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GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal fe...

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Autores principales: Jubair, Sheikh, Tucker, James R., Henderson, Nathan, Hiebert, Colin W., Badea, Ana, Domaratzki, Michael, Fernando, W. G. Dilantha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716695/
https://www.ncbi.nlm.nih.gov/pubmed/34975945
http://dx.doi.org/10.3389/fpls.2021.761402
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author Jubair, Sheikh
Tucker, James R.
Henderson, Nathan
Hiebert, Colin W.
Badea, Ana
Domaratzki, Michael
Fernando, W. G. Dilantha
author_facet Jubair, Sheikh
Tucker, James R.
Henderson, Nathan
Hiebert, Colin W.
Badea, Ana
Domaratzki, Michael
Fernando, W. G. Dilantha
author_sort Jubair, Sheikh
collection PubMed
description Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (–1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.
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spelling pubmed-87166952021-12-31 GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley Jubair, Sheikh Tucker, James R. Henderson, Nathan Hiebert, Colin W. Badea, Ana Domaratzki, Michael Fernando, W. G. Dilantha Front Plant Sci Plant Science Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (–1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods. Frontiers Media S.A. 2021-12-16 /pmc/articles/PMC8716695/ /pubmed/34975945 http://dx.doi.org/10.3389/fpls.2021.761402 Text en Copyright © 2021 Jubair, Tucker, Henderson, Hiebert, Badea, Domaratzki and Fernando. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Jubair, Sheikh
Tucker, James R.
Henderson, Nathan
Hiebert, Colin W.
Badea, Ana
Domaratzki, Michael
Fernando, W. G. Dilantha
GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley
title GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley
title_full GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley
title_fullStr GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley
title_full_unstemmed GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley
title_short GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley
title_sort gptransformer: a transformer-based deep learning method for predicting fusarium related traits in barley
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716695/
https://www.ncbi.nlm.nih.gov/pubmed/34975945
http://dx.doi.org/10.3389/fpls.2021.761402
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