Mining germplasm panels and phenotypic datasets to identify loci for resistance to Phytophthora sojae in soybean.
Kyujung VanWilliam R RollingRuslan M BiyashevRashelle L MatthiesenNilwala S AbeysekaraAlison E RobertsonDeloris J VeneyAnne E DorranceLeah K McHaleM A Saghai MaroofPublished in: The plant genome (2020)
Phytophthora sojae causes Phytophthora root and stem rot of soybean and has been primarily managed through deployment of qualitative Resistance to P. sojae genes (Rps genes). The effectiveness of each individual or combination of Rps gene(s) depends on the diversity and pathotypes of the P. sojae populations present. Due to the complex nature of P. sojae populations, identification of more novel Rps genes is needed. In this study, phenotypic data from previous studies of 16 panels of plant introductions (PIs) were analyzed. Panels 1 and 2 consisted of 448 Glycine max and 520 G. soja, which had been evaluated for Rps gene response with a combination of P. sojae isolates. Panels 3 and 4 consisted of 429 and 460 G. max PIs, respectively, which had been evaluated using individual P. sojae isolates with complex virulence pathotypes. Finally, Panels 5-16 (376 G. max PIs) consisted of data deposited in the USDA Soybean Germplasm Collection from evaluations with 12 races of P. sojae. Using these panels, genome-wide association (GWA) analyses were carried out by combining phenotypic and SoySNP50K genotypic data. GWA models identified two, two, six, and seven novel Rps loci with Panels 1, 2, 3, and 4, respectively. A total of 58 novel Rps loci were identified using Panels 5-16. Genetic and phenotypic dissection of these loci may lead to the characterization of novel Rps genes that can be effectively deployed in new soybean cultivars against diverse P. sojae populations.
Keyphrases
- genome wide
- genome wide association
- dna methylation
- genome wide identification
- copy number
- genetic diversity
- bioinformatics analysis
- electronic health record
- systematic review
- big data
- escherichia coli
- genome wide association study
- staphylococcus aureus
- gene expression
- transcription factor
- rna seq
- cystic fibrosis
- artificial intelligence
- single cell
- deep learning