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

Abstract

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.

Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

“…The GS models could be retrained by incorporating data from additional environments in subsequent years. High prediction accuracies for stability could better predict the performance of genotypes at multiple locations during breeding cycles [82].…”

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

“…A previous study from our group showed that GS accuracies varied from 0.27 to 0.81 for 14 end-use quality traits using nine different uni-trait models (Sandhu et al, 2021c). Statistical models used for training the uni-or multi-trait GS models play an important role in evaluating performance (Jia and Jannink, 2012).…”

Transcriptome Reprogramming of Tomato Orchestrate the Hormone Signaling Network of Systemic Resistance Induced by Chaetomium globosum

“…Chaetomium globosum is a biocontrol agent which has been found effective against a large number of plant pathogens operating through various mechanisms, such as mycoparasitism, antibiosis, nutrient competitiveness, and inducing systemic resistance in plants (Soytong et al, 2001 ; Sandhu et al, 2021a ). The induced resistance mechanism of C. globosum is least explored except a few reports stating the effectiveness of induced resistance in wheat against tan spot and spot blotch diseases (Istifadah and McGee, 2006 ; Biswas et al, 2012 ).…”

“…Chaetomium globosum is a biocontrol agent which has been found effective against a large number of plant pathogens operating through various mechanisms, such as mycoparasitism, antibiosis, nutrient competitiveness, and inducing systemic resistance in plants (Soytong et al, 2001;Sandhu et al, 2021a).…”