McBreen Jordan, Babar Md Ali, Jarquin Diego, Khan Naeem, Harrison Steve, DeWitt Noah, Mergoum Mohamed, Lopez Ben, Boyles Richard, Lyerly Jeanette, Murphy J Paul, Shakiba Ehsan, Sutton Russel, Ibrahim Amir, Howell Kimberly, Smith Jared H, Brown-Guedira Gina, Tiwari Vijay, Santantonio Nicholas, Van Sanford David A
Department of Agronomy, University of Florida, Gainesville, Florida, USA.
School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, Louisiana, USA.
Plant Genome. 2025 Mar;18(1):e20532. doi: 10.1002/tpg2.20532. Epub 2024 Nov 19.
Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high-throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross-validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic-only models, indicated by higher specificity, which is the proportion of correctly identified top-yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.
通过将高通量表型（HTP）数据与基因组信息相结合来提高小麦（Triticum aestivum）育种中的预测建模准确性，对于最大化遗传增益至关重要。在这项跨越美国东南部四个地点、为期3年的研究中，通过不同的交叉验证方法研究了预测籽粒产量（GY）的模型。结果表明，整合了基因组和HTP数据的多变量综合模型具有优越性，特别是在准确预测不同地点和年份的GY方面。这些纳入HTP的模型的预测准确率在0.59至0.68之间，而在不同年份和地点的不同预测场景下进行测试时，仅基因组模型的预测准确率为0.40 - 0.54。综合模型在新环境中表现出卓越的泛化能力，并且在使用不同数据集进行训练时能达到最高准确率。随着模型纳入多年的数据，预测准确率提高，这突出了在建模方法中考虑时间动态的重要性。该研究表明，在跨年份和地点预测品系方面，多变量预测优于基因组预测方法。基于多变量预测选出的前25%品系的百分比高于仅基因组模型，这由更高的特异性表明，特异性是指在不同地点和年份正确识别出的高产品系与观察到的前25%表现相匹配的比例。此外，该研究还探讨了基于同一年其他地点以及先前测试地点的新年份数据来预测未测试地点的情况。研究结果表明，综合模型能够有效地外推到新环境，突出了它们在指导育种策略方面的潜力。
