Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models
Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic...
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MDPI AG
2021-07-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/10/7/689 |
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| author | Karansher Singh Sandhu Meriem Aoun Craig F. Morris Arron H. Carter |
| author_facet | Karansher Singh Sandhu Meriem Aoun Craig F. Morris Arron H. Carter |
| author_sort | Karansher Singh Sandhu |
| collection | DOAJ |
| description | Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits. |
| first_indexed | 2024-03-10T09:44:51Z |
| format | Article |
| id | doaj.art-3e42aef617884be49795badc4c72acf2 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-10T09:44:51Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-3e42aef617884be49795badc4c72acf22023-11-22T03:16:35ZengMDPI AGBiology2079-77372021-07-0110768910.3390/biology10070689Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning ModelsKaransher Singh Sandhu0Meriem Aoun1Craig F. Morris2Arron H. Carter3Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USADepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USAUSDA-ARS Western Wheat Quality Laboratory, E-202 Food Quality Building, Washington State University, Pullman, WA 99164, USADepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USABreeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.https://www.mdpi.com/2079-7737/10/7/689deep learningend-use qualitygenomic selectionmachine learningwheat breeding |
| spellingShingle | Karansher Singh Sandhu Meriem Aoun Craig F. Morris Arron H. Carter Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models Biology deep learning end-use quality genomic selection machine learning wheat breeding |
| title | Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models |
| title_full | Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models |
| title_fullStr | Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models |
| title_full_unstemmed | Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models |
| title_short | Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models |
| title_sort | genomic selection for end use quality and processing traits in soft white winter wheat breeding program with machine and deep learning models |
| topic | deep learning end-use quality genomic selection machine learning wheat breeding |
| url | https://www.mdpi.com/2079-7737/10/7/689 |
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