Locally epistatic models for genome-wide prediction and association by importance sampling.

ABSTRACT:

Background

In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions, and there is an increased interest in using marker annotations in genome-wide prediction and association analyses. In this paper, we discuss a hybrid modeling method which combines parametric mixed modeling and non-parametric rule ensembles.

Results

This approach gives us a flexible class of models that can be used to capture ad

SUBMITTER: Akdemir D 

PROVIDER: S-EPMC5646165 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature