Genomic control

Genomic control (GC) is a statistical method that is commonly used to control for the confounding effects of population stratification in genetic association studies. The method was originally outlined by Bernie Devlin and Kathryn Roeder in a 1999 paper.[1] It involves using a set of anonymous genetic markers to estimate the effect of population structure on the distribution of the chi-square statistic. The distribution of the chi-square statistics for a given allele that is suspected to be associated with a given trait can then be compared to the distribution of the same statistics for an allele that is expected not to be related to the trait.[2][3] The method is supposed to involve the use of markers that are not linked to the marker being tested for a possible association.[4] In theory, it takes advantage of the tendency of population structure to cause overdispersion of test statistics in association analyses.[5] The genomic control method is as robust as family-based designs, despite being applied to population-based data.[6] It has the potential to lead to a decrease in statistical power to detect a true association, and it may also fail to completely eliminate the biasing effects of population stratification.[7] A more robust form of the genomic control method can be performed by expressing the association being studied as two Cochran–Armitage trend tests, and then applying the method to each test separately.[8]

References
  1. Devlin, Bernie; Roeder, Kathryn (1999). "Genomic Control for Association Studies". Biometrics. 55 (4): 997–1004. CiteSeerX 10.1.1.420.1751. doi:10.1111/j.0006-341X.1999.00997.x. ISSN 1541-0420. PMID 11315092.
  2. Donnelly, Peter; Phillips, Michael S.; Cardon, Lon R.; Marchini, Jonathan (May 2004). "The effects of human population structure on large genetic association studies". Nature Genetics. 36 (5): 512–517. doi:10.1038/ng1337. ISSN 1546-1718. PMID 15052271.
  3. Altshuler, David; Hirschhorn, Joel N.; Henderson, Brian; Sklar, Pamela; Lander, Eric S.; Kolonel, Laurence N.; Petryshen, Tracey L.; Pato, Michele T.; Pato, Carlos N. (April 2004). "Assessing the impact of population stratification on genetic association studies". Nature Genetics. 36 (4): 388–393. doi:10.1038/ng1333. ISSN 1546-1718. PMID 15052270.
  4. Krawczak, Michael; Dempfle, Astrid; Lieb, Wolfgang; Freitag-Wolf, Sandra; Yadav, Pankaj (2015-10-01). "Allowing for population stratification in case-only studies of gene–environment interaction, using genomic control". Human Genetics. 134 (10): 1117–1125. doi:10.1007/s00439-015-1593-y. ISSN 1432-1203. PMID 26297539.
  5. Devlin, Bernie; Roeder, Kathryn; Wasserman, Larry (2001-11-01). "Genomic Control, a New Approach to Genetic-Based Association Studies". Theoretical Population Biology. 60 (3): 155–166. doi:10.1006/tpbi.2001.1542. ISSN 0040-5809. PMID 11855950. S2CID 11547174.
  6. Roeder, Kathryn; Devlin, B.; Bacanu, Silviu-Alin (2000-06-01). "The Power of Genomic Control". The American Journal of Human Genetics. 66 (6): 1933–1944. doi:10.1086/302929. ISSN 1537-6605. PMC 1378064. PMID 10801388.
  7. Greenberg, David A.; Zhang, Junying; Shmulewitz, Dvora (2004). "Case-Control Association Studies in Mixed Populations: Correcting Using Genomic Control". Human Heredity. 58 (3–4): 145–153. doi:10.1159/000083541. ISSN 1423-0062. PMID 15812171.
  8. Gastwirth, Joseph L.; Freidlin, Boris; Zheng, Gang (2006-02-01). "Robust Genomic Control for Association Studies". The American Journal of Human Genetics. 78 (2): 350–356. doi:10.1086/500054. ISSN 1537-6605. PMC 1380242. PMID 16400614.
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