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

Abstract 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 collect...

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Autores principales:	Karansher Sandhu, Shruti Sunil Patil, Michael Pumphrey, Arron Carter
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/af5dd8233cd340e3a2bc7acfae77f353
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spelling	oai:doaj.org-article:af5dd8233cd340e3a2bc7acfae77f3532021-12-05T07:50:11ZMultitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program1940-337210.1002/tpg2.20119https://doaj.org/article/af5dd8233cd340e3a2bc7acfae77f3532021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20119https://doaj.org/toc/1940-3372Abstract 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 (2014–2016), 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.Karansher SandhuShruti Sunil PatilMichael PumphreyArron CarterWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Karansher Sandhu Shruti Sunil Patil Michael Pumphrey Arron Carter Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
description	Abstract 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 (2014–2016), 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.
format	article
author	Karansher Sandhu Shruti Sunil Patil Michael Pumphrey Arron Carter
author_facet	Karansher Sandhu Shruti Sunil Patil Michael Pumphrey Arron Carter
author_sort	Karansher Sandhu
title	Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
title_short	Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
title_full	Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
title_fullStr	Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
title_full_unstemmed	Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
title_sort	multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/af5dd8233cd340e3a2bc7acfae77f353
work_keys_str_mv	AT karanshersandhu multitraitmachineanddeeplearningmodelsforgenomicselectionusingspectralinformationinawheatbreedingprogram AT shrutisunilpatil multitraitmachineanddeeplearningmodelsforgenomicselectionusingspectralinformationinawheatbreedingprogram AT michaelpumphrey multitraitmachineanddeeplearningmodelsforgenomicselectionusingspectralinformationinawheatbreedingprogram AT arroncarter multitraitmachineanddeeplearningmodelsforgenomicselectionusingspectralinformationinawheatbreedingprogram
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Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

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