title: Epistasis, complexity, and multifactor dimensionality reduction.
abstract: Genome-wide association studies (GWASs) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology. Conversion of this wealth of new information about genomic variation to knowledge about public health and human biology will depend critically on the complexity of the genotype to phenotype mapping relationship. We review here computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health. We focus on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene-gene interaction.
authors.affiliation: Computational Genetics Laboratory, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.
authors.forename: Qinxin
authors.initials: Q
authors.lastname: Pan
authors.forename: Ting
authors.initials: T
authors.lastname: Hu
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D058011:Multifactor Dimensionality Reduction; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide