title: Higher order interactions: detection of epistasis using machine learning and evolutionary computation.
abstract: Higher order interactions are known to affect many different phenotypic traits. The advent of large-scale genotyping has, however, shown that finding interactions is not a trivial task. Classical genome-wide association studies (GWAS) are a useful starting point for unraveling the genetic architecture of a phenotypic trait. However, to move beyond the additive model we need new analysis tools specifically developed to deal with high-dimensional genotypic data. Here we show that evolutionary algorithms are a useful tool in high-dimensional analyses designed to identify gene-gene interactions in current large-scale genotypic data.
authors.affiliation: Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Nelson
authors.forename: Marcin
authors.initials: M
authors.lastname: Kierczak
authors.forename: Orjan
authors.initials: O
authors.lastname: Carlborg
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D003663:Decision Trees; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide