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<p>The extended generation cycles and high genomic heterozygosity of forest trees have long hindered investigations into the genetic basis of quantitative traits, impeding progress in molecular breeding applications. Recent advances in genome-wide association studies (GWAS), empowered by next-generation sequencing technologies, now offer unprecedented opportunities to dissect complex trait architectures and identify causal allelic variants in tree species. This review critically examines the evolving role of GWAS in forest tree genetics, emphasizing its achievements in mapping quantitative trait loci (QTLs) and characterizing functionally relevant alleles for breeding. We further analyze the unresolved challenge of “missing heritability” in tree GWAS and propose integrative approaches to mitigate this gap, including the development of high-throughput phenotyping platforms for capturing trait dynamics across environments, synergistic integration of multi-omics data (genomics, transcriptomics, epigenomics) via advanced computational models, and construction of pan-genome references to resolve structural variations in highly heterozygous genomes. Finally, we discuss the translational potential of GWAS-driven strategies in modern forestry, particularly for enhancing marker-assisted selection of climate-adaptive traits, optimizing wood properties, and shortening domestication timelines. By bridging methodological innovations with practical breeding applications, this synthesis aims to accelerate the translation of genetic discoveries into sustainable forest management practices.</p>

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Genome-wide association studies in forestry

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Xiangrun Meng,
  • Yinyin Fu,
  • Yanran Qi,
  • Zhuoying Jin,
  • Peng Li,
  • Yalin Sang

摘要

The extended generation cycles and high genomic heterozygosity of forest trees have long hindered investigations into the genetic basis of quantitative traits, impeding progress in molecular breeding applications. Recent advances in genome-wide association studies (GWAS), empowered by next-generation sequencing technologies, now offer unprecedented opportunities to dissect complex trait architectures and identify causal allelic variants in tree species. This review critically examines the evolving role of GWAS in forest tree genetics, emphasizing its achievements in mapping quantitative trait loci (QTLs) and characterizing functionally relevant alleles for breeding. We further analyze the unresolved challenge of “missing heritability” in tree GWAS and propose integrative approaches to mitigate this gap, including the development of high-throughput phenotyping platforms for capturing trait dynamics across environments, synergistic integration of multi-omics data (genomics, transcriptomics, epigenomics) via advanced computational models, and construction of pan-genome references to resolve structural variations in highly heterozygous genomes. Finally, we discuss the translational potential of GWAS-driven strategies in modern forestry, particularly for enhancing marker-assisted selection of climate-adaptive traits, optimizing wood properties, and shortening domestication timelines. By bridging methodological innovations with practical breeding applications, this synthesis aims to accelerate the translation of genetic discoveries into sustainable forest management practices.