Council for Agricultural Research and Economics (CREA), Research Centre for Animal Production and Aquaculture, viale Piacenza 29, 26900 Lodi, Italy.
Ecole Nationale Supérieure Agronomique (ENSA), Laboratoire d'Amélioration Intégrative des Productions Végétales (C2711100), Rue Hassen Badi, El Harrach, Alger DZ16200, Algeria.
Int J Mol Sci. 2020 Mar 31;21(7):2414. doi: 10.3390/ijms21072414.
Terminal drought is the main stress limiting pea ( 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.
终端干旱是限制地中海环境中豌豆（L.）籽粒产量的主要胁迫因素。本研究旨在探讨基因型×环境（GE）互作模式，基于测序基因型的单核苷酸多态性（SNP）标记，为严重干旱下的产量定义一个基于基因组选择（GS）模型，并将 GS 与表型选择（PS）和标记辅助选择（MAS）进行比较。来自三个相关 RIL 群体的 288 个品系在意大利北部的受控胁迫（MS）环境、摩洛哥的 Marchouch 和阿尔及利亚的 Alger 进行了评估。通过 Ridge Regression 最佳线性无偏预测（rrBLUP）和贝叶斯套索模型评估了各环境内、各环境间和各群体间的预测能力。在中等到严重胁迫环境中，GE 相互作用特别大。在 MS 环境中进行的概念验证实验中，基于 MS 环境和 Marchouch 数据构建的 GS 模型应用于独立的材料，将表现最好的品系与表现中等和较差的品系区分开来，并产生了与 PS 相似的实际产量增益。当考虑相同的选择成本时，后一种结果意味着 GS 效率会更高，这与预测的效率结果部分一致。GS 利用了干旱逃避和内在耐旱性，其选择效率比 MAS 高 18%（尽管选择之间没有显著差异），并且具有中等至高的跨群体预测能力。在严重干旱下，GS 可以有效地提高产量，同时降低成本。
