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Zaitlen, Noah

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Zaitlen

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Noah

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Zaitlen, Noah
Author's Publications: search.filters.isAuthorOfPublication.071ae0b3-9f5e-4c71-9bbc-937fdd4ed02a

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    Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
    (Public Library of Science, 2012) Zaitlen, Noah; Lindström, Sara; Pasaniuc, Bogdan; Cornelis, Marilyn; Genovese, Giulio; Pollack, Samuela; Barton, Anne; Bickeböller, Heike; Bowden, Donald W.; Eyre, Steve; Freedman, Barry I.; Friedman, David; Field, John K.; Groop, Leif; Haugen, Aage; Heinrich, Joachim; Henderson, Brian E.; Hicks, Pamela J.; Hocking, Lynne J.; Kolonel, Laurence N.; Landi, Maria Teresa; Langefeld, Carl D.; Le Marchand, Loic; Meister, Michael; Morgan, Ann W.; Raji, Olaide Y.; Risch, Angela; Rosenberger, Albert; Scherf, David; Steer, Sophia; Walshaw, Martin; Waters, Kevin M.; Wilson, Anthony G.; Wordsworth, Paul; Zienolddiny, Shanbeh; Tchetgen, Eric Tchetgen; Haiman, Christopher; Hunter, David; Plenge, Robert M.; Worthington, Jane; Christiani, David; Schaumberg, Debra A.; Chasman, Daniel; Altshuler, David; Voight, Benjamin; Kraft, Peter; Patterson, Nick; Price, Alkes
    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 χ2 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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    Combining Effects from Rare and Common Genetic Variants in an Exome-Wide Association Study of Sequence Data
    (BioMed Central, 2011) Aschard, Hugues; Qiu, Weiliang; Pasaniuc, Bogdan; Zaitlen, Noah; Cho, Michael; Carey, Vincent
    Recent breakthroughs in next-generation sequencing technologies allow cost-effective methods for measuring a growing list of cellular properties, including DNA sequence and structural variation. Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Because for a given gene both rare and common causal variants can coexist and have independent effects on a trait, strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. To date, little work has been done on integrating signals from common and rare variants into powerful statistics for finding disease genes in genome-wide association studies. In this analysis of the Genetic Analysis Workshop 17 data, we evaluate various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals. We show that the analysis of common variants only using classical approaches can achieve higher power to detect causal genes than recently proposed rare variant methods and that strategies that combine association signals derived independently in rare and common variants can slightly increase the power compared to strategies that focus on the effect of either the rare variants or the common variants.