Cargando…
Genomic Selection in Winter Wheat Breeding Using a Recommender Approach
Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397162/ https://www.ncbi.nlm.nih.gov/pubmed/32664601 http://dx.doi.org/10.3390/genes11070779 |
_version_ | 1783565717223768064 |
---|---|
author | Lozada, Dennis N. Carter, Arron H. |
author_facet | Lozada, Dennis N. Carter, Arron H. |
author_sort | Lozada, Dennis N. |
collection | PubMed |
description | Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs. |
format | Online Article Text |
id | pubmed-7397162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73971622020-08-16 Genomic Selection in Winter Wheat Breeding Using a Recommender Approach Lozada, Dennis N. Carter, Arron H. Genes (Basel) Article Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs. MDPI 2020-07-11 /pmc/articles/PMC7397162/ /pubmed/32664601 http://dx.doi.org/10.3390/genes11070779 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lozada, Dennis N. Carter, Arron H. Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
title | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
title_full | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
title_fullStr | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
title_full_unstemmed | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
title_short | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
title_sort | genomic selection in winter wheat breeding using a recommender approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397162/ https://www.ncbi.nlm.nih.gov/pubmed/32664601 http://dx.doi.org/10.3390/genes11070779 |
work_keys_str_mv | AT lozadadennisn genomicselectioninwinterwheatbreedingusingarecommenderapproach AT carterarronh genomicselectioninwinterwheatbreedingusingarecommenderapproach |