Premium
Improving strategies for detecting genetic patterns of disease susceptibility in association studies
Author(s) -
Calle M. L.,
Urrea V.,
Vellalta G.,
Malats N.,
Steen K. V.
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3431
Subject(s) - multifactor dimensionality reduction , epistasis , confounding , genetic association , single nucleotide polymorphism , computational biology , disease , genome wide association study , snp , computer science , cancer , gene , bioinformatics , biology , genetics , medicine , genotype
Abstract The analysis of gene interactions and epistatic patterns of susceptibility is especially important for investigating complex diseases such as cancer characterized by the joint action of several genes. This work is motivated by a case‐control study of bladder cancer, aimed at evaluating the role of both genetic and environmental factors in bladder carcinogenesis. In particular, the analysis of the inflammation pathway is of interest, for which information on a total of 282 SNPs in 108 genes involved in the inflammatory response is available. Detecting and interpreting interactions with such a large number of polymorphisms is a great challenge from both the statistical and the computational perspectives. In this paper we propose a two‐stage strategy for identifying relevant interactions: (1) the use of a synergy measure among interacting genes and (2) the use of the model‐based multifactor dimensionality reduction method (MB‐MDR), a model‐based version of the MDR method, which allows adjustment for confounders. Copyright © 2008 John Wiley & Sons, Ltd.