bioRxivGeneticsPreprint
Development and validation of KASP markers for a novel powdery mildew resistance gene in wheat using BSR-Seq analysis
Ramandeep Kaur, Vikrant TyagiiD, Raman DhariwaliD, Imran SheikhiD, Harcharan Singh DhaliwaliD, M. SivasamyiD, Thamaraikannan SivakumariD, Sundeep KumariD, Vikas Kumar Ravat, Neeraj Kumar VasisthaiD
Abstract
Wheat powdery mildew occurs as a devastating infection caused by the pathogenic fungi Blumeeria graminis f. sp. tritici (Bgt). The management of disease becomes much more effective when host resistance methods are employed. The WL711-2U/2B line served as the introgressed line to identify genes linked with powdery mildew resistance. This allowed us to look into the potential resistance components in response to powdery mildew infection. The resistance status of the line towards powdery mildew developed from a single dominant gene named PmAT. Bulked segregant RNA-Seq (BSR-Seq) together with previous QTL-Seq SNP markers helped identify strong candidate regions in chromosome arm 2B extending from 531114848 bp to 530235618 bp. The LOD score from this region reached 72.4 with a PVE% of 64.7, thus proving its main role in resistance. The physical position was subsequently locked up, which was different from the previously identified genes on the same chromosome arm in its position, suggesting that it is most likely a new Pm gene. The QTL interval contained five potential genes that we identified during this research. The five candidate genes directs the protein production, which performs functions linked to the pentatricopeptide repeat family as well as B-box-type zinc finger domain, P-loop-containing nucleoside triphosphate hydrolase, and plant peroxidase. HighlightThe identification of a new wheat powdery mildew resistance gene (PmAT) using a combination of BSR-Seq and QTL-Seq analysis, which located five candidate genes specifically on chromosome 2B.
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- approach: statistical model
- Citations
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