Location: Plant Genetic Resources Research
Project Number: 1910-21000-024-08-A
Project Type: General Cooperative Agreement
Start Date: Sep 1, 2011
End Date: Aug 31, 2013
We will genotype approximately 400 accessions of the USDA cabbage germplasm collection for SSR markers. This data will be used to evaluate diversity in the cabbage collection to avoid difficulties of environmental influences on phenotypic traits and these markers also offer the additional benefits of being ubiquitous and distributed throughout the plant genome. Co-dominant markers such as microsatellite or simple sequence repeats (SSRs) are often preferred for evaluating genetic diversity due to their increased polymorphic information content (PIC).
This proposal will include multiple types of cabbage (e.g. green, red, and Savoy). We hope this study will also lead to the eventual development of a core collection of cabbage. We will collaborate with the current curator to select 400 accessions that best represent diversity within and among types. We will include historical accessions as well as more recently developed elite material. We will also take into consideration availability, type, usage, geographical origin, agronomic factors and other available passport information when compiling the list of requested material. One hundred and twenty one SSR markers will be used to screen accessions at the seedling stage. These SSR markers have amplified at least one band when screening the parents of our F2:3 broccoli population. An initial screening set of 16 accessions will be utilized to detect amplification and polymorphisms on agarose gels using unlabeled primers. A set of 25 to 30 markers will be chosen on the basis of this initial screen for analysis of the 400 accessions. Primers (25-30) displaying the greatest degree of polymorphisms during initial screening will be used to genotype all 400 lines. A similarity matrix will be generated with Jaccards coefficient, (Sij) using the software package NTsys. A dendogram will be generated from the similarity matrix using the average unweighted group mean (UGMA) and principle component analysis (PCA) estimates will also be generated from the same program. Population genetic substructure will be estimated using the program STUCTURE 2.1 that utilizes a Bayesian algorithm to assign accessions to putative sub-populations (k) in Hardy-Weinberg equilibrium and can provide an estimate of the degree of admixture of the accessions that can be utilized as a matrix of co-factors in structured association programs.