J
Juan Pablo Lewinger
Researcher at University of Southern California
Publications - 75
Citations - 2141
Juan Pablo Lewinger is an academic researcher from University of Southern California. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 24, co-authored 61 publications receiving 1826 citations. Previous affiliations of Juan Pablo Lewinger include University of Toronto & Mount Sinai Hospital, Toronto.
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Gene-Environment Interaction in Genome-Wide Association Studies
TL;DR: An efficient and easily implemented 2-step analysis of genome-wide association study data aimed at identifying genes involved in a gene-environment interaction has the potential to uncover new genetic signals that have not been identified previously.
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Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies.
TL;DR: An approach in which individuals with very extreme phenotypes are discarded is examined, and it is demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme‐based sampling can still be more efficient.
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Radical Prostatectomy or External Beam Radiation Therapy vs No Local Therapy for Survival Benefit in Metastatic Prostate Cancer: A SEER-Medicare Analysis.
Raj Satkunasivam,Andre E. Kim,Mihir M. Desai,Mike M. Nguyen,David I. Quinn,Leslie K. Ballas,Juan Pablo Lewinger,Mariana C. Stern,Ann S. Hamilton,Monish Aron,Inderbir S. Gill +10 more
TL;DR: Local therapy with radical prostatectomy and intensity modulated radiation therapy but not with conformal radiation therapy was associated with a survival benefit in men with metastatic prostate cancer, and this finding warrants prospective evaluation in clinical trials.
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Finding novel genes by testing G × E interactions in a genome-wide association study.
TL;DR: A new two‐step screening and testing method (EDG×E) that is optimized to find genes with a weak marginal effect is proposed, and application of this method to a G × Sex scan for childhood asthma reveals two potentially interesting SNPs that were not identified in the marginal‐association scan.
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Sample size requirements to detect gene-environment interactions in genome-wide association studies.
TL;DR: A hybrid method is proposed that combines two screening approaches by allocating a proportion of the overall genome‐wide significance level to each test and is shown to be a powerful and robust method for nearly any underlying model.