Location: Plant, Soil and Nutrition ResearchTitle: Locally epistatic models for genome-wide prediction and association by importance sampling
|ADKEMIR, DENIZ - Schaller Consulting|
|ISIDRO-SANCHEZ, JULIO - University College Dublin|
Submitted to: Genetics Selection Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/26/2017
Publication Date: 10/17/2017
Citation: Adkemir, D., Jannink, J., Isidro-Sanchez, J. 2017. Locally epistatic models for genome-wide prediction and association by importance sampling. Genetics Selection Evolution. 49:74. https://doi.org/10.1186/s12711-017-0348-8.
Interpretive Summary: In genomic selection (GS), the practice of making selections based on predicted breeding values from genomic marker analyses, an important task involves building models to predict future phenotypes based on genomic markers. Sometimes, phenotype is determined not just by main effects of genes but by their interactions. In this paper, we discuss a method capable of capturing such interactions when they happen in smaller genomic segments that are likely to be passed on without recombination from parents to offspring. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this method. The method resulted in accurate models, with sometimes significantly higher accuracies than that of standard additive models.
Technical 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 additive, locally epistatic genetic effects, gene-by-background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this approach. Conclusions- In this paper, we describe a new strategy for incorporating genetic interactions into genomic prediction and association models. This strategy results in accurate models, with sometimes significantly higher accuracies than that of a standard additive model.