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

Sandhu, Karansher S.; Pd, Mihalyov; Mj, Lewien; Pumphrey, Michael O.; Ah, Carter

doi:10.1101/2021.04.15.440064

Cited by 13 publications

(6 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These low correlations among most end-use quality traits strengthen the fact that no single quality parameter can assist in final variety selection, but that many are needed [1]. Only three end-use quality traits, namely, GPC, FPROT, and FSV, had intermediate heritability values, which were also reported in previous studies due to their complex and polygenic inheritance nature [32,52]. Similarly, comparatively low prediction accuracies obtained from these traits validated the fact for inclusion of genotype by environment interaction or environmental covariates for their prediction [53].…”

Section: Discussionsupporting

confidence: 76%

See 1 more Smart Citation

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

Sandhu

Aoun

Morris

et al. 2021

Biology

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 76%

“…Residuals from the model were used to calculate the adjusted means (line effect). Adjusted means across the environments were calculated following the method implemented in Sandhu et al [18,32] and is as follows…”

Section: Discussionmentioning

confidence: 99%

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

Sandhu

Aoun

Morris

et al. 2021

Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…These low correlations among most end-use quality traits strengthen the fact that no single quality parameter can assist in final variety selection, but that many are needed (Souza et al 2002). Only three end-use quality traits, namely, GPC, FPROT and FSV, had intermediate heritability values, which were also reported in previous studies due to their complex and polygenic inheritance nature (Hayes et al 2017;Sandhu et al 2021c). Similarly, comparatively low prediction accuracies obtained from these traits validated the fact for inclusion of genotype by environment interaction or environmental covariates for their prediction (Monteverde et al 2019).…”

Section: Discussionsupporting

confidence: 72%

“…Adjusted means across the environments were calculated following the method implemented in Sandhu et al (2021c) and is as follows…”

Section: Discussionmentioning

confidence: 99%

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

Sandhu

Aoun

Morris

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Grain yield and grain protein content are highly important target traits in wheat breeding programs and for other cereal grains (Chhabra et al, 2021;Sandhu et al, 2021c). The generally negative correlation between them, along with lower heritability, creates a problem in efficiently select-ing both the traits simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

et al. 2021

Self Cite

View full text Add to dashboard Cite

Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine-and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine-and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine-and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014)(2015)(2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and −0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine-and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine-and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine-and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

show abstract

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

Cited by 13 publications

References 75 publications

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

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

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

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

Contact Info

Product

Resources

About