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Sökning: WFRF:(Zaitlen Noah)

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1.
  • Zaitlen, Noah, et al. (författare)
  • Analysis of case-control association studies with known risk variants
  • 2012
  • Ingår i: Bioinformatics. - Oxford University Press. - 1367-4803. ; 28:13, s. 1729-1737
  • Tidskriftsartikel (refereegranskat)abstract
    • Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants.
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2.
  • Zaitlen, Noah, et al. (författare)
  • Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
  • 2012
  • Ingår i: PLoS Genetics. - Public Library of Science. - 1553-7404. ; 8:11
  • Tidskriftsartikel (refereegranskat)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-controlcovariate 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 falsepositive 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 = 1x10(-9)). The improvement varied across diseases with a 16% median increase in chi(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.
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