scholarly journals Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program

2021 ◽  
Vol 11 ◽  
Author(s):  
Karansher S. Sandhu ◽  
Dennis N. Lozada ◽  
Zhiwu Zhang ◽  
Michael O. Pumphrey ◽  
Arron H. Carter
Keyword(s):  
Deep Learning ◽  
Spring Wheat ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Recombinant Inbred Lines ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Breeding Program ◽  
Wheat Breeding Program

Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014–2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder’s toolkit for use in large scale breeding programs.

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

2021 ◽  
Author(s):  
Karansher S Sandhu ◽  
Meriem Aoun ◽  
Craig Morris ◽  
Arron H Carter
Keyword(s):  
Deep Learning ◽  
Grain Yield ◽  
Cross Validation ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
End Use

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. Hence, testing is delayed until later stages in the breeding program. Delayed phenotyping results in advancement of inferior end-use quality lines into the program. Genomic selection provides an alternative to predict performance using genome-wide markers. 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 performed 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 trailing. Furthermore, the superior performance of machine and deep learning models strengthen the idea to include them in large scale breeding programs for predicting complex traits.

scholarly journals Breeding Program Optimization for Genomic Selection in Winter Wheat

2020 ◽  
Author(s):  
Megan Calvert ◽  
Byron Evers ◽  
Xu Wang ◽  
Allan Fritz ◽  
Jesse Poland
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Economic Benefit ◽  
Prediction Accuracy ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Program Optimization ◽  
Crop Species ◽  
Wheat Breeding Program ◽  
Basic Set

AbstractDeveloping methodologies in the fields of phenomics and genomic prediction have the potential to increase the production of crop species by accelerating germplasm improvement. The integration of these technologies into germplasm improvement and breeding programs requires evidence that there will be a direct economic benefit to the program. We determined a basic set of parameters, such as prediction accuracy greater than 0.3, the ability to genotype over 7 lines for the cost of one phenotypic evaluation, and heritability levels below 0.4, at which the use of genomic selection would be of economic benefit in terms of genetic gain and operational costs to the Kansas State University (KSU) winter wheat breeding program. The breeding program was then examined to determine whether the parameters benefitting genomic selection were observed or achievable in a practical sense. Our results show that the KSU winter wheat breeding program is at a decision point with regards to their primary means of selection. A few operational changes to increase prediction accuracy would place the program in the parameter space where genomic selection is expected to outpace the current phenotypic selection methodology at a parity of the operation cost and would be of greatest benefit to the program.

Evaluation of Semidwarf Selections from a Spring Wheat Breeding Program 1

1960 ◽  
Vol 52 (12) ◽  
pp. 710-712 ◽  
Author(s):  
F. H. Mcneal ◽  
M. A. Berg ◽  
M. G. Klages
Keyword(s):  
Spring Wheat ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program

Winter increases and kernel hardness testing in a spring wheat breeding program

1992 ◽  
Vol 72 (1) ◽  
pp. 247-249 ◽  
Author(s):  
K. G. Briggs ◽  
Zhang Hongli
Keyword(s):  
Spring Wheat ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Kernel Hardness ◽  
Hardness Testing ◽  
Wheat Breeding Program ◽  
Wheat Cross

Kernel hardness measurements were performed on lines from a Canada Prairie Spring wheat cross. High correlations were found between performance of F2-derived F5 bulks from a winter increase nursery (latitude 33°N) with performance of the related F4 and F6 bulks grown in Edmonton (latitude 54°N). It is proposed that hardness testing of samples increased in winter nurseries may allow greater efficiency in a spring wheat breeding program.Key words: Wheat (spring), kernel hardness, breeding

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