TY - JOUR T1 - Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling JF - bioRxiv DO - 10.1101/046177 SP - 046177 AU - Deniz Akdemir AU - Jean-Luc Jannink Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/03/29/046177.abstract N2 - In statistical genetics an important task involves building predictive models for the genotype-phenotype relationships and thus attribute a proportion of the total phenotypic variance to the variation in genotypes. Numerous models have been proposed to incorporate additive genetic effects into models for prediction or association. However, there is a scarcity of models that can adequately account for gene by gene or other forms of genetical interactions. In addition, there is an increased interest in using marker annotations in genome-wide prediction and association. In this paper, we discuss an hybrid modeling methodology which combines the parametric mixed modeling approach and the non-parametric rule ensembles. This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene x background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark data sets covering a range of organisms and traits in addition to simulated data sets to illustrate the strengths of this approach. The improvement of model accuracies and association results suggest that a part of the’’missing heritability” in complex traits can be captured by modeling local epistasis. ER -