ArticleGenetics, selection, evolution : GSE2017
Locally epistatic models for genome-wide prediction and association by importance sampling.
Article in Genetics, selection, evolution : GSE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.Briefings in bioinformatics · 2024Article
- Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach.Heredity · 2023Article
- NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction.Frontiers in plant science · 2022Article
- Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.Frontiers in genetics · 2022Article
- Large-scale genome-wide association study, using historical data, identifies conserved genetic architecture of cyanogenic glucoside content in cassava (Manihot esculenta Crantz) root.The Plant journal : for cell and molecular biology · 2021Article
- Article
- Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Effect of drought stress on the genetic architecture of photosynthate allocation and remobilization in pods of common bean (Phaseolus vulgaris L.), a key species for food security.BMC plant biology · 2019Article
- Prediction of Subgenome Additive and Interaction Effects in Allohexaploid Wheat.G3 (Bethesda, Md.) · 2019Article
- A Low Resolution Epistasis Mapping Approach To Identify Chromosome Arm Interactions in Allohexaploid Wheat.G3 (Bethesda, Md.) · 2019Article
- Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers.G3 (Bethesda, Md.) · 2018Article
- Non-additive Effects in Genomic Selection.Frontiers in genetics · 2018Review
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3 authors.
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Abstract
backgroundIn 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.
resultsThis 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.
conclusionsIn 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.
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