhatia, G., Patterson, N., Sankararaman, S. & Price, A. L. Estimating and interpreting FST: the impact of rare variants. Genome Res. 23, 1514–1521 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Lloyd-Jones, L. R., Robinson, M. R., Yang, J. & Visscher, P. M. Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics 208, 1397–1408 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

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

    PubMed  PubMed Central  Google Scholar 

  65. 65.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Siegel, R., Ma, J., Zou, Z. & Jemal, A. Cancer statistics, 2014. CA Cancer J. Clin. 64, 9–29 (2014).

    PubMed  Google Scholar 

  67. 67.

    Zhang, Y. D. et al. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers. Nat. Commun. 11, 3353 (2020).

  68. 68.

    Molodecky, N. A. et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology 142, 46–54 (2012).

    PubMed  Google Scholar 

  69. 69.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  70. 70.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Zhang, Y., Qi, G., Park, J.-H. & Chatterjee, N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326 (2018).

    CAS  PubMed  Google Scholar 

  72. 72.

    Zeng, J. et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat. Genet. 50, 746–753 (2018).

    CAS  PubMed  Google Scholar 

  73. 73.

    Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Schoech, L. O’Connor, O. Weissbrod, S. Gazal, D. Ruderfer, B. W. J. H. Penninx, N. R. Wray, K. Kendler, J. Smoller, W. van Rheenen and the members of the Cross-Disorder Group of the Psychiatric Genomics Consortium (PGC) for helpful discussions. For information about sample overlap, we thank S. Ripke, V. Trubetskoy and the PGC working groups of Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Attention Deficit Hyperactivity Disorder, Eating Disorders, Autism Spectrum Disorder and OCD & Tourette Syndrome. The BC genome-wide association analyses were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and grant PSR-SIIRI-701, the National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710) and the European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in ref. 26. Data on coronary artery disease and/or myocardial infarction were contributed by CARDIoGRAMplusC4D investigators and were downloaded from http://www.cardiogramplusc4d.org/. This research was funded by NIH grants R01 HG006399, R37 MH107649, R01 MH101244 and R01 CA222147 and NWO Veni grant (91619152) to W.J.P. This research was conducted using the UK Biobank Resources under application 10438.

Author information

Affiliations

Authors

Contributions

W.J.P. and A.L.P. designed experiments, W.J.P. performed experiments and analyzed data, and W.J.P. and A.L.P. wrote the manuscript.

Corresponding authors

Correspondence to Wouter J. Peyrot or Alkes L. Price.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Bjarni Vilhjalmsson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Figs. 1–20

Reporting Summary

Supplementary Tables

Supplementary Tables 1–39

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Peyrot, W.J., Price, A.L. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat Genet 53, 445–454 (2021). https://doi.org/10.1038/s41588-021-00787-1

Download citation

Further reading

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