Structures and Assumptions: Strategies to Harness Gene × Gene and Gene × Environment Interactions in GWAS.

Statistical Science : a Review Journal of the Institute of Mathematical Statistics
Charles KooperbergIndika Rajapakse

Abstract

Genome-wide association studies, in which as many as a million single nucleotide polymorphisms (SNP) are measured on several thousand samples, are quickly becoming a common type of study for identifying genetic factors associated with many phenotypes. There is a strong assumption that interactions between SNPs or genes and interactions between genes and environmental factors substantially contribute to the genetic risk of a disease. Identification of such interactions could potentially lead to increased understanding about disease mechanisms; drug × gene interactions could have profound applications for personalized medicine; strong interaction effects could be beneficial for risk prediction models. In this paper we provide an overview of different approaches to model interactions, emphasizing approaches that make specific use of the structure of genetic data, and those that make specific modeling assumptions that may (or may not) be reasonable to make. We conclude that to identify interactions it is often necessary to do some selection of SNPs, for example, based on prior hypothesis or marginal significance, but that to identify SNPs that are marginally associated with a disease it may also be useful to consider larger numbers...Continue Reading

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Citations

Mar 25, 2011·Genetics Research·Nengjun Yi
Jul 27, 2010·Proceedings of the National Academy of Sciences of the United States of America·Michael LeBlanc, Charles Kooperberg
Feb 3, 2015·Journal of the American Statistical Association·Yoonsuh JungJianhua Hu
Sep 22, 2011·Human Heredity·Wei PanXiaotong Shen
Jul 12, 2012·Genetic Epidemiology·Indika RajapakseCharles Kooperberg
Jan 17, 2014·Genetic Epidemiology·Jie Kate HuPei Wang
Jul 8, 2010·Statistical Science : a Review Journal of the Institute of Mathematical Statistics·Duncan C ThomasDaniel O Stram

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