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Publications

Journal Article Review Epublish

PubMed: 39861529 DOI: 10.3390/plants14020175

Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects.

Plants (Basel) 14(2)

Authors: Umar Hayat, Cao Ke, Lirong Wang, Gengrui Zhu, Weichao Fang, Xinwei Wang, Changwen Chen, Yong Li, Jinlong Wu

Affiliations

  • The Key Laboratory of the Gene Resources Evaluation and Utilization of Horticultural Crop [Fruit Tree], Ministry of Agriculture, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
  • Zhongyuan Research Center, Chinese Academy of Agricultural Sciences, Xinxiang 453003, China.
  • The Key Laboratory of the Gene Resources Evaluation and Utilization of Horticultural Crop [Fruit Tree], Ministry of Agriculture, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China. Zhongyuan Research Center, Chinese Academy of Agricultural Sciences, Xinxiang 453003, China.

Abstract

Modern breeding technologies and the development of quantitative trait locus (QTL) mapping have brought about a new era in peach breeding. This study examines the complex genetic structure that underlies the morphology of peach fruits, paying special attention to the interaction between genome editing, genomic selection, and marker-assisted selection. Breeders now have access to precise tools that enhance crop resilience, productivity, and quality, facilitated by QTL mapping, which has significantly advanced our understanding of the genetic determinants underlying essential traits such as fruit shape, size, and firmness. New technologies like CRISPR/Cas9 and genomic selection enable the development of cultivars that can withstand climate change and satisfy consumer demands with unprecedented precision in trait modification. Genotype-environment interactions remain a critical challenge for modern breeding efforts, which can be addressed through high-throughput phenotyping and multi-environment trials. This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. This study outlines a roadmap that prioritizes the development of superior cultivars utilizing cutting-edge methods and technologies in order to address evolving agricultural and environmental challenges.

MeSH

Breeding, Multiple Chronic Conditions, Genome, Fruit, Environment, Quantitative Trait Loci

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