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Genetic variation in drought resilience-related traits among wheat multiple synthetic derivative lines: insights for climate resilience breeding.

ABSTRACT: Twenty-four wheat lines, developed by Aegilops tauschii Coss. introgressions and previously selected for heat or salinity stress tolerance, were evaluated under a drought-rewatering-drought cycle for two years. The objective was to select breeding lines that are resilient to more than one abiotic stress. The experiment was designed in alpha lattice with three replications. Drought was imposed by withholding water during flowering. The results revealed considerable genetic variability in physio-agronomic traits, reflecting the variation in the introgressed segments. High heritability estimates (above 47%) were recorded for most traits, including days to 50% heading, plant height, and thousand-grain weight, indicating the genetic control of these traits which may be useful for cultivar development. The trait-trait correlations within and between water regimes highlighted a strong association among the genetic factors controlling these traits. Some lines exhibited superior performance in terms of stress tolerance index and mean productivity compared with their backcross parent and elite cultivars commonly grown in hot and dry areas. Graphical genotyping revealed unique introgressed segments on chromosomes 4B, 6B, 2D, and 3D in some drought-resilient lines which may be linked to drought resilience. Therefore, we recommend these lines for further breeding to develop climate-resilient wheat varieties.

SUBMITTER: Itam MO

PROVIDER: S-EPMC8661488 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

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Genetic variation in drought resilience-related traits among wheat multiple synthetic derivative lines: insights for climate resilience breeding.

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