Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction

Abstract In hard‐winter wheat (Triticum aestivum L.) breeding, the evaluation of end‐use quality is expensive and time‐consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this...

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Autores principales:	Shichen Zhang‐Biehn, Allan K. Fritz, Guorong Zhang, Byron Evers, Rebecca Regan, Jesse Poland
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/8d682625ddfb428aa4b0f48794aad3c2
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spelling	oai:doaj.org-article:8d682625ddfb428aa4b0f48794aad3c22021-12-05T07:50:11ZAccelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction1940-337210.1002/tpg2.20164https://doaj.org/article/8d682625ddfb428aa4b0f48794aad3c22021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20164https://doaj.org/toc/1940-3372Abstract In hard‐winter wheat (Triticum aestivum L.) breeding, the evaluation of end‐use quality is expensive and time‐consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome‐wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard‐winter wheat. Advanced breeding lines (n = 462) from 2015–2017 were genotyped using genotyping‐by‐sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker‐assisted breeding. Candidate genes for newly associated loci are phosphate‐dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end‐use quality traits. As a baseline, univariate GS models had 0.25–0.55 prediction accuracy in cross‐validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end‐use quality.Shichen Zhang‐BiehnAllan K. FritzGuorong ZhangByron EversRebecca ReganJesse PolandWileyarticlePlant 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 Shichen Zhang‐Biehn Allan K. Fritz Guorong Zhang Byron Evers Rebecca Regan Jesse Poland Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
description	Abstract In hard‐winter wheat (Triticum aestivum L.) breeding, the evaluation of end‐use quality is expensive and time‐consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome‐wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard‐winter wheat. Advanced breeding lines (n = 462) from 2015–2017 were genotyped using genotyping‐by‐sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker‐assisted breeding. Candidate genes for newly associated loci are phosphate‐dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end‐use quality traits. As a baseline, univariate GS models had 0.25–0.55 prediction accuracy in cross‐validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end‐use quality.
format	article
author	Shichen Zhang‐Biehn Allan K. Fritz Guorong Zhang Byron Evers Rebecca Regan Jesse Poland
author_facet	Shichen Zhang‐Biehn Allan K. Fritz Guorong Zhang Byron Evers Rebecca Regan Jesse Poland
author_sort	Shichen Zhang‐Biehn
title	Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
title_short	Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
title_full	Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
title_fullStr	Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
title_full_unstemmed	Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
title_sort	accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/8d682625ddfb428aa4b0f48794aad3c2
work_keys_str_mv	AT shichenzhangbiehn acceleratingwheatbreedingforendusequalitythroughassociationmappingandmultivariategenomicprediction AT allankfritz acceleratingwheatbreedingforendusequalitythroughassociationmappingandmultivariategenomicprediction AT guorongzhang acceleratingwheatbreedingforendusequalitythroughassociationmappingandmultivariategenomicprediction AT byronevers acceleratingwheatbreedingforendusequalitythroughassociationmappingandmultivariategenomicprediction AT rebeccaregan acceleratingwheatbreedingforendusequalitythroughassociationmappingandmultivariategenomicprediction AT jessepoland acceleratingwheatbreedingforendusequalitythroughassociationmappingandmultivariategenomicprediction
_version_	1718372567804280832

Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic prediction

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