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Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of tw...

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Autores principales: Bhandari, Aditi, Bartholomé, Jérôme, Cao-Hamadoun, Tuong-Vi, Kumari, Nilima, Frouin, Julien, Kumar, Arvind, Ahmadi, Nourollah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502484/
https://www.ncbi.nlm.nih.gov/pubmed/31059529
http://dx.doi.org/10.1371/journal.pone.0208871
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author Bhandari, Aditi
Bartholomé, Jérôme
Cao-Hamadoun, Tuong-Vi
Kumari, Nilima
Frouin, Julien
Kumar, Arvind
Ahmadi, Nourollah
author_facet Bhandari, Aditi
Bartholomé, Jérôme
Cao-Hamadoun, Tuong-Vi
Kumari, Nilima
Frouin, Julien
Kumar, Arvind
Ahmadi, Nourollah
author_sort Bhandari, Aditi
collection PubMed
description Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.
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spelling pubmed-65024842019-05-23 Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice Bhandari, Aditi Bartholomé, Jérôme Cao-Hamadoun, Tuong-Vi Kumari, Nilima Frouin, Julien Kumar, Arvind Ahmadi, Nourollah PLoS One Research Article Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments. Public Library of Science 2019-05-06 /pmc/articles/PMC6502484/ /pubmed/31059529 http://dx.doi.org/10.1371/journal.pone.0208871 Text en © 2019 Bhandari et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bhandari, Aditi
Bartholomé, Jérôme
Cao-Hamadoun, Tuong-Vi
Kumari, Nilima
Frouin, Julien
Kumar, Arvind
Ahmadi, Nourollah
Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
title Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
title_full Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
title_fullStr Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
title_full_unstemmed Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
title_short Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
title_sort selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502484/
https://www.ncbi.nlm.nih.gov/pubmed/31059529
http://dx.doi.org/10.1371/journal.pone.0208871
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