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March 21, 2026The Plant Genome4 citationsOpen Access

Genomics in wheat improvement: Progress and perspectives

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SLShaoshuai LiuSGShuaifeng GengSDSusanne Dreisigacker

Key Points

  • The review aims to summarize the current advancements and future directions in genomic research for wheat improvement.
  • Review of current genomic research efforts in wheat
  • Discussion on CRISPR-Cas9 applications for genome editing
  • Evaluation of pan-genomics and multi-omics studies
  • Analysis of functional genomics and epigenetics in wheat breeding
  • Exploration of artificial intelligence in agricultural practices.
  • Identification of the genetic basis for key agronomic traits such as grain yield and stress tolerance
  • Advances in genome assemblies and functional genomics tools
  • Enhanced understanding of wheat domestication mechanisms
  • CRISPR-Cas9 highlighted as a vital tool for targeted genetic modifications
  • Future research expected to leverage big data analytics and AI for breeding advancements.

Abstract

Bread wheat (Triticum aestivum) remains a major source of food and calories globally, yet its vast genome, polyploidy, and high number of repetitive sequences make genomic research challenging in this crop. In this review, we discuss the progress and future perspectives of genome research in wheat. Current efforts focus on the establishment of genome assemblies, advances in functional genomics, advances in epigenetics, translational genetics, and CRISPR-Cas9 genome editing offers a powerful tool for site-specific genome editing for wheat improvement and functional genetic analysis. These approaches have elucidated the genetic basis of many important agronomic traits such as grain yield, biotic and abiotic stress, and wheat quality. Future aims are expected to expand to pan-genomics, the mechanism of wheat domestication, funnel the outputs of functional genomics for deployment in wheat breeding, multi-omics studies facilitate genetic dissection, and the era of big data: creation, integration and utilization, and artificial intelligence breeding.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/69be38ca6e48c4981c679770https://doi.org/10.1002/tpg2.70176
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