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

Nature Genetics volume 53, pages 445–454 (2021)Cite this article

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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.

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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.

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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).

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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.

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Authors and Affiliations

  1. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Wouter J. Peyrot & Alkes L. Price

  2. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Wouter J. Peyrot & Alkes L. Price

  3. Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands

    Wouter J. Peyrot

  4. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Alkes L. Price

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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.

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

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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

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