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PMID: 17266119 Published · ppublish English Journal Article Research Support, N.I.H., Extramural

Upward bias in odds ratio estimates from genome-wide association studies.

Genetic epidemiology ·Vol. 31 ·No. 4 ·2007-05-00 ·Pages 288-95

Garner C

Abstract

Genome-wide association studies are carried out to identify unknown genes for a complex trait. Polymorphisms showing the most statistically significant associations are reported and followed up in subsequent confirmatory studies. In addition to the test of association, the statistical analysis provides point estimates of the relationship between the genotype and phenotype at each polymorphism, typically an odds ratio in case-control association studies. The statistical significance of the test and the estimator of the odds ratio are completely correlated. Selecting the most extreme statistics is equivalent to selecting the most extreme odds ratios. The value of the estimator, given the value of the statistical significance depends on the standard error of the estimator and the power of the study. This report shows that when power is low, estimates of the odds ratio from a genome-wide association study, or any large-scale association study, will be upwardly biased. Genome-wide association studies are often underpowered given the low alpha levels required to declare statistical significance and the small individual genetic effects known to characterize complex traits. Factors such as low allele frequency, inadequate sample size and weak genetic effects contribute to large standard errors in the odds ratio estimates, low power and upwardly biased odds ratios. Studies that have high power to detect an association with the true odds ratio will have little or no bias, regardless of the statistical significance threshold. The results have implications for the interpretation of genome-wide association analysis and the planning of subsequent confirmatory stages.

MeSH Terms
Computer Simulation Gene Frequency Genetic Predisposition to Disease Genome, Human Genotype Humans Models, Genetic Models, Statistical Odds Ratio Phenotype Polymorphism, Genetic
Authors & Affiliations
1 authors, click to expand affiliations / ORCID
Garner Chad
Epidemiology Division, Department of Medicine, University of California-Irvine, Irvine, CA 92697, USA. [email protected]
Article Info
Journal
Genetic epidemiology
Abbr.
Genet Epidemiol
ISSN
0741-0395
Published
2007-05-00
Pages
288-95
Language
English
Region
United States
NLM ID
8411723
Subset
IM
Grants
NCI NIH HHS · R01 CA 58860-10 · United States
NCI NIH HHS · R01 CA 74415 · United States
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