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Juan Pablo Lewinger

Showing results (11-20 of 80) with videos related to

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Statistics in Medicine|January 25, 2022
Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpointEric S Kawaguchi, Gang Li, Juan Pablo Lewinger, et al.
Genetic Epidemiology|July 23, 2013
Finding novel genes by testing G × E interactions in a genome-wide association studyW James Gauderman, Pingye Zhang, John L Morrison, et al.
Journal of Data Science : JDS|October 24, 2022
Hierarchical Ridge Regression for Incorporating Prior Information in Genomic StudiesEric S Kawaguchi, Sisi Li, Garrett M Weaver, et al.
Genetic Epidemiology|December 29, 2015
Adaptive Set-Based Methods for Association TestingYu-Chen Su, William James Gauderman, Kiros Berhane, et al.
Genetic Epidemiology|November 21, 2018
Using Bayes model averaging to leverage both gene main effects and G ×  E interactions to identify genomic regions in genome-wide association studiesLilit C Moss, William J Gauderman, Juan Pablo Lewinger, et al.
American Journal of Epidemiology|December 27, 2011
Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposomeDuncan C Thomas, Juan Pablo Lewinger, Cassandra E Murcray, et al.
Genetic Epidemiology|December 26, 2022
Improved two-step testing of genome-wide gene-environment interactionsEric S Kawaguchi, Andre E Kim, Juan Pablo Lewinger, et al.
Genetic Epidemiology|September 17, 2011
Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studiesDalin Li, Juan Pablo Lewinger, William J Gauderman, et al.
Genetic Epidemiology|May 28, 2016
Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association StudyPingye Zhang, Juan Pablo Lewinger, David Conti, et al.
American Journal of Epidemiology|January 22, 2013
Confounding and heterogeneity in genetic association studies with admixed populationsJinghua Liu, Juan Pablo Lewinger, Frank D Gilliland, et al.
Pageof 8

Showing results (11-20 of 80) with videos related to

Sort By:
Pageof 8
Statistics in Medicine|January 25, 2022
Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpointEric S Kawaguchi, Gang Li, Juan Pablo Lewinger, et al.
Genetic Epidemiology|July 23, 2013
Finding novel genes by testing G × E interactions in a genome-wide association studyW James Gauderman, Pingye Zhang, John L Morrison, et al.
Journal of Data Science : JDS|October 24, 2022
Hierarchical Ridge Regression for Incorporating Prior Information in Genomic StudiesEric S Kawaguchi, Sisi Li, Garrett M Weaver, et al.
Genetic Epidemiology|December 29, 2015
Adaptive Set-Based Methods for Association TestingYu-Chen Su, William James Gauderman, Kiros Berhane, et al.
Genetic Epidemiology|November 21, 2018
Using Bayes model averaging to leverage both gene main effects and G ×  E interactions to identify genomic regions in genome-wide association studiesLilit C Moss, William J Gauderman, Juan Pablo Lewinger, et al.
American Journal of Epidemiology|December 27, 2011
Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposomeDuncan C Thomas, Juan Pablo Lewinger, Cassandra E Murcray, et al.
Genetic Epidemiology|December 26, 2022
Improved two-step testing of genome-wide gene-environment interactionsEric S Kawaguchi, Andre E Kim, Juan Pablo Lewinger, et al.
Genetic Epidemiology|September 17, 2011
Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studiesDalin Li, Juan Pablo Lewinger, William J Gauderman, et al.
Genetic Epidemiology|May 28, 2016
Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association StudyPingye Zhang, Juan Pablo Lewinger, David Conti, et al.
American Journal of Epidemiology|January 22, 2013
Confounding and heterogeneity in genetic association studies with admixed populationsJinghua Liu, Juan Pablo Lewinger, Frank D Gilliland, et al.
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