Candidate gene

The candidate gene approach to conducting genetic association studies focuses on associations between genetic variation within pre-specified genes of interest, and phenotypes or disease states. This is in contrast to genome-wide association studies (GWAS), which is a hypothesis-free approach that scans the entire genome for associations between common genetic variants (typically SNPs) and traits of interest. Candidate genes are most often selected for study based on a priori knowledge of the gene's biological functional impact on the trait or disease in question.[1][2] The rationale behind focusing on allelic variation in specific, biologically relevant regions of the genome is that certain alleles within a gene may directly impact the function of the gene in question and lead to variation in the phenotype or disease state being investigated. This approach often uses the case-control study design to try to answer the question, "Is one allele of a candidate gene more frequently seen in subjects with the disease than in subjects without the disease?"[1] Candidate genes hypothesized to be associated with complex traits have generally not been replicated by subsequent GWASs[3][4][5][6] or highly powered replication attempts.[7][8] The failure of candidate gene studies to shed light on the specific genes underlying such traits has been ascribed to insufficient statistical power, low prior probability that scientists can correctly guess a specific allele within a specific gene that is related to a trait, poor methodological practices, and data dredging.[9][6][10]

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  2. ^ Zhu M, Zhao S (October 2007). "Candidate gene identification approach: progress and challenges". International Journal of Biological Sciences. 3 (7): 420–427. doi:10.7150/ijbs.3.420. PMC 2043166. PMID 17998950.
  3. ^ Johnson EC, Border R, Melroy-Greif WE, de Leeuw CA, Ehringer MA, Keller MC (November 2017). "No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Noncandidate Genes". Biological Psychiatry. 82 (10): 702–708. doi:10.1016/j.biopsych.2017.06.033. PMC 5643230. PMID 28823710.
  4. ^ Chabris CF, Hebert BM, Benjamin DJ, Beauchamp J, Cesarini D, van der Loos M, et al. (2012-09-24). "Most reported genetic associations with general intelligence are probably false positives". Psychological Science. 23 (11): 1314–1323. doi:10.1177/0956797611435528. PMC 3498585. PMID 23012269.
  5. ^ Bosker FJ, Hartman CA, Nolte IM, Prins BP, Terpstra P, Posthuma D, et al. (May 2011). "Poor replication of candidate genes for major depressive disorder using genome-wide association data". Molecular Psychiatry. 16 (5): 516–532. doi:10.1038/mp.2010.38. PMID 20351714.
  6. ^ a b Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, Keller MC (May 2019). "No Support for Historical Candidate Gene or Candidate Gene-by-Interaction Hypotheses for Major Depression Across Multiple Large Samples". The American Journal of Psychiatry. 176 (5): 376–387. doi:10.1176/appi.ajp.2018.18070881. PMC 6548317. PMID 30845820.
  7. ^ Duncan LE, Keller MC (October 2011). "A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry". The American Journal of Psychiatry. 168 (10): 1041–1049. doi:10.1176/appi.ajp.2011.11020191. PMC 3222234. PMID 21890791.
  8. ^ Culverhouse RC, Saccone NL, Horton AC, Ma Y, Anstey KJ, Banaschewski T, et al. (January 2018). "Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression". Molecular Psychiatry. 23 (1): 133–142. doi:10.1038/mp.2017.44. PMC 5628077. PMID 28373689.
  9. ^ Farrell MS, Werge T, Sklar P, Owen MJ, Ophoff RA, O'Donovan MC, et al. (May 2015). "Evaluating historical candidate genes for schizophrenia". Molecular Psychiatry. 20 (5): 555–562. doi:10.1038/mp.2015.16. PMC 4414705. PMID 25754081.
  10. ^ Risch N, Herrell R, Lehner T, Liang KY, Eaves L, Hoh J, et al. (June 2009). "Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis". JAMA. 301 (23): 2462–71. doi:10.1001/jama.2009.878. PMC 2938776. PMID 19531786.