Original Articles Vol. 3 No. 3 (2026): Bioscience Insights Published 2026-10-31

GENOME-WIDE ASSOCIATION STUDIES ACROSS CROPS FOR PREDICTIVE BREEDING UNDER CHANGING CLIMATES

Authors

  • Muhammad Mehraj Riasat Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Muhammad Haseeb Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Sara Faridoon Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Muhammad Waleed Department of Agriculture, Lincoln University College, Petaling Jaya 47301, Malaysia Pakistan Author
  • Muhammad Muddasir Department of Plant Breeding and Genetics, Gomal University, Dera Ismail Khan, Pakistan Pakistan Author
  • Uzair Ahmad Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Ghanzanfar Hameed Awan Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Shafaq Munir Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Atika Liaqat Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Shoaib Khan Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
  • Farhan Tahir Department of Agriculture, Hazara University, Mansehra, Pakistan Pakistan Author
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Abstract

Genome-wide association studies (GWAS) have advanced the understanding of complex agronomic traits; however, their translation into practical crop improvement remains limited. This review provides a cross-crop synthesis of GWAS applications to identify conserved and crop-specific genetic architectures underlying stress resilience and yield stability. Moving beyond descriptive analysis, we propose an integrative framework that connects GWAS outputs with predictive breeding by incorporating phenomics, pangenomics, and environmental interactions. We highlight how conserved pathways enable transferable genetic insights across species, while crop-specific adaptations support targeted breeding strategies. Key challenges, including genotype-by-environment interactions, phenotyping limitations, and population structure, are critically evaluated in the context of predictive accuracy. By bridging gene discovery with breeding application, this work offers a conceptual roadmap for next-generation, data-driven crop improvement under changing environmental conditions.

Keywords

  • Crop improvement; Climate-resilient crops; Stress tolerance; Yield stability; Genomic selection; High-throughput phenotyping; Pangenome; G×E interaction

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