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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-11-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20164 |
| _version_ | 1818723315305414656 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T21:08:34Z |
| format | Article |
| id | doaj.art-8d682625ddfb428aa4b0f48794aad3c2 |
| institution | Directory Open Access Journal |
| issn | 1940-3372 |
| language | English |
| last_indexed | 2024-12-17T21:08:34Z |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj.art-8d682625ddfb428aa4b0f48794aad3c22022-12-21T21:32:30ZengWileyThe Plant Genome1940-33722021-11-01143n/an/a10.1002/tpg2.20164Accelerating wheat breeding for end‐use quality through association mapping and multivariate genomic predictionShichen Zhang‐Biehn0Allan K. Fritz1Guorong Zhang2Byron Evers3Rebecca Regan4Jesse Poland5Dep. of Plant Pathology Kansas State Univ. 4024 Throckmorton Plant Sciences Center, 1712 Claflin Rd. Manhattan KS 66506 USADep. of Agronomy Kansas State Univ. 4012 Throckmorton Plant Sciences Center, 1712 Claflin Rd. Manhattan KS 66506 USAAgricultural Research Center‐Hays Kansas State Univ. 1232 240th Ave. Hays KS 67601 USADep. of Plant Pathology Kansas State Univ. 4024 Throckmorton Plant Sciences Center, 1712 Claflin Rd. Manhattan KS 66506 USADep. of Grain Science and Industry Kansas State Univ. Shellenberger 108 Manhattan KS 66506 USADep. of Plant Pathology Kansas State Univ. 4024 Throckmorton Plant Sciences Center, 1712 Claflin Rd. Manhattan KS 66506 USAAbstract 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.https://doi.org/10.1002/tpg2.20164 |
| spellingShingle | 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 The Plant Genome |
| title | 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_short | 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 |
| url | https://doi.org/10.1002/tpg2.20164 |
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