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

2021 ◽  
Author(s):  
Karansher Sandhu ◽  
Shruti Sunil Patil ◽  
Michael Pumphrey ◽  
Arron Carter
Keyword(s):  
Deep Learning ◽  
Genomic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Spectral Information ◽  
Learning Models ◽  
Wheat Breeding Program

scholarly journals Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program

2021 ◽  
Author(s):  
Karansher S. Sandhu ◽  
Shruti S. Patil ◽  
Michael O. Pumphrey ◽  
Arron H. Carter
Keyword(s):  
Deep Learning ◽  
Protein Content ◽  
Genomic Selection ◽  
Grain Protein Content ◽  
Wheat Breeding ◽  
Spectral Information ◽  
Grain Protein ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program

AbstractPrediction of breeding values and phenotypes 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 in the context of GS. Spectral reflectance indices further provide information about various physiological parameters previously undetectable in plants. This research explores the potential of multi-trait (MT) machine and deep learning models for predicting grain yield and grain protein content in wheat using spectral information in GS models. This study compares the performance of four machine and deep learning-based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models. The dataset consisted of 650 recombinant inbred lines from a spring wheat breeding program, grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. MT-GS models performed 0-28.5% and −0.04-15% superior to the UT-GS models for predicting grain yield and grain protein content. Random forest and multilayer perceptron were the best performing machine and deep learning models to predict both traits. These two models performed similarly under UT and MT-GS models. 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 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.Core IdeasPotential for combining high throughput phenotyping, machine and deep learning in breeding.Multi-trait models exploit information from secondary correlated traits efficiently.Spectral information improves genomic selection models.Deep learning can aid plant breeders owing to increased data generated in 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 Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program

2018 ◽  
Vol 8 (8) ◽  
pp. 2735-2747 ◽  
Author(s):  
Vikas Belamkar ◽  
Mary J. Guttieri ◽  
Waseem Hussain ◽  
Diego Jarquín ◽  
Ibrahim El-basyoni ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Yield Trials

scholarly journals The use of unbalanced historical data for genomic selection in an international wheat breeding program

2013 ◽  
Vol 154 ◽  
pp. 12-22 ◽  
Author(s):  
Julie C. Dawson ◽  
Jeffrey B. Endelman ◽  
Nicolas Heslot ◽  
Jose Crossa ◽  
Jesse Poland ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Historical Data ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Wheat Breeding Program

scholarly journals Practical application of genomic selection in a doubled-haploid winter wheat breeding program

2017 ◽  
Vol 37 (10) ◽  
Author(s):  
Jiayin Song ◽  
Brett F. Carver ◽  
Carol Powers ◽  
Liuling Yan ◽  
Jaroslav Klápště ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Doubled Haploid ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Practical Application ◽  
Wheat Breeding Program

scholarly journals Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1591
Author(s):  
Sebastian Michel ◽  
Franziska Löschenberger ◽  
Ellen Sparry ◽  
Christian Ametz ◽  
Hermann Bürstmayr
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Wheat Breeding ◽  
Single Step ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Record Keeping ◽  
Line Breeding ◽  
Cost Efficient

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

scholarly journals Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program

2015 ◽  
Vol 8 (3) ◽  
Author(s):  
Marcio P. Arruda ◽  
Patrick J. Brown ◽  
Alexander E. Lipka ◽  
Allison M. Krill ◽  
Carrie Thurber ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Genomic Selection ◽  
Fusarium Head Blight Resistance ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Blight Resistance ◽  
Head Blight ◽  
Wheat Breeding Program ◽  
Selection For

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Crop Science ◽  
2016 ◽  
Vol 56 (5) ◽  
pp. 2165-2179 ◽  
Author(s):  
Bettina Lado ◽  
Pablo González Barrios ◽  
Martín Quincke ◽  
Paula Silva ◽  
Lucía Gutiérrez
Keyword(s):  
Genomic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Unbalanced Data ◽  
Environment Interaction ◽  
Wheat Breeding Program ◽  
Genotype Environment Interaction

scholarly journals Genomic Selection for Processing and End‐Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program

2016 ◽  
Vol 9 (2) ◽  
Author(s):  
Sarah D. Battenfield ◽  
Carlos Guzmán ◽  
R. Chris Gaynor ◽  
Ravi P. Singh ◽  
Roberto J. Peña ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Bread Wheat ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Spring Bread Wheat ◽  
Wheat Breeding Program ◽  
Selection For ◽  
End Use
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