Harnessing big data for enhanced genome-wide prediction in winter wheat breeding

By combining data from different public and private breeding programs for genomic selection, we have increased the size and diversity of the training population, which has led to better predictions of grain yield and plant height in winter wheat compared to using individual training sets. The accuracy of genome-wide prediction is anticipated to improve with an increase in training population size. In our study, we assembled a comprehensive wheat data set consisting of about 18,000 inbred lines and phenotypic data from about 250,000 plots. We evaluated the potential to train genome-wide prediction models using this big data set through data from post-registration trials conducted across a wide range of environments. Our findings demonstrated that using big data can enhance the prediction ability by up to 97% for grain yield and 44% for plant height, outperforming individual training sets. This improvement is primarily attributed to the expansion of the training set size relative to the genetic diversity. In conclusion, big data holds significant potential to accelerate genetic gain in winter wheat predictive breeding, making it a compelling option.

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Publication Details

Journal
Theoretical and Applied Genetics
Published
2025-08-22
DOI
https://doi.org/10.1007/s00122-025-05007-6
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
0.00

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article

Harnessing big data for enhanced genome-wide prediction in winter wheat breeding

Nina Pfeiffer, Sonja Kollers, Valentin Wimmer, Yusheng Zhao et al.
Theoretical and Applied Genetics
Genetic and phenotypic traits in livestock
article

Harnessing big data for enhanced genome-wide prediction in winter wheat breeding

Nina Pfeiffer, Sonja Kollers, Valentin Wimmer, Yusheng Zhao, Martin Kirchhoff, Michael Koch, Jutta Foerster, Ulrike Avenhaus, Jochen C. Reif, Markus Wolf, Matthias Rapp, Monika Spiller, Wera Maria Eckhoff, Mario Gils, Ravindra Reddy Gundala, Jost Doernte
article en

Abstract

By combining data from different public and private breeding programs for genomic selection, we have increased the size and diversity of the training population, which has led to better predictions of grain yield and plant height in winter wheat compared to using individual training sets. The accuracy of genome-wide prediction is anticipated to improve with an increase in training population size. In our study, we assembled a comprehensive wheat data set consisting of about 18,000 inbred lines and phenotypic data from about 250,000 plots. We evaluated the potential to train genome-wide prediction models using this big data set through data from post-registration trials conducted across a wide range of environments. Our findings demonstrated that using big data can enhance the prediction ability by up to 97% for grain yield and 44% for plant height, outperforming individual training sets. This improvement is primarily attributed to the expansion of the training set size relative to the genetic diversity. In conclusion, big data holds significant potential to accelerate genetic gain in winter wheat predictive breeding, making it a compelling option.

Theoretical and Applied GeneticsVol. 138(9)
Deutscher Wetterdienst (DE), Nordhausen University of Applied Sciences (DE), Dutch Centre for Field Ornithology (NL), KWS Saat (Germany) (DE), Jain Irrigation Systems (Israel) (IL), Institute for Ecological Economy Research (DE), Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK) (DE), Energiewerke Nord (Germany) (DE)
Bundesministerium für Ernährung und Landwirtschaft, Bundesanstalt für Landwirtschaft und Ernährung
Openalex Percentile: Top 76%
Genetic and phenotypic traits in livestock
DOIOpen Access PDFOpenAlex
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