Population structure and genetic differentiation among the USDA common bean (Phaseolus vulgaris L.) core collection

Abstract

Genetic diversity data were collected from a large population of common bean (Phaseolus vulgaris L.) landraces representing the United States Department of Agriculture core collection. The data were based on microsatellite data from all linkage groups. A procedure was developed to determine if we collected sufficient marker data to adequately estimated pairwise diversity. The diversity data were used to define populations using distance and modelbased approaches. Genetic differentiation and genetic isolation by distance data were collected. Diversity was also compared for markers linked and unlinked to domestication loci. Using a model-based approach, the landraces were divided into the traditional Middle American and Andean gene pools. Diversity was greater for the Middle American gene pool. Six Middle American and three Andean subpopulations were defined, and the Middle American subpopulations exhibited strong geographic identity. Unlike other studies, seed size varied considerably with subpopulations, and a number of the subpopulations contained landraces from multiple common bean races. All of the subpopulations were highly differentiated, with the Middle American subpopulations showing the greatest differentiation. Genetic isolation by distance was observed among the Middle American and Andean subpopulations but not among subpopulations within a gene pool. Within each gene pool, diversity was lower for markers linked to domestication loci.

Investigation of the domestication of common bean (Phaseolus vulgaris) using multilocus sequence data

“…The close relationship of these is further confirmed by the low F st values between the wild and landraces within each gene pool (0.15 in MA and 0.04 in Andean). The two major subpopulations identified here are consistent with earlier work of Rossi et al (2009), McClean et al (2011). This data clearly suggests that landraces within a gene pool arose by a domestication event specific to that gene pool.…”

“…As for mapping in the MA gene pool, it may even be of further benefit to consider populations derived from the Durango and Jalisco races as a pool and Mesoamerican races as a second pool. Population structure analyses consistently define Durango and Jalisco landraces as a single subpopulation and Mesoamerican genotypes as a second subpopulation (McClean et al 2011). Due to a significant LD decay distance, fewer markers would be necessary.…”

“…First, this will greatly reduce the population structure problem often observed for common bean populations Rossi et al 2009;McClean et al 2011). This will be most beneficial for mapping in the Andean gene pool because the very low genetic differentiation among the races and the number of markers required would be high due to its low LD decay.…”

A reference genome for common bean and genome-wide analysis of dual domestications

“…Initially, 126 wild and 179 landrace genotypes, collected from the full geographic range of the species, were scored with 22 indel markers distributed throughout the genome. A Bayesian analysis was performed on the genotype data within each of the two groups using STRUCTURE software 60,61 with the parameters outlined previously 62 . For the wild genotypes where k is the number of populations, k = 2 best fit the data 63 , and, for the landraces, k = 6 defined 3 Mexican subpopulations, 1 Central American subpopulations and 2 Andean subpopulations.…”

Microsatellite markers in common bean (Phaseolus vulgaris L.)

“…Along with other advantages, SSRs are very useful because data from SSR analysis can be reproducible between laboratories and suitable for comparisons between studies and germplasm sets (Blair et al, 2012). In recent years, to characterize common bean genetic diversity, population structure and determination of evolutionary origin numerous studies have been carried out based on SSRs (Blair et al, 2006b;Díaz and Blair, 2006;Asfaw et al, 2009;Blair et al 2009c;Kwak and Gepts, 2009;Angioi et al, 2010;Lobo Burle et al, 2010;Cabral et al, 2011;Khaidizar et al, 2012;McClean et al, 2012;Gioia et al, 2013;Okii et al, 2014;Carović-Stanko et al, 2017;Leitão et al, 2017).…”