Newswise — Peach (Prunus persica) is an economically important fruit, and understanding the genetic basis of its quality traits is crucial for breeding. Recent advances in genome sequencing have led to the construction of detailed genetic maps, enabling deeper insights into the inheritance of traits. However, complex traits like fruit color remain challenging due to the multiple factors involved. Traditional methods for measuring color, such as colorimetric systems, often fail to provide consistent results. These challenges highlight the need for advanced genomic and computational approaches to improve trait analysis. Based on these challenges, or due to these issues, deeper research is needed to refine breeding strategies and enhance genetic mapping.
In a new study published (DOI: 10.1093/hr/uhaf087) in Horticulture Research (May 2025), researchers from the Chinese Academy of Agricultural Sciences and the Institute of Agrifood Research and Technology (IRTA) applied whole-genome resequencing and machine learning to map key fruit quality traits in peaches. This collaborative study identifies multiple genetic loci and introduces a novel approach for phenotyping fruit color, opening new pathways for precision breeding in peaches and potentially other fruit crops.
The study focused on analyzing eight fruit-related traits in peaches using a high-density genetic map constructed from 134,277 segregating SNPs in the progeny of two genetically distant peach cultivars, ‘Zhongyou Pan #9’ and ‘September Free’. Researchers identified major genes for fruit shape and flesh adhesion to the stone, alongside nine QTLs for important traits such as fruit weight, soluble solids concentration, titratable acidity, and maturity date. One of the major innovations was the use of machine learning for phenotyping peach fruit color, specifically for grading yellow to orange flesh. Traditional methods, based on physical colorimetric parameters like the L, a*, and b* scales, were ineffective in detecting certain QTLs. The machine learning approach identified two new QTLs that were previously undetectable, demonstrating that machine learning can refine the accuracy of complex trait phenotyping. The study also provides valuable insights into the genetic architecture of peach fruit quality, contributing to better breeding practices by pinpointing genetic hotspots for fruit color and other quality traits. This approach marks a significant advancement in the genetic analysis of crops with complex traits.
Dr. Jinlong Wu, one of the senior authors, comments, “This study demonstrates the power of combining genomic sequencing with machine learning to address the complexities of phenotyping in peach breeding. By refining the accuracy of trait measurement, we are not only improving our understanding of fruit quality traits but also setting the stage for more precise, efficient breeding programs. This method can be extended to other crops, potentially accelerating the development of new varieties with improved quality and resilience.”
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