Higher order interactions: detection of epistasis using machine learning and evolutionary computation.

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.