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<p>The planet hosts half a million plant species exhibiting a spectacular diversity of plant forms with genomes driving phenotypic variations. The genome information exists for less than 1% of species, limiting quantitative genomic studies in natural populations. This review explores how recent advances in cutting-edge genomic and phenomic techniques extended genome-wide association studies (GWAS) to wild, non-model species and other natural populations. We also discuss the incorporation of diverse bioinformatic tools into comprehensive in-silico pipelines and recommend implementing machine learning algorithms to address methodological challenges. The critical literature synthesis highlights several scopes of GWAS, bringing natural populations into the spotlight of genomic research. Thus, the study presents GWAS as a cornerstone for advancing quantitative genomics in natural populations. This shift holds great promise for understanding adaptation, trait evolution, and conservation genetics across diverse plant germplasm.</p>

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Genome-wide association study bridging genomics–phenomics gap in natural plant populations

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Sarbani Roy,
  • Hari Shankar Gadri,
  • Vikas Sharma,
  • Md. Asif Chowdhary,
  • Rohini Dwivedi,
  • Pankaj Bhardwaj

摘要

The planet hosts half a million plant species exhibiting a spectacular diversity of plant forms with genomes driving phenotypic variations. The genome information exists for less than 1% of species, limiting quantitative genomic studies in natural populations. This review explores how recent advances in cutting-edge genomic and phenomic techniques extended genome-wide association studies (GWAS) to wild, non-model species and other natural populations. We also discuss the incorporation of diverse bioinformatic tools into comprehensive in-silico pipelines and recommend implementing machine learning algorithms to address methodological challenges. The critical literature synthesis highlights several scopes of GWAS, bringing natural populations into the spotlight of genomic research. Thus, the study presents GWAS as a cornerstone for advancing quantitative genomics in natural populations. This shift holds great promise for understanding adaptation, trait evolution, and conservation genetics across diverse plant germplasm.