OBJECTIVES: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. METHODS: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. RESULTS: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. CONCLUSION: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.
OBJECTIVES: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. METHODS: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. RESULTS: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. CONCLUSION: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.
Authors: Therese Truong; Rayjean J Hung; Christopher I Amos; Xifeng Wu; Heike Bickeböller; Albert Rosenberger; Wiebke Sauter; Thomas Illig; H-Erich Wichmann; Angela Risch; Hendrik Dienemann; Rudolph Kaaks; Ping Yang; Ruoxiang Jiang; John K Wiencke; Margaret Wrensch; Helen Hansen; Karl T Kelsey; Keitaro Matsuo; Kazuo Tajima; Ann G Schwartz; Angie Wenzlaff; Adeline Seow; Chen Ying; Andrea Staratschek-Jox; Peter Nürnberg; Erich Stoelben; Jürgen Wolf; Philip Lazarus; Joshua E Muscat; Carla J Gallagher; Shanbeh Zienolddiny; Aage Haugen; Henricus F M van der Heijden; Lambertus A Kiemeney; Dolores Isla; Jose Ignacio Mayordomo; Thorunn Rafnar; Kari Stefansson; Zuo-Feng Zhang; Shen-Chih Chang; Jin Hee Kim; Yun-Chul Hong; Eric J Duell; Angeline S Andrew; Flavio Lejbkowicz; Gad Rennert; Heiko Müller; Hermann Brenner; Loïc Le Marchand; Simone Benhamou; Christine Bouchardy; M Dawn Teare; Xiaoyan Xue; John McLaughlin; Geoffrey Liu; James D McKay; Paul Brennan; Margaret R Spitz Journal: J Natl Cancer Inst Date: 2010-06-14 Impact factor: 13.506
Authors: Christopher I Amos; Xifeng Wu; Peter Broderick; Ivan P Gorlov; Jian Gu; Timothy Eisen; Qiong Dong; Qing Zhang; Xiangjun Gu; Jayaram Vijayakrishnan; Kate Sullivan; Athena Matakidou; Yufei Wang; Gordon Mills; Kimberly Doheny; Ya-Yu Tsai; Wei Vivien Chen; Sanjay Shete; Margaret R Spitz; Richard S Houlston Journal: Nat Genet Date: 2008-04-02 Impact factor: 38.330
Authors: Teri A Manolio; Francis S Collins; Nancy J Cox; David B Goldstein; Lucia A Hindorff; David J Hunter; Mark I McCarthy; Erin M Ramos; Lon R Cardon; Aravinda Chakravarti; Judy H Cho; Alan E Guttmacher; Augustine Kong; Leonid Kruglyak; Elaine Mardis; Charles N Rotimi; Montgomery Slatkin; David Valle; Alice S Whittemore; Michael Boehnke; Andrew G Clark; Evan E Eichler; Greg Gibson; Jonathan L Haines; Trudy F C Mackay; Steven A McCarroll; Peter M Visscher Journal: Nature Date: 2009-10-08 Impact factor: 49.962
Authors: Carolyn M Hutter; Leah E Mechanic; Nilanjan Chatterjee; Peter Kraft; Elizabeth M Gillanders Journal: Genet Epidemiol Date: 2013-10-05 Impact factor: 2.135