Ditching candidate gene association studies: lessons from psychiatric genetics

For decades, candidate association studies represented a cost-effective approach to investigate the genetic factors underlying heritable human traits, including psychiatric disorders. This methodology entailed testing variants in genes hypothetically related to phenotypes of interest, based on gene function and historical relevance. However, the genes selected for candidate association studies were typically gleaned from rare, familial instances of disease that do not necessarily translate to the wider population, or were based on weak empirical data that yielded false associations, often due to the presence of hidden confounders in the small samples analyzed. Another issue was that we currently do not know the full repertoire of functional variants regulating human gene expression and, therefore, candidate studies were unlikely to have selected the correct regulatory variants to test for association in the first place. Ultimately, this approach contributed to a publication bias in the literature and hindered the identification of the true biological risk mechanisms underlying psychiatric disorders. A lack of consistency and trust in the results arising from these studies led to replication studies and meta-analyses that altogether discredited most candidate gene associations in psychiatry.¹-³

Following the cost reduction in whole-genome genotyping and the establishment of collaborative international initiatives, candidate studies were superseded by large-scale genome-wide association studies (GWAS). In this approach, virtually all independent common variants in the genome are tested for association, for example, with case-control status, personality traits, or drug response, often in cohorts of hundreds of thousands of individuals. These studies have implicated specific cell types like pyramidal neurons and interneurons in the etiology of schizophrenia,⁴ and frontal brain regions in depression.⁵

Psychiatric genetics has largely moved away from historical candidate association studies, as most candidate genes failed to show associations in GWAS. In fact, a study that analyzed results from a schizophrenia GWAS showed that common variants in 25 historical candidate genes, including COMT, BDNF and DISC1, were no more associated with schizophrenia than control sets of non-candidate genes.¹ Even for candidates that turned out to be associated with schizophrenia (e.g., DRD2 and GRM3), their biological relevance remains unclear considering there are many other genes with a stronger association. Another study depicted a similar scenario for depression.² However, despite the lack of success in candidate studies, a PubMed search for COMT revealed 269 research outputs published in 2020 alone (checked on January 20th, 2021). Many of these tested this gene for association with complex traits like pain, cognitive performance, behaviors, etc., even though the evidence of association between COMT and behavioral, psychiatric or neurological outcomes is weak, according to a phenome-wide association study (PheWAS) from the GWAS Atlas,⁶ which analyzed 4,756 GWAS results (Figure 1).

Figure 1
The phenome-wide association plot of COMT, based on an analysis of 4,756 GWAS results, separated into 22 categories, shows that no psychiatric, behavioral or neurological traits are strongly associated with this gene. The Bonferroni p-value threshold is 1.05 × 10^-5. Retrieved from the GWAS Atlas.⁶

Due to their reduced costs and analytical ease, the candidate approach still has a place in studies investigating panels of single nucleotide polymorphisms derived from large-scale GWAS in populations that are currently underrepresented in those studies (e.g., Latino and African populations), or in studies investigating related phenotypes. However, considering the polygenic nature underlying complex human traits and the limitations of the candidate approach, we should be ditching association studies, especially if these are based solely on historical gene relevance. Psychiatrists, geneticists and neuroscientists must reconsider the cost-benefits of candidate studies when there is no prior robust evidence of trait association, particularly now that there are hundreds of genes unbiasedly associated with many psychiatric traits. Ultimately, only the analysis of large cohorts using genome-wide methods and replication studies using panels of putatively-causal (i.e., fine-mapped) single nucleotide polymorphisms, selected through robust evidence, will provide the means to expand our understanding of the genetic architecture of complex human traits.

Acknowledgements

RRRD has received funding from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; BEX1279/13-0). HB has received funding from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; 2018/18560-6).

References

• ¹
  Johnson EC, Border R, Melroy-Greif WE, de Leeuw C, Ehringer MA, Keller MC. No Evidence That schizophrenia candidate genes are more associated with schizophrenia than non-candidate genes. Biol Psychiatry. 2017;82:702-8.
• ²
  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;176:376-87.
• ³
  Farrell MS, Werge T, Sklar P, Owen MJ, Ophoff RA, O'Donovan MC, et al. Evaluating historical candidate genes for schizophrenia. Mol Psychiatry. 2015;20:555-62.
• ⁴
  Schizophrenia Working Group of the Psychiatric Genomics Consortium; Ripke S, Walters JT, O'Donovan MC. Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. medRxiv. September 2020. http://10.1101/2020.09.12.20192922
  » http://10.1101/2020.09.12.20192922
• ⁵
  Howard DM, Adams MJ, Clarke TK, 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.
• ⁶
  Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51:1339-48.

Publication Dates

• Publication in this collection
  22 Feb 2021
• Date of issue
  May-Jun 2021

History

• Received
  17 Nov 2020
• Accepted
  21 Jan 2021