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Informed conditioning on clinical covariates increases power in case-control association studies.

  • Academic Journal
  • PLoS genetics [PLoS Genet] 2012; Vol. 8 (11), pp. e1003032. Date of Electronic Publication: 2012 Nov 08.
  • English
  • Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in χ(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.
    Competing Interests: The authors have declared that no competing interests exist.
Additional Information
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101239074 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1553-7404 (Electronic) Linking ISSN: 15537390 NLM ISO Abbreviation: PLoS Genet Subsets: MEDLINE
Original Publication: San Francisco, CA : Public Library of Science, c2005-
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Date Created: 20121113 Date Completed: 20130513 Latest Revision: 20220129
20221216
PMC3493452
10.1371/journal.pgen.1003032
23144628
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