Genomic Selection in Winter Wheat Breeding Using a Recommender Approach

被引:12
作者
Lozada, Dennis N. [1 ,2 ]
Carter, Arron H. [1 ]
机构
[1] Washington State Univ, Dept Crop & Soil Sci, Pullman, WA 99164 USA
[2] New Mexico State Univ, Dept Plant & Environm Sci, Las Cruces, NM 88003 USA
基金
美国食品与农业研究所;
关键词
Bayesian models; genomic BLUP (GBLUP); grain yield; heading date; high-throughput phenotyping; item-based collaborative filtering (IBCF); plant height; recommender system; snow mold tolerance; spectral reflectance indices; GRAIN-YIELD; MULTIPLE-TRAIT; PREDICTION; ACCURACY; INDEXES;
D O I
10.3390/genes11070779
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页码:1 / 14
页数:14
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