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How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete

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Notes

  1. 1.

    It is common to use the abbreviation “GWAS” for both the singular and plural forms of genome-wide association study/studies. “GWASs” is sometimes used to denote the plural (i.e. more than one GWAS).

  2. 2.

    Throughout the remainder of this manuscript, the term “candidate gene study” refers to traditional candidate gene studies, meaning studies that test for an association between one or a small number of polymorphisms and a phenotype of interest (e.g. depression), without examining genome-wide data. These “traditional” candidate gene studies should be distinguished from a different class of studies, focused on candidate genes identified through genome-wide searches. The latter is an important area of ongoing research.

  3. 3.

    There are potential exceptions for substance use disorders.

  4. 4.

    Experts have discrepant opinions about whether or not 22q11.2-linked schizophrenia is the same as “typical” schizophrenia, and broader questions about potential genetic subtypes of schizophrenia are not yet settled.

  5. 5.

    Meaning variants that explain a large fraction of phenotypic variance in the population; see Box 1.

  6. 6.

    Here we state the common-sense conclusion, that “such variants do not exist.” Technically: hypotheses about large effect variants have been repeatedly rejected, in large, high-quality studies.

  7. 7.

    Efforts to determine the portions of the genome most likely to harbor GWAS loci are ongoing, and the most consistent finding to date is that GWAS loci are concentrated in portions of the genome that are evolutionarily conserved across species.

  8. 8.

    Note that in practice, merely observing presence/absence of overlap of a GWAS locus with a candidate gene is insufficient to include or exclude that gene’s putative relevance to disease, but discussion of this is beyond the scope of this paper. For this paper, it is sufficient to ask the question of whether or not the specific hypotheses put forward by candidate gene studies are supported or not by GWAS results.

References

  1. 1.

    Sullivan PF, Agrawal A, Bulik CM, Andreassen OA, Børglum AD, Breen G, et al. Psychiatric genomics: an update and an agenda. Am J Psychiatry. 2018;175:15–27.

  2. 2.

    Sullivan PF, Daly MJ, O’Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13:537–51.

  3. 3.

    Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012. https://doi.org/10.1038/nrg3240.

  4. 4.

    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203.

  5. 5.

    Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, Breeling J, et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:214–23.

  6. 6.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

  7. 7.

    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.

  8. 8.

    Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343–52.

  9. 9.

    Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder. BioRxiv. 2018:173062.

  10. 10.

    Walters RK, Polimanti R, Johnson EC, McClintick JN, Adams MJ, Adkins AE, et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci. 2018;21:1656.

  11. 11.

    Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL, 23andMe Research Team, the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Adams MJ, et al. Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176:107–18.

  12. 12.

    Duncan LE, Ratanatharathorn A, Aiello AE, Almli LM, Amstadter AB, Ashley-Koch AE, et al. Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol Psychiatry. 2018;23:666–73.

  13. 13.

    Nievergelt C, Maihofer A, Dalvie S, Duncan L, Ratanatharathorn A, Ressler K, et al. 157. large-scale genetic characterization of PTSD: addressing heterogeneity across ancestry, sex, and trauma. Biol Psychiatry. 2018;83:S64.

  14. 14.

    Stein M, Gelernter J, Zhao H, Sun N, Pietrzak R, Harrington K, et al. 159. GWAS of PTSD re-experiencing symptoms in the VA million veteran program. Biol Psychiatry. 2018;83:S64–5.

  15. 15.

    Duncan LE, Yilmaz Z, Gaspar H, Walters R, Goldstein J, Anttila V, et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am J Psychiatry. 2017;174:850–8.

  16. 16.

    Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51:63.

  17. 17.

    Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51:431.

  18. 18.

    Anney RJL, Ripke S, Anttila V, Grove J, Holmans P, Huang H, et al. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:21.

  19. 19.

    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.

  20. 20.

    Colhoun HM, McKeigue PM, Smith GD. Problems of reporting genetic associations with complex outcomes. Lancet. 2003;361:865–72.

  21. 21.

    Sullivan PF. Spurious genetic associations. Biol Psychiatry. 2007;61:1121–6.

  22. 22.

    Duncan LE, Keller MC. A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am J Psychiatry. 2011;168:1041–9.

  23. 23.

    Johnson EC, Border R, Melroy-Greif WE, de Leeuw CA, Ehringer MA, Keller MC. No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biol Psychiatry. 2017;82:702–8.

  24. 24.

    Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatry. 2019;appi.ajp.2018:18070881.

  25. 25.

    Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748–52.

  26. 26.

    Psychiatric GWAS Consortium - PGC - Schizophrenia. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43:969–76.

  27. 27.

    Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17:1520–8.

  28. 28.

    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24.

  29. 29.

    Wray NR, Lee SH, Mehta D, Vinkhuyzen AAE, Dudbridge F, Middeldorp CM. Research review: polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry. 2014;55:1068–87.

  30. 30.

    Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013;9:e1003348.

  31. 31.

    Rees E, Walters JTR, Georgieva L, Isles AR, Chambert KD, Richards AL, et al. Analysis of copy number variations at 15 schizophrenia-associated loci. Br J Psychiatry. 2014;204:108–14.

  32. 32.

    Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46:1173–86.

  33. 33.

    Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, Cunningham JM, et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet. 2011;43:519–25.

  34. 34.

    Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47:1228–35.

  35. 35.

    Pers TH, Timshel P, Ripke S, Lent S, Sullivan PF, O’Donovan MC, et al. Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genes. Hum Mol Genet. 2016;25:1247–54.

  36. 36.

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

  37. 37.

    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.

  38. 38.

    Sekar A, Bialas AR, de Rivera H, Davis A, Hammond TR, Kamitaki N, et al. Schizophrenia risk from complex variation of complement component 4. Nature. 2016;530:177–83.

  39. 39.

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

  40. 40.

    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009;106:9362–7.

  41. 41.

    Purcell SM, Moran JL, Fromer M, Ruderfer D, Solovieff N, Roussos P, et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature. 2014;506:185–90.

  42. 42.

    Sullivan PF. How good were candidate gene guesses in schizophrenia genetics? Biol Psychiatry. 2017;82:696–7.

  43. 43.

    Duncan LE, Hutchison KE, Carey G, Craighead WE. Variation in brain-derived neurotrophic factor (BDNF) gene is associated with symptoms of depression. J Affect Disord. 2009;115:215–9.

  44. 44.

    Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.

  45. 45.

    Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90:7–24.

  46. 46.

    CONVERGE Consortium. Sparse swhole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015;523:588–91.

  47. 47.

    Karayiorgou M, Morris MA, Morrow B, Shprintzen RJ, Goldberg R, Borrow J, et al. Schizophrenia susceptibility associated with interstitial deletions of chromosome 22q11. Proc Natl Acad Sci USA. 1995;92:7612–6.

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Competing interests

The authors declare no competing interests.

Correspondence to Laramie E. Duncan.

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