Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought
- PMID: 32244428
- PMCID: PMC7177262
- DOI: 10.3390/ijms21072414
Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought
Abstract
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.
Keywords: Pisum sativum; drought tolerance; genetic gain; genomic selection; genotype × environment interaction; grain yield; inter-population predictive ability; marker-assisted selection.
Conflict of interest statement
The authors declare no conflict of interest.
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Grants and funding
- 613551/Seventh Framework Programme
- ArimNet REFORMA/Ministero delle Politiche Agricole Alimentari e Forestali
- ArimNet REFORMA/Ministère de l'agriculture, de la pêche maritime, du développement rurale et des eaux et forêts du Maroc
- ArimNet REFORMA/Ministère de l'Agriculture, du Développement rural et de la Pêche de Algerie
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