Advancements in QTL mapping and GWAS applications in plant improvement


Altaf M. T., Tatar M., Ali A., Ali W., Mortazvi P., Ölmez F., ...Daha Fazla

TURKISH JOURNAL OF BOTANY, cilt.48, ss.376-426, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 48
  • Basım Tarihi: 2024
  • Doi Numarası: 10.55730/1300-008x.2824
  • Dergi Adı: TURKISH JOURNAL OF BOTANY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Geobase, Veterinary Science Database
  • Sayfa Sayıları: ss.376-426
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

In modern plant breeding, molecular markers have become indispensable tools, allowing the precise identification of genetic

loci linked to key agronomic traits. These markers provide critical insight into the genetic architecture of crops, accelerating the selection

of desirable traits for sustainable agriculture. This review focuses on the advancements in quantitative trait locus (QTL) mapping and

genome-wide association studies (GWASs), highlighting their effective roles in identifying complex traits such as stress tolerance, yield,

disease resistance, and nutrient efficiency. QTL mapping identifies the significant genetic regions linked to desired traits, while GWASs

enhance precision using larger populations. The integration of high-throughput phenotyping has further improved the efficiency and

accuracy of QTL research and GWASs, enabling precise trait analysis across diverse conditions. Additionally, next-generation sequencing,

clustered regularly interspaced short palindromic repeats (CRISPR) technology, and transcriptomics have transformed these methods,

offering profound insights into gene function and regulation. Single-cell RNA sequencing further enhances our understanding of plant

responses at the cellular level, especially under environmental stress. Despite this progress, however, challenges persist in optimizing

methods, refining training populations, and integrating these tools into breeding programs. Future studies must aim to enhance genetic

prediction models, incorporate advanced molecular technologies, and refine functional markers to tackle the challenges of sustainable

agriculture.