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Related Experiment Video

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Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine

Karansher Singh Sandhu¹, Meriem Aoun¹, Craig F Morris²

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

|August 6, 2021

Summary

This summary is machine-generated.

Machine and deep learning models accurately predict wheat end-use quality traits, improving breeding efficiency. These advanced models outperform traditional methods, enabling early-stage selection of superior genotypes for enhanced grain yield.

Keywords:

deep learning end-use quality genomic selection machine learning wheat breeding

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Area of Science:

Agricultural Science
Genetics
Computational Biology

Background:

Wheat breeding programs prioritize grain yield, biotic/abiotic stress resistance, and end-use quality.
End-use quality screening is often secondary to yield due to resource constraints.
Genomic selection offers predictive capabilities using genome-wide markers.

Purpose of the Study:

To evaluate machine and deep learning models for predicting fourteen end-use quality traits in a winter wheat breeding program.
To compare the predictive performance of machine/deep learning models against traditional methods (RRBLUP, Bayesian).
To assess the potential of these models for early generation selection and large-scale breeding applications.

Main Methods:

Utilized a dataset of 666 wheat genotypes screened over five years (2015-2019) at two locations.
Explored nine models: Random Forest, Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, RRBLUP, and Bayesian models.
Performed cross-validation, forward, and across-location predictions to assess model accuracy.

Main Results:

Prediction accuracies for end-use quality traits ranged from 0.45-0.81 (cross-validation), 0.29-0.55 (forward), and 0.27-0.50 (across locations).
Forward prediction accuracy generally increased with larger training datasets, particularly for machine and deep learning models.
Deep learning models demonstrated superior performance compared to RRBLUP and Bayesian models across all prediction scenarios.

Conclusions:

Machine and deep learning models show high accuracy for predicting wheat end-use quality traits.
These models support early generation prediction, facilitating the advancement of superior genotypes.
The superior performance of machine and deep learning models justifies their integration into large-scale breeding programs for complex trait prediction.