期刊
HUMAN HEREDITY
卷 73, 期 4, 页码 185-194出版社
KARGER
DOI: 10.1159/000339906
关键词
Statistical power; Genetic association studies; Case-control association analysis; Gene-environment interaction Environmental risk factor; Association mapping; Orthogonal modeling
资金
- National Institutes of Health [R01CA134682, CA121197, CA134682, CPRIT RP100443, U19 CA148127, R01AR057120]
- University of Texas MD Anderson Cancer Center Duncan Family Institute for Cancer Prevention and Risk Assessment
- NATIONAL CANCER INSTITUTE [P30CA016672, R01CA134682, R01CA121197, R01CA133996, U19CA148127, P01CA034936] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR RESEARCH RESOURCES [P20RR024475] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL106034] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [R01AR057120] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P20GM103534, R01GM104411] Funding Source: NIH RePORTER
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. Copyright (C) 2012 S. Karger AG, Basel
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据