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Related Experiment Video

Updated: Dec 14, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits
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Genomic Selection in Winter Wheat Breeding Using a Recommender Approach.

Dennis N Lozada1,2, Arron H Carter1

  • 1Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.

Genes
|July 16, 2020
PubMed
Summary
This summary is machine-generated.

Genomic selection (GS) in wheat breeding can be improved using item-based collaborative filtering (IBCF) and multi-trait, multi-environment data. Bayesian regression models with SNP markers enhanced prediction accuracy for key traits.

Keywords:
Bayesian modelsgenomic BLUP (GBLUP)grain yieldheading datehigh-throughput phenotypingitem-based collaborative filtering (IBCF)plant heightrecommender systemsnow mold tolerancespectral reflectance indices

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Area of Science:

  • Agricultural Science
  • Genetics
  • Plant Breeding

Background:

  • Genomic selection (GS) is crucial for enhancing plant breeding efficiency.
  • Optimizing predictive ability is essential for the successful implementation of GS.
  • Multi-trait, multi-environment data present unique challenges and opportunities for GS.

Purpose of the Study:

  • To evaluate the potential of an item-based collaborative filtering (IBCF) recommender system for multi-trait, multi-environment GS in winter wheat.
  • To compare different GS scenarios and modeling approaches for predictive accuracy.
  • To identify effective strategies for improving genomic prediction in wheat breeding programs.

Main Methods:

  • Evaluated IBCF in various GS scenarios for winter wheat lines.
  • Utilized multi-trait data from high-throughput phenotyping and SNP markers.
  • Employed cross-validations and Bayesian regression models, including Bayesian LASSO, for trait adjustment.
  • Compared IBCF performance against the genomic best linear unbiased predictor (GBLUP) model.

Main Results:

  • High correlation between environments improved predictive ability across years.
  • Using multiple spectral traits enhanced genomic selection accuracies for grain yield (GY) compared to single traits.
  • Bayesian regression models, particularly Bayesian LASSO, improved accuracies for GY, heading date, and plant height.
  • IBCF demonstrated competitive prediction accuracies compared to GBLUP.

Conclusions:

  • Item-based collaborative filtering (IBCF) shows promise as an alternative prediction model in wheat breeding.
  • Multi-trait data and advanced Bayesian methods significantly improve genomic prediction accuracy.
  • Optimized GS approaches are vital for advancing breeding programs and developing improved crop varieties.