Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling

TL;DR

This paper introduces a hybrid modeling approach combining parametric and non-parametric methods to better capture local epistatic effects and gene interactions in genome-wide prediction and association, improving accuracy and addressing missing heritability.

Contribution

It presents a novel hybrid model that integrates mixed models and rule ensembles to incorporate local epistasis and annotations in genetic prediction and association.

Findings

Improved model accuracy over traditional methods

Effective capture of local epistatic effects

Enhanced detection of gene interactions

Abstract

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.