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Journal article
Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat
BMC genomics, Vol.24(1), 259
2023
Abstract
Background
Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance.
Results
Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development.
Conclusion
The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.
Details
- Title
- Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat
- Authors/Creators
- Sandeep Kumar - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirDinesh Kumar Saini - Punjab Agricultural UniversityFarkhandah Jan - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirSofora Jan - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirMohd Tahir - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirIvica Djalovic - Institute of Field and Vegetable CropsDragana Latkovic - University of Novi SadMohd Anwar Khan - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirSundeep Kumar - National Bureau of Plant Genetic ResourcesV. K. Vikas - Indian Council of Agricultural ResearchUpendra Kumar - Chaudhary Charan Singh Haryana Agricultural UniversitySundip Kumar - Govind Ballabh Pant University of Agriculture and TechnologyNarendra Singh Dhaka - Govind Ballabh Pant University of Agriculture and TechnologyOm Parkash Dhankher - University of Massachusetts AmherstSachin Rustgi - Clemson UniversityReyazul Rouf Mir - Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir
- Publication Details
- BMC genomics, Vol.24(1), 259
- Publisher
- BioMed Central Ltd
- Number of pages
- 19
- Identifiers
- 991005840056507891
- Copyright
- © The Author(s) 2023
- Murdoch Affiliation
- Centre for Crop and Food Innovation
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.4 Crop Science
- 3.4.96 QTL
- Web Of Science research areas
- Biotechnology & Applied Microbiology
- Genetics & Heredity
- ESI research areas
- Molecular Biology & Genetics