Jim McCreight leads the Melon team. Their objectives are to Map and develop markers for disease resistance in Melon and introgress, pyramid/stack resistances into advanced breeding lines.
CucCAP Project
Contributions to the CucCAP project by the Bioinformatics Team, Cucurbit Crop Breeding Teams, and the Integrated Disease Management Team.
Genomics and Bioinformatics Team | 2021 Progress Report
2021 CucCAP Cucumber Team Publications and Presentations
CucCAP researchers genetically characterize more than 2000 melon accessions in the U.S. National Plant Germplasm System
The U.S. National Plant Germplasm System maintains a melon germplasm collection from worldwide melon production areas and regions where primitive melons exist. The CucCAP team genetically characterized the collection to increase understanding of genetic diversity, phylogenetic relationships, and population structure of the collection, and to improve melon taxonomic classifications. A core collection was developed from the analysis to provide a public resource for future research and genomics-assisted breeding. Thirty-five morphological characters were evaluated in the core collection to identify genomic regions potentially related to fruit quality and other horticultural traits important in melon improvement.
Watermelon Team | 2021 Progress Report
Cucumber Team | 2021 Progress Report
Early Career Scientist Spotlight | Feifan Chen
Feifan Chan’s work in Yiqun Weng’s lab focuses on on pathogen resistance to downy mildew and powdery mildew in cucumber.
CucCAP scientists translate genomic studies into novel detection method for precision management of downy mildew outbreaks
Downy mildew caused by Pseudoperonospora cubensis is the most destructive foliar disease affecting cucurbit crops. Genomic studies by Dr. Lina Quesada-Ocampo and colleagues at North Carolina State University showed that different isolates preferentially infect different cucurbit crops. From this information, a multiplex PCR-based assay was combined with spore trapping to identify which crops are most at risk. These results can facilitate timely and crop-specific fungicide application prior to appearance of symptoms in sentinel plots.
