biostudies-literatureGundala RRBundesanstalt für Landwirtschaft und Ernährung224https://www.ebi.ac.uk/biostudies/studies/S-EPMC12373547biostudies-literatureUnknown138(9)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.TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikHarnessing big data for enhanced genome-wide prediction in winter wheat breeding.PMC12373547FKZ2818408B18Pfeiffer NDoernte JFoerster JRapp MReif JCSpiller MZhao YAvenhaus UKoch MWimmer VGundala RRGils MKirchhoff MKollers SWolf MEckhoff WMfalseHarnessing 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.2025-01-01T00:00:00Z2025 Aug2026-05-08T10:46:10.369Z2026-04-07T23:47:19.964ZS-EPMC123735474084463910.1007/s00122-025-05007-6