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<p>Recent advances in genome sequencing technologies have profoundly transformed soybean research by enabling deeper insights into its genetic architecture, domestication history, and complex trait evolution. This review summarizes the current landscape of soybean genomic technologies, key research developments, and their practical applications in breeding. It also addresses ongoing challenges and explores strategies to maximize the utility of genomic data. The development of high-quality reference genomes—beginning with the Williams 82 assembly and extending to elite cultivars and wild accessions—has facilitated genome-wide discovery of single-nucleotide polymorphisms (SNPs), insertion-deletion (InDels), and structural variants (SVs). Whole-genome resequencing (WGRS) has improved quantitative trait loci (QTL) mapping, gene discovery, and genome-assisted breeding (GAB), enabling high-resolution detection of polymorphisms related to yield, stress tolerance, and seed composition. Pangenome construction, exemplified by projects like PanSoy, has revealed significant genomic diversity and clarified the roles of core and dispensable genes in trait expression and environmental adaptation. Complementary high-throughput genotyping platforms, such as SNP arrays and genotyping-by-sequencing (GBS), have enhanced the precision of genome-wide association studies (GWAS) and accelerated the deployment of marker-assisted selection (MAS) and genomic selection (GS). Despite these advances, challenges persist in data integration, sequencing costs, and computational infrastructure. Future research must emphasize the integration of multi-omics approaches—including transcriptomics, proteomics, and epigenomics—with genomic data to unravel regulatory mechanisms. Furthermore, machine learning and artificial intelligence (AI) hold great promise for improving variant discovery and predictive breeding models. In summary, the integration of cutting-edge genomic tools provides a robust foundation for accelerating soybean breeding and ensuring sustainable crop improvement.</p>

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Harnessing genomic technologies for modern soybean breeding

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Chae Won Yoon,
  • Yejin Kang,
  • Jong-Wook Chung,
  • Mariano Bulos,
  • Tae-Young Hwang,
  • Ju-Kyung Yu

摘要

Recent advances in genome sequencing technologies have profoundly transformed soybean research by enabling deeper insights into its genetic architecture, domestication history, and complex trait evolution. This review summarizes the current landscape of soybean genomic technologies, key research developments, and their practical applications in breeding. It also addresses ongoing challenges and explores strategies to maximize the utility of genomic data. The development of high-quality reference genomes—beginning with the Williams 82 assembly and extending to elite cultivars and wild accessions—has facilitated genome-wide discovery of single-nucleotide polymorphisms (SNPs), insertion-deletion (InDels), and structural variants (SVs). Whole-genome resequencing (WGRS) has improved quantitative trait loci (QTL) mapping, gene discovery, and genome-assisted breeding (GAB), enabling high-resolution detection of polymorphisms related to yield, stress tolerance, and seed composition. Pangenome construction, exemplified by projects like PanSoy, has revealed significant genomic diversity and clarified the roles of core and dispensable genes in trait expression and environmental adaptation. Complementary high-throughput genotyping platforms, such as SNP arrays and genotyping-by-sequencing (GBS), have enhanced the precision of genome-wide association studies (GWAS) and accelerated the deployment of marker-assisted selection (MAS) and genomic selection (GS). Despite these advances, challenges persist in data integration, sequencing costs, and computational infrastructure. Future research must emphasize the integration of multi-omics approaches—including transcriptomics, proteomics, and epigenomics—with genomic data to unravel regulatory mechanisms. Furthermore, machine learning and artificial intelligence (AI) hold great promise for improving variant discovery and predictive breeding models. In summary, the integration of cutting-edge genomic tools provides a robust foundation for accelerating soybean breeding and ensuring sustainable crop improvement.