|Coyne, Clarice - Clare|
Submitted to: Pisum Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2007
Publication Date: 1/10/2008
Citation: Varshney, R.K., Coyne, C.J., Swamy, P., Hoisington, D. 2007. Molecular identification of genetically distinct accessions in the USDA chickpea core collection. Pisum Genetics 39: 32-33.
Interpretive Summary: Understanding of the molecular diversity in germplasm collections has several applications and advantages. These include genebank management issues and association mapping studies. Genebank management uses of molecular diversity information include maintaining genetic diversity, increasing diversity through knowledge-based acquisition, reducing redundancy and creating association mapping studies populations. Association mapping studies have increased the possible ways of utilizing the genetic diversity of the 6,193 accessions in the USDA chickpea germplasm collection. The initial step for using the chickpea core collection for these studies is determining the underlying population structure to improve the likelihood of finding true associations between a gene and trait.
Technical Abstract: Knowledge of the molecular genetic variation of the accessions of core collections will be important for their efficient use in breeding programs, and for conservation purposes. The present study was undertaken for genotyping the part of the USDA chickpea core collection (Hannan et al 1994) with 20 microsatellite or simple sequence repeat (SSR) markers. In addition to understand the molecular diversity in the core collection, the genetic relationship was studied. A total of 376 accessions from the USDA chickpea core collection were genotyped. Twenty SSR markers revealed a total of 388 alleles among the 376 accessions. In the USDA core collection, the shared allele frequency (SAF) varied from 7.5% to 47.5% with an average of 21.6%. This suggests a higher level of genetic diversity present in the germplasm investigated. In the present study, the structure of the population was determined by using K=4 based on model-based (Bayesian) clustering algorithm.