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Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat

Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped w...

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Autores principales: Sehgal, Deepmala, Rosyara, Umesh, Mondal, Suchismita, Singh, Ravi, Poland, Jesse, Dreisigacker, Susanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064468/
https://www.ncbi.nlm.nih.gov/pubmed/32194596
http://dx.doi.org/10.3389/fpls.2020.00197
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author Sehgal, Deepmala
Rosyara, Umesh
Mondal, Suchismita
Singh, Ravi
Poland, Jesse
Dreisigacker, Susanne
author_facet Sehgal, Deepmala
Rosyara, Umesh
Mondal, Suchismita
Singh, Ravi
Poland, Jesse
Dreisigacker, Susanne
author_sort Sehgal, Deepmala
collection PubMed
description Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index Pi (measure of stability). Sixteen associations with GY were “environment-specific” with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For Pi, 8 associations were from chromosomes 4B and 7B, indicating ‘hot spot’ regions for stability. Epistatic interactions contributed to an additional 5–9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9–10% increase in prediction accuracy, whereas for Pi this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat.
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spelling pubmed-70644682020-03-19 Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat Sehgal, Deepmala Rosyara, Umesh Mondal, Suchismita Singh, Ravi Poland, Jesse Dreisigacker, Susanne Front Plant Sci Plant Science Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index Pi (measure of stability). Sixteen associations with GY were “environment-specific” with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For Pi, 8 associations were from chromosomes 4B and 7B, indicating ‘hot spot’ regions for stability. Epistatic interactions contributed to an additional 5–9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9–10% increase in prediction accuracy, whereas for Pi this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat. Frontiers Media S.A. 2020-03-04 /pmc/articles/PMC7064468/ /pubmed/32194596 http://dx.doi.org/10.3389/fpls.2020.00197 Text en Copyright © 2020 Sehgal, Rosyara, Mondal, Singh, Poland and Dreisigacker. http://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
Sehgal, Deepmala
Rosyara, Umesh
Mondal, Suchismita
Singh, Ravi
Poland, Jesse
Dreisigacker, Susanne
Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
title Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
title_full Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
title_fullStr Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
title_full_unstemmed Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
title_short Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
title_sort incorporating genome-wide association mapping results into genomic prediction models for grain yield and yield stability in cimmyt spring bread wheat
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064468/
https://www.ncbi.nlm.nih.gov/pubmed/32194596
http://dx.doi.org/10.3389/fpls.2020.00197
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