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Key message <p>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.</p> Abstract <p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月22日

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

摘要

Key message

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.

Abstract

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.