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 i...
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doaj-5b4bb80e070b4fd5824132b25188fddc2020-11-25T00:23:41ZdeuBMCGenetics Selection Evolution1297-96862017-10-0149111410.1186/s12711-017-0348-8Locally epistatic models for genome-wide prediction and association by importance samplingDeniz Akdemir0Jean-Luc Jannink1Julio Isidro-Sánchez2StatGen ConsultingRobert W. Holley Center for Agriculture and Health, USDA-ARSDepartment of Animal and Crop Science, University College DublinAbstract 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.http://link.springer.com/article/10.1186/s12711-017-0348-8 |
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DOAJ |
language |
deu |
format |
Article |
sources |
DOAJ |
author |
Deniz Akdemir Jean-Luc Jannink Julio Isidro-Sánchez |
spellingShingle |
Deniz Akdemir Jean-Luc Jannink Julio Isidro-Sánchez Locally epistatic models for genome-wide prediction and association by importance sampling Genetics Selection Evolution |
author_facet |
Deniz Akdemir Jean-Luc Jannink Julio Isidro-Sánchez |
author_sort |
Deniz Akdemir |
title |
Locally epistatic models for genome-wide prediction and association by importance sampling |
title_short |
Locally epistatic models for genome-wide prediction and association by importance sampling |
title_full |
Locally epistatic models for genome-wide prediction and association by importance sampling |
title_fullStr |
Locally epistatic models for genome-wide prediction and association by importance sampling |
title_full_unstemmed |
Locally epistatic models for genome-wide prediction and association by importance sampling |
title_sort |
locally epistatic models for genome-wide prediction and association by importance sampling |
publisher |
BMC |
series |
Genetics Selection Evolution |
issn |
1297-9686 |
publishDate |
2017-10-01 |
description |
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. |
url |
http://link.springer.com/article/10.1186/s12711-017-0348-8 |
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