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• W2124698911 abstract "In statistical data analysis, penalized regression is considered an attractive approachfor its ability of simultaneous variable selection and parameter estimation. Althoughpenalized regression methods have shown many advantages in variable selection andoutcome prediction over other approaches for high-dimensional data, there is a relativepaucity of the literature on their applications to hypothesis testing, e.g. in geneticassociation analysis. In this study, we apply several new penalized regression methodswith a novel penalty, called Truncated L1-penalty (TLP) (Shen et al. 2012), foreither variable selection, or both variable selection and parameter grouping, in a dataadaptiveway to test for association between a quantitative trait and a group of rarevariants. The performance of the new methods are compared with some existing tests,including some recently proposed global tests and penalized regression-based methods,via simulations and an application to the real sequence data of the Genetic AnalysisWorkshop 17 (GAW17). Although our proposed penalized methods can improve oversome existing penalized methods, often they do not outperform some existing globalassociation tests. Some possible problems with utilizing penalized regression methodsin genetic hypothesis testing are discussed. Given the capability of penalized regressionin selecting causal variants and its sometimes promising performance, further studiesare warranted." @default.
• W2124698911 created "2016-06-24" @default.
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• W2124698911 date "2014-05-13" @default.
• W2124698911 modified "2023-09-27" @default.
• W2124698911 title "Penalized regression approaches to testing for quantitative trait-rare variant association" @default.
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• W2124698911 doi "https://doi.org/10.3389/fgene.2014.00121" @default.
• W2124698911 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4026747" @default.
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