Mapping resistance to leaf and stripe rusts in bread Triticum aestivum L. germplasm through GWAS and field phenotyping
European Journal of Plant Pathology, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Publication Date: 2026
- Doi Number: 10.1007/s10658-026-03236-z
- Journal Name: European Journal of Plant Pathology
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Environment Index, Geobase, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Pharma Collection (ProQuest)
- Keywords: Bread wheat, Leaf rust, Puccinia striiformis, Puccinia triticina, QTL analysis
- Open Archive Collection: AVESIS Open Access Collection
- Karadeniz Technical University Affiliated: Yes
Abstract
This study aims to evaluate the phenotypic and molecular levels of resistance of Triticum aestivum L. (bread wheat) genotypes to leaf rust (Puccinia triticina) and stripe rust (Puccinia striiformis). Increasing climate pressures and the rapid evolution of pathogens threaten wheat production, underscoring the critical role of understanding genetic resistance mechanisms for sustainable solutions. The study evaluated 175 wheat lines sourced from CIMMYT for disease severity and infection type under natural field conditions; subsequently, genome-wide association analysis was used to correlate these phenotypic data with existing molecular markers. The analyses identified four lines resistant to stripe rust, 33 lines resistant to leaf rust, and two lines (no. 69 and 164) resistant to both diseases. One hundred seventy-six marker-trait associations were identified on 19 of 21 chromosomes, particularly on chromosomes 3B, 5B, 7A, and the D genome, with significant p-values (p < 0.001). These QTL regions explain 6.8–27.0% of phenotypic variation in disease severity. The resistant lines obtained are suitable as genetic resources for classical breeding programs and can be effectively evaluated in marker-assisted selection studies. Thus, the study produces fundamental scientific knowledge and offers concrete solutions to increase wheat productivity in regions such as Pakistan, where rust epidemics are prevalent.