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Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic...

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Autores principales: Bari, Md. Abdullah Al, Zheng, Ping, Viera, Indalecio, Worral, Hannah, Szwiec, Stephen, Ma, Yu, Main, Dorrie, Coyne, Clarice J., McGee, Rebecca J., Bandillo, Nonoy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740293/
https://www.ncbi.nlm.nih.gov/pubmed/35003202
http://dx.doi.org/10.3389/fgene.2021.707754
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author Bari, Md. Abdullah Al
Zheng, Ping
Viera, Indalecio
Worral, Hannah
Szwiec, Stephen
Ma, Yu
Main, Dorrie
Coyne, Clarice J.
McGee, Rebecca J.
Bandillo, Nonoy
author_facet Bari, Md. Abdullah Al
Zheng, Ping
Viera, Indalecio
Worral, Hannah
Szwiec, Stephen
Ma, Yu
Main, Dorrie
Coyne, Clarice J.
McGee, Rebecca J.
Bandillo, Nonoy
author_sort Bari, Md. Abdullah Al
collection PubMed
description Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction’s potential to a set of 482 pea (Pisum sativum L.) accessions—genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components—for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
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spelling pubmed-87402932022-01-08 Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction Bari, Md. Abdullah Al Zheng, Ping Viera, Indalecio Worral, Hannah Szwiec, Stephen Ma, Yu Main, Dorrie Coyne, Clarice J. McGee, Rebecca J. Bandillo, Nonoy Front Genet Genetics Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction’s potential to a set of 482 pea (Pisum sativum L.) accessions—genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components—for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8740293/ /pubmed/35003202 http://dx.doi.org/10.3389/fgene.2021.707754 Text en Copyright © 2021 Bari, Zheng, Viera, Worral, Szwiec, Ma, Main, Coyne, McGee and Bandillo. 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 Genetics
Bari, Md. Abdullah Al
Zheng, Ping
Viera, Indalecio
Worral, Hannah
Szwiec, Stephen
Ma, Yu
Main, Dorrie
Coyne, Clarice J.
McGee, Rebecca J.
Bandillo, Nonoy
Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction
title Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction
title_full Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction
title_fullStr Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction
title_full_unstemmed Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction
title_short Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction
title_sort harnessing genetic diversity in the usda pea germplasm collection through genomic prediction
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740293/
https://www.ncbi.nlm.nih.gov/pubmed/35003202
http://dx.doi.org/10.3389/fgene.2021.707754
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