Newswise — Peach (Sakura) is an economically important fruit, and understanding the genetic basis of its quality traits is of great importance for breeding. Recent advances in genome sequencing have led to the construction of detailed genetic maps, allowing deeper insight into the inheritance of traits. However, complex traits like fruit color remain challenging because multiple factors are involved. Traditional color measurement methods, such as colorimetric systems, often do not provide consistent results. These challenges highlight the need for advanced genomic and computational approaches to improve trait analysis. Based on or due to these challenges, deeper research is needed to improve breeding strategies and enhance genetic mapping.
New research published at (DOI: 10.1093/hr/uhaf087) horticultural research (May 2025) Researchers from the Chinese Academy of Agricultural Sciences and the Institute of Agricultural and Food Research and Technology (IRTA) applied whole-genome resequencing and machine learning to map key fruit quality traits in peaches. This collaboration identifies multiple loci and introduces a new approach for determining fruit color phenotypes, opening new avenues for precision breeding of peaches and potentially other fruit crops.
This study focused on analyzing eight fruit-related traits in peach using a high-density genetic map constructed from 134,277 segregating SNPs in the offspring of two genetically distant peach cultivars. “Medium oil bread #9” and “September Free”. The researchers identified key genes for fruit shape and pulp stone adhesion, along with nine QTL representing important traits such as fruit weight, soluble solids concentration, titratable acidity, and ripening date. One of the key innovations was the use of machine learning for peach fruit color phenotyping, specifically grading the yellow to orange color of the pulp. Traditional methods based on physical colorimetric parameters such as L, a*, and b* scales were ineffective for detecting specific QTLs. A machine learning approach identified two new previously undetectable QTLs, demonstrating that machine learning can improve the accuracy of complex trait phenotyping. This study also provides valuable insights into the genetic architecture of peach fruit quality and contributes to better breeding practices by pinpointing genetic hotspots for fruit color and other quality traits. This approach represents a major advance in the genetic analysis of crops with complex traits.
Jinlong, one of the senior authors Dr. Wu commented, “This study demonstrates the power of combining genome sequencing and machine learning to address the complexity of phenotyping in peach breeding. Improving the accuracy of trait measurements not only improves our understanding of fruit quality traits, but also sets the stage for more accurate and efficient breeding programs. This method could be extended to other crops, accelerating the development of new varieties with improved quality and resilience.”
The integration of machine learning and high-resolution genome mapping of peaches offers exciting possibilities for improving fruit quality traits such as color, shape, and taste. This methodology can speed up the breeding process by increasing the accuracy of phenotyping, reducing environmental noise, and providing a deeper understanding of genetic variation. The findings have important implications for the agricultural industry, particularly for breeders aiming to develop peaches of optimized quality for consumers. Additionally, this approach can be applied to other crops, making it a valuable tool for advancing agricultural genomics and improving global food production efficiency.
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References
Toi
10.1093/hour/uhaf087
Original source URL
https://doi.org/10.1093/hr/uhaf087
Funding information
This research was supported by funding by LabEx AGRO 2011-LABX-002 (under the I-Site Muse framework), coordinated by the Agropolis Foundation (Project ID: 2002-030), the INRAE Plant Genetics and Breeding Department, the France AgriMer CASDAR Project 'RésiDiv' (Project ID: 6846752), and the EU Horizon Innovation Actions. InnOBreed number 101061028.
About horticultural research
horticultural research is an open access journal of Nanjing Agricultural University and was ranked #1 in the Horticulture category of Clarivate's Journal Citation Reports™ in 2023. This journal is committed to publishing original research papers, reviews, perspectives, comments, correspondence, and letters to the editor related to all major horticultural plants and fields, including biotechnology, breeding, cell and molecular biology, evolution, genetics, interspecies interactions, physiology, plant origins and domestication. crops.
