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Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy

Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection (GS) has lately emerged as a promising technique to increase the a...

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Autores principales: Tayeh, Nadim, Klein, Anthony, Le Paslier, Marie-Christine, Jacquin, Françoise, Houtin, Hervé, Rond, Céline, Chabert-Martinello, Marianne, Magnin-Robert, Jean-Bernard, Marget, Pascal, Aubert, Grégoire, Burstin, Judith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648083/
https://www.ncbi.nlm.nih.gov/pubmed/26635819
http://dx.doi.org/10.3389/fpls.2015.00941
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author Tayeh, Nadim
Klein, Anthony
Le Paslier, Marie-Christine
Jacquin, Françoise
Houtin, Hervé
Rond, Céline
Chabert-Martinello, Marianne
Magnin-Robert, Jean-Bernard
Marget, Pascal
Aubert, Grégoire
Burstin, Judith
author_facet Tayeh, Nadim
Klein, Anthony
Le Paslier, Marie-Christine
Jacquin, Françoise
Houtin, Hervé
Rond, Céline
Chabert-Martinello, Marianne
Magnin-Robert, Jean-Bernard
Marget, Pascal
Aubert, Grégoire
Burstin, Judith
author_sort Tayeh, Nadim
collection PubMed
description Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection (GS) has lately emerged as a promising technique to increase the accuracy and gain of marker-based selection. It uses genome-wide molecular marker data to predict the breeding values of candidate lines to selection. A collection of 339 genetic resource accessions (CRB339) was subjected to high-density genotyping using the GenoPea 13.2K SNP Array. Genomic prediction accuracy was evaluated for thousand seed weight (TSW), the number of seeds per plant (NSeed), and the date of flowering (BegFlo). Mean cross-environment prediction accuracies reached 0.83 for TSW, 0.68 for NSeed, and 0.65 for BegFlo. For each trait, the statistical method, the marker density, and/or the training population size and composition used for prediction were varied to investigate their effects on prediction accuracy: the effect was large for the size and composition of the training population but limited for the statistical method and marker density. Maximizing the relatedness between individuals in the training and test sets, through the CDmean-based method, significantly improved prediction accuracies. A cross-population cross-validation experiment was further conducted using the CRB339 collection as a training population set and nine recombinant inbred lines populations as test set. Prediction quality was high with mean Q(2) of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop GS strategies in pea.
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spelling pubmed-46480832015-12-03 Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy Tayeh, Nadim Klein, Anthony Le Paslier, Marie-Christine Jacquin, Françoise Houtin, Hervé Rond, Céline Chabert-Martinello, Marianne Magnin-Robert, Jean-Bernard Marget, Pascal Aubert, Grégoire Burstin, Judith Front Plant Sci Plant Science Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection (GS) has lately emerged as a promising technique to increase the accuracy and gain of marker-based selection. It uses genome-wide molecular marker data to predict the breeding values of candidate lines to selection. A collection of 339 genetic resource accessions (CRB339) was subjected to high-density genotyping using the GenoPea 13.2K SNP Array. Genomic prediction accuracy was evaluated for thousand seed weight (TSW), the number of seeds per plant (NSeed), and the date of flowering (BegFlo). Mean cross-environment prediction accuracies reached 0.83 for TSW, 0.68 for NSeed, and 0.65 for BegFlo. For each trait, the statistical method, the marker density, and/or the training population size and composition used for prediction were varied to investigate their effects on prediction accuracy: the effect was large for the size and composition of the training population but limited for the statistical method and marker density. Maximizing the relatedness between individuals in the training and test sets, through the CDmean-based method, significantly improved prediction accuracies. A cross-population cross-validation experiment was further conducted using the CRB339 collection as a training population set and nine recombinant inbred lines populations as test set. Prediction quality was high with mean Q(2) of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop GS strategies in pea. Frontiers Media S.A. 2015-11-17 /pmc/articles/PMC4648083/ /pubmed/26635819 http://dx.doi.org/10.3389/fpls.2015.00941 Text en Copyright © 2015 Tayeh, Klein, Le Paslier, Jacquin, Houtin, Rond, Chabert-Martinello, Magnin-Robert, Marget, Aubert and Burstin. 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) or licensor 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
Tayeh, Nadim
Klein, Anthony
Le Paslier, Marie-Christine
Jacquin, Françoise
Houtin, Hervé
Rond, Céline
Chabert-Martinello, Marianne
Magnin-Robert, Jean-Bernard
Marget, Pascal
Aubert, Grégoire
Burstin, Judith
Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy
title Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy
title_full Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy
title_fullStr Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy
title_full_unstemmed Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy
title_short Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy
title_sort genomic prediction in pea: effect of marker density and training population size and composition on prediction accuracy
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648083/
https://www.ncbi.nlm.nih.gov/pubmed/26635819
http://dx.doi.org/10.3389/fpls.2015.00941
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