A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.
Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.
Moore
Jason H
JH
Computational Genetics Laboratory, Department of Genetics, Dartmouth-Hitchcock Medical Center, One Medical Center Dr., 706 Rubin Bldg, HB7937, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu
Gilbert
Joshua C
JC
Tsai
Chia-Ti
CT
Chiang
Fu-Tien
FT
Holden
Todd
T
Barney
Nate
N
White
Bill C
BC
D001281:Atrial Fibrillation / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D019277:Entropy; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide