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Legume Genetics and Biology: From Mendel’s Pea to Legume Genomics
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Article

Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought

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Council for Agricultural Research and Economics (CREA), Research Centre for Animal Production and Aquaculture, viale Piacenza 29, 26900 Lodi, Italy
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Ecole Nationale Supérieure Agronomique (ENSA), Laboratoire d’Amélioration Intégrative des Productions Végétales (C2711100), Rue Hassen Badi, El Harrach, Alger DZ16200, Algeria
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Institut National de la Recherche Agronomique (INRA), Centre Régional de Rabat, Av. de la Victoire, Rabat BP 415, Morocco
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(7), 2414; https://doi.org/10.3390/ijms21072414
Received: 13 February 2020 / Revised: 27 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Legume Genetics and Biology: From Mendel's Pea to Legume Genomics)
Terminal drought is the main stress limiting pea (Pisum sativum L.) grain yield in Mediterranean environments. This study aimed to investigate genotype × environment (GE) interaction patterns, define a genomic selection (GS) model for yield under severe drought based on single nucleotide polymorphism (SNP) markers from genotyping-by-sequencing, and compare GS with phenotypic selection (PS) and marker-assisted selection (MAS). Some 288 lines belonging to three connected RIL populations were evaluated in a managed-stress (MS) environment of Northern Italy, Marchouch (Morocco), and Alger (Algeria). Intra-environment, cross-environment, and cross-population predictive ability were assessed by Ridge Regression best linear unbiased prediction (rrBLUP) and Bayesian Lasso models. GE interaction was particularly large across moderate-stress and severe-stress environments. In proof-of-concept experiments performed in a MS environment, GS models constructed from MS environment and Marchouch data applied to independent material separated top-performing lines from mid- and bottom-performing ones, and produced actual yield gains similar to PS. The latter result would imply somewhat greater GS efficiency when considering same selection costs, in partial agreement with predicted efficiency results. GS, which exploited drought escape and intrinsic drought tolerance, exhibited 18% greater selection efficiency than MAS (albeit with non-significant difference between selections) and moderate to high cross-population predictive ability. GS can be cost-efficient to raise yields under severe drought. View Full-Text
Keywords: drought tolerance; genotype × environment interaction; genetic gain; genomic selection; grain yield; inter-population predictive ability; marker-assisted selection; Pisum sativum drought tolerance; genotype × environment interaction; genetic gain; genomic selection; grain yield; inter-population predictive ability; marker-assisted selection; Pisum sativum
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MDPI and ACS Style

Annicchiarico, P.; Nazzicari, N.; Laouar, M.; Thami-Alami, I.; Romani, M.; Pecetti, L. Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought. Int. J. Mol. Sci. 2020, 21, 2414. https://doi.org/10.3390/ijms21072414

AMA Style

Annicchiarico P, Nazzicari N, Laouar M, Thami-Alami I, Romani M, Pecetti L. Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought. International Journal of Molecular Sciences. 2020; 21(7):2414. https://doi.org/10.3390/ijms21072414

Chicago/Turabian Style

Annicchiarico, Paolo, Nelson Nazzicari, Meriem Laouar, Imane Thami-Alami, Massimo Romani, and Luciano Pecetti. 2020. "Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought" International Journal of Molecular Sciences 21, no. 7: 2414. https://doi.org/10.3390/ijms21072414

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