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Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders


Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium—schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

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Fig. 1: Forest plots for the four most significant SNPs in SCZ mtCOJO analysis with larger conditional effect sizes.
Fig. 2: Results from MAGMA brain cell-type enrichment analyses of raw and conditional GWAS analyses.

Code availability

Scripts used to generate the results are available on request from the corresponding author.


  1. Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM, et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45:984–94.

    Article  Google Scholar 

  2. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41.

    CAS  Article  Google Scholar 

  3. Grove J, Ripke S, Als TD, Mattheisen M, Walters R, Won H, et al. Common risk variants identified in autism spectrum disorder. 2017.

  4. 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. 2013;381:1371–9.

    Article  Google Scholar 

  5. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50:229–37.

    CAS  Article  Google Scholar 

  6. Maier R, Moser G, Chen GB, Ripke S, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Coryell W, et al. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am J Hum Genet. 2015;96:283–94.

    CAS  Article  Google Scholar 

  7. Scott J, Leboyer M, Hickie I, Berk M, Kapczinski F, Frank E, et al. Clinical staging in psychiatry: a cross-cutting model of diagnosis with heuristic and practical value. Br J Psychiatry J Ment Sci. 2013;202:243–5.

    Article  Google Scholar 

  8. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.

    CAS  Article  Google Scholar 

  9. Nieuwboer HA, Pool R, Dolan CV, Boomsma DI, Nivard MG. GWIS: genome-wide inferred statistics for functions of multiple phenotypes. Am J Hum Genet. 2016;99:917–27.

    CAS  Article  Google Scholar 

  10. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9:224.

    Article  Google Scholar 

  11. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129:687–702.

  12. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    CAS  Article  Google Scholar 

  13. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  15. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.

    Article  Google Scholar 

  16. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, 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. 2017;33:272–9.

    CAS  Article  Google Scholar 

  17. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun. 2018;9:2282.

    Article  Google Scholar 

  18. Hannon E, Dempster E, Viana J, Burrage J, Smith AR, Macdonald R, et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 2016;17:176.

    Article  Google Scholar 

  19. Skene NG, Bryois J, Bakken TE, Breen G, Crowley JJ, Gaspar HA, et al. Genetic identification of brain cell types underlying schizophrenia. Nat Genet. 2018;50:825–33.

    CAS  Article  Google Scholar 

  20. Lin LF, Doherty DH, Lile JD, Bektesh S, Collins F. GDNF: a glial cell line-derived neurotrophic factor for midbrain dopaminergic neurons. Science. 1993;260:1130–2.

    CAS  Article  Google Scholar 

  21. Stromberg I, Bjorklund L, Johansson M, Tomac A, Collins F, Olson L, et al. Glial cell line-derived neurotrophic factor is expressed in the developing but not adult striatum and stimulates developing dopamine neurons in vivo. Exp Neurol. 1993;124:401–12.

    CAS  Article  Google Scholar 

  22. Wang S, Bellen HJ. The retromer complex in development and disease. Development. 2015;142:2392–6.

    CAS  Article  Google Scholar 

  23. Yagi T, Takeichi M. Cadherin superfamily genes: functions, genomic organization, and neurologic diversity. Genes Dev. 2000;14:1169–80.

    CAS  PubMed  Google Scholar 

  24. Vagnozzi AN, Pratico D. Endosomal sorting and trafficking, the retromer complex and neurodegeneration. Mol Psychiatry. 2019;24:857–68.

    CAS  Article  Google Scholar 

  25. Bhalla A, Vetanovetz CP, Morel E, Chamoun Z, Di Paolo G, Small SA. The location and trafficking routes of the neuronal retromer and its role in amyloid precursor protein transport. Neurobiol Dis. 2012;47:126–34.

    CAS  Article  Google Scholar 

  26. Zhang H, Huang T, Hong Y, Yang W, Zhang X, Luo H, et al. The retromer complex and sorting nexins in neurodegenerative diseases. Front Aging Neurosci. 2018;10:79.

    CAS  Article  Google Scholar 

  27. Mecozzi VJ, Berman DE, Simoes S, Vetanovetz C, Awal MR, Patel VM, et al. Pharmacological chaperones stabilize retromer to limit APP processing. Nat Chem Biol. 2014;10:443–9.

    CAS  Article  Google Scholar 

  28. Pedrosa E, Stefanescu R, Margolis B, Petruolo O, Lo Y, Nolan K, et al. Analysis of protocadherin alpha gene enhancer polymorphism in bipolar disorder and schizophrenia. Schizophrenia Res. 2008;102:210–9.

    Article  Google Scholar 

  29. Cordova-Palomera A, Fatjo-Vilas M, Gasto C, Navarro V, Krebs MO, Fananas L. Genome-wide methylation study on depression: differential methylation and variable methylation in monozygotic twins. Transl Psychiatry. 2015;5:e557.

    CAS  Article  Google Scholar 

  30. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173:1705–15.e16.

    Article  Google Scholar 

  31. Pardiñas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018; 50:381–9.

    Article  Google Scholar 

  32. Stahl EA, Breen G, Forstner AJ, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51:793–803.

    CAS  Article  Google Scholar 

  33. Demontis D, Walters RK, Martin J, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51:63–75.

    CAS  Article  PubMed  Google Scholar 

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This work is supported by grants from the National Health and Medical Research Council of Australia (1087889, 1145645, 1113400, 1078901 and 1078037), and the Sylvia & Charles Viertel Charitable Foundation. The PGC has received major funding from the US National Institute of Mental Health and the US National Institute of Drug Abuse (U01 MH109528 and U01 MH1095320). We thank the research participants and employees of 23andMe, Inc. for contributing to this study. This paper would not have been possible without the generosity of participants in the many studies that comprise the final meta-analyses and the dedication of many clinicians and research staff who have collected the data and made them publically available. Acknowledgments for specific data sets are provided in the Supplementary Material.

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Correspondence to Enda M. Byrne.

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PFS is on the advisory committee at Lundbeck, is a Scientific Advisory Board member at Pfizer and has received speaker reimbursement and grant funding from Roche. JH-L. is a Scientific Advisor at Cartana and has received grant funding from Roche.

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Members of the Bipolar Working Group of the Psychiatric Genomics Consortium and Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium are listed in Supplementary Information file.

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Byrne, E.M., Zhu, Z., Qi, T. et al. Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders. Mol Psychiatry 26, 2070–2081 (2021).

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