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


ABSTRACT: 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 ?(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.

SUBMITTER: Zaitlen N 

PROVIDER: S-EPMC3493452 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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

Zaitlen Noah N   Lindström Sara S   Pasaniuc Bogdan B   Cornelis Marilyn M   Genovese Giulio G   Pollack Samuela S   Barton Anne A   Bickeböller Heike H   Bowden Donald W DW   Eyre Steve S   Freedman Barry I BI   Friedman David J DJ   Field John K JK   Groop Leif L   Haugen Aage A   Heinrich Joachim J   Henderson Brian E BE   Hicks Pamela J PJ   Hocking Lynne J LJ   Kolonel Laurence N LN   Landi Maria Teresa MT   Langefeld Carl D CD   Le Marchand Loic L   Meister Michael M   Morgan Ann W AW   Raji Olaide Y OY   Risch Angela A   Rosenberger Albert A   Scherf David D   Steer Sophia S   Walshaw Martin M   Waters Kevin M KM   Wilson Anthony G AG   Wordsworth Paul P   Zienolddiny Shanbeh S   Tchetgen Eric Tchetgen ET   Haiman Christopher C   Hunter David J DJ   Plenge Robert M RM   Worthington Jane J   Christiani David C DC   Schaumberg Debra A DA   Chasman Daniel I DI   Altshuler David D   Voight Benjamin B   Kraft Peter P   Patterson Nick N   Price Alkes L AL  

PLoS genetics 20121108 11


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  ...[more]

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