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An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group

Sung, Yun Ju ; Winkler, Thomas W. ; Manning, Alisa K. ; Aschard, Hugues ; Gudnason, Vilmundur ; Harris, Tamara B. ; Smith, Albert V. ; Boerwinkle, Eric ; Brown, Michael R. ; Morrison, Alanna C. ; Fornage, Myriam ; Lin, Li-An ; Richard, Melissa ; Bartz, Traci M. ; Psaty, Bruce M. ; Hayward, Caroline ; Polasek, Ozren ; Marten, Jonathan ; Rudan, Igor ; Feitosa, Mary F. ; Kraja, Aldi T. ; Province, Michael A. ; Deng, Xuan ; Fisher, Virginia A. ; Zhou, Yanhua ; Bielak, Lawrence F. ; Smith, Jennifer ; Huffman, Jennifer E. ; Padmanabhan, Sandosh ; Smith, Blair H. ; Ding, Jingzhong ; Liu, Yongmei ; Lohman, Kurt ; Bouchard, Claude ; Rankinen, Tuomo ; Rice, Treva K. ; Arnett, Donna ; Schwander, Karen ; Guo, Xiuqing ; Palmas, Walter ; Rotter, Jerome I. ; Alfred, Tamuno ; Bottinger, Erwin P. ; Loos, Ruth J. F. ; Amin, Najaf ; Franco, Oscar H. ; van Duijn, Cornelia M. ; Vojinovic, Dina ; Chasman, Daniel I. ; Ridker, Paul M. ; Rose, Lynda M. ; Kardia, Sharon ; Zhu, Xiaofeng ; Rice, Kenneth ; Borecki, Ingrid B. ; Rao, Dabeeru C. ; Gauderman, W. James ; Cupples, L. Adrienne



Abstract

Studying gene-environment (G x E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G x E interactions uses a single regression model that includes both the genetic main and G x E interaction effects (the joint ...

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