Browsing by Person "Spiller, Monika"

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Insights into a genomics‐based pre‐breeding program in wheat
(2025) Meyenberg, Carina; Thorwarth, Patrick; Spiller, Monika; Kollers, Sonja; Reif, Jochen Christoph; Longin, Carl Friedrich Horst; Meyenberg, Carina; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Thorwarth, Patrick; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Spiller, Monika; KWS LOCHOW GmbH, Bergen, Germany; Kollers, Sonja; KWS LOCHOW GmbH, Bergen, Germany; Reif, Jochen Christoph; Leibnitz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany; Longin, Carl Friedrich Horst; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
Continuous intercrossing of the best‐performing wheat ( Triticum aestivum L.) elite lines has resulted in genetic gains for a wide range of traits. However, this approach can also reduce genetic diversity, which potentially limits the long‐term genetic improvement. The use of plant genetic resources (PGRs) is therefore considered as crucial to maintain, or even increase, genetic variability in breeding to address future challenges in agriculture in a sustainable manner. Pre‐breeding programs aim to incorporate untapped genetic diversity into an elite germplasm background. Since there is limited knowledge exchange and few publications on how to run pre‐breeding programs efficiently, we report here our latest pre‐breeding scheme and key lessons learned from a decade of wheat pre‐breeding. Our study is based on genotypic and phenotypic data from 390 pre‐breeding lines coming from multiple locations and 4 years of yield trials. We used the genotypic data to estimate the genetically estimated parental contribution (GEPC) of PGRs in pre‐breeding lines. Considerable variation in GEPC between pre‐breeding lines were found even within the same cross. Combining both genotypic and phenotypic data, we compared different scenarios for genome‐wide predictions. Predicting new lines based on calibrations developed across previous years, we determined prediction abilities ranging between 0.34 and 0.69 for grain yield and 0.53 and 0.71 for sedimentation volume, depending on the predicted dataset. Finally, we showed that targeted pre‐breeding yields a small number of promising pre‐breeding lines that perform at the level of the most important commercial varieties.