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Michael Koch

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Open access Jul 2026

Introgression of rye chromosome arm 1RS enhances climate resilience in German winter wheat

We provide quantitative evidence that rye chromosome arm 1RS exerts background- and context-dependent effects on yield, stability, and grain protein content in contemporary elite winter wheat. Integrated multi-environment analyses demonstrate that translocation lines exhibit pronounced yield stability across 14 contrasting environments, with ‘Insave’- and ‘Petkus’-derived segments contributing through distinct performance profiles relevant for climate-resilient wheat breeding. Climate extremes increasingly threaten wheat production and yield stability globally. Rye (Secale cereale L.) chromosome arm 1RS has long been deployed in wheat breeding, yet its agronomic performance under drought and its interaction with elite genetic backgrounds remain insufficiently characterized. We evaluated 1RS translocations in elite German winter wheat using integrated molecular diversity analyses, multi-environment field phenotyping, and genomic modeling. Under near-optimal precipitation in 2021, matching the 1961–1990 reference period, the investigated winter wheat panel exhibited considerable genetic variation for yield and agronomic traits, indicating that high yield potential is still present in current German winter wheat breeding germplasm. In contrast, severe drought conditions in 2022 resulted in a 13.7% average yield decline, underscoring the sensitivity of current germplasm to drought stress. We show that 1RS translocations exhibit background-dependent effects on grain yield and stability. In particular, T1AL.1RS rye translocation originating from ‘Insave’ rye and stacked T1AL.1RS/T1BL.1RS translocations combining ‘Insave’ and ‘Petkus’ rye segments demonstrated favorable yield performance under drought conditions, although effects were context-dependent. Genetic modeling confirmed a significant interaction between 1RS translocations and the wheat genetic background, indicating that deployment of 1RS requires consideration of recipient genetic background. Our results highlight substantial response diversity within elite germplasm and demonstrate that targeted introgression of rye chromatin can contribute to improved climate resilience when systematically introgressed into adapted genetic backgrounds. The robust multi-environment field evaluation provides a strong foundation for interpreting translocation effects in modern elite germplasm. Complementary trait-level analyses, particularly root phenotyping and evaluation of double 1RS translocations within uniform genetic backgrounds, will further elucidate the physiological mechanisms underlying the observed responses.

Yeneneh Bekele-Reba, L. Bülow, A. Zaar et al. · Theoretical and Applied Gene... · 0 citations

Open access Aug 2026

Optimizing phenotypic data analysis strategies to enhance genomic prediction in winter wheat breeding

Proper analysis of phenotypic data is essential for reliable genomic prediction (GP) and sustained genetic gain in breeding programs. In this study, we used phenotypic and genotypic data generated from the winter wheat breeding program of Deutsche Saatveredelung AG (DSV), Lippstadt, Germany, comprising 1,941 genotypes and 6,335 SNP markers across three traits: grain yield, plant height, and heading date. We evaluated the impact of different two-stage analysis strategies: one using the environment (year × location combination) as the analysis unit (TS-S1) and the other using the breeding stage as the analysis unit (TS-S2), each with and without accounting for breeding-stage effects (-YesBS and -NoBS), on the computation of best linear unbiased estimates (BLUEs) and genome-wide prediction ability (PA), defined as the correlation between BLUEs and predicted values. The performance of these 4 different second stage models (TS-S1-NoBS, TS-S1-YesBS, TS-S2-NoBS, and TS-S2-YesBS) were evaluated using the extended genomic best linear prediction (EGBLUP) model under 5-fold cross validation (5-fold CV), leave one year out cross validation (LOY-CV) and leave one breeding stage out cross validation (LOBS-CV) scenarios. In the presence of the strong breeding stage effect ignoring the breeding stage in the model resulted in biased BLUEs and overestimated prediction abilities in the 5-fold CV, and underestimated prediction abilities in the LOY-CV and LOBS-CV. These unstable prediction abilities are driven by confounded environmental effects in the BLUEs due to omission of the breeding-stage effect. In contrast, models that accounted for the breeding stage produced more reliable BLUEs and more stable prediction results across all cross-validation scenarios, with TS-S1-YesBS performing best overall. Overall, our findings demonstrate that breeding stage effects mainly arise from differences in growing conditions and must be adequately considered. Therefore, including breeding stage in phenotypic models is critical to obtaining unbiased BLUEs and ensuring accurate genomic prediction and selection decisions.

Ravindra Reddy Gundala, Georg Witte, Jost Doernte et al. · Frontiers in Plant Science · 0 citations