Microsatellite diversity and broad scale geographic structure in a model legume: building a set of nested core collection for studying naturally occurring variation in Medicago truncatula

Abstract

Background: Exploiting genetic diversity requires previous knowledge of the extent and structure of the variation occurring in a species. Such knowledge can in turn be used to build a core-collection, i.e. a subset of accessions that aim at representing the genetic diversity of this species with a minimum of repetitiveness. We investigate the patterns of genetic diversity and population structure in a collection of 346 inbred lines representing the breadth of naturally occurring diversity in the Legume plant model Medicago truncatula using 13 microsatellite loci distributed throughout the genome.

Results: We confirm the uniqueness of all these genotypes and reveal a large amount of genetic diversity and allelic variation within this autogamous species. Spatial genetic correlation was found only for individuals originating from the same population and between neighbouring populations. Using a model-based clustering algorithm, we identified four main genetic clusters in the set of individuals analyzed. This stratification matches broad geographic regions. We also identified a set of "admixed" individuals that do not fit with this population structure scheme.

Conclusion: The stratification inferred is discussed considering potential historical events like expansion, refuge history and admixture between neighbouring groups. Information on the allelic richness and the inferred population structure are used to build a nested core-collection. The set of inbred lines and the core collections are publicly available and will help coordinating efforts for the study of naturally occurring variation in the growing Medicago truncatula community.

Figure 1

Geographical distribution of individuals comprising the groups inferred using microsatellite polymorphism. The stratification of the collection obtained assuming K = 2 (A), or K = 4, plotting only accessions assigned with a high membership into a group, p > 0.6, (B), or plotting all the individuals (C). We used the output yielding the highest posterior probability to assign each genotype to a group.

Figure 2

Comparison of the efficiency of random and "M" sampling strategies. (A) The score, calculated as the number of alleles captured in each core collection summed over 6 loci chosen as marker loci, of a pure random strategy (black) and the M strategy (grey) are compared for various Core (collection) sizes. For each core size, the score of each method reported here is an average over 30 independent core collections. (B) The score, calculated as the number of alleles captured in each core collection summed over 7 loci chosen as target loci, of a pure random strategy (black) and the M strategy (grey) are compared for various Core (collection) sizes. For each core size, the score of each method reported here is an average over 30 independent sampled core collections. See methods for details about the implementation of each sampling strategy.

Figure 3

Comparison of the efficiency of random and stratified sampling strategies. The scores, the number of alleles captured in each core collection summed over the 13 loci used in this study, of a pure random strategy (black) and a stratified random strategy (grey) are compared for various Core (collection) sizes. For each core size, the score of each method reported here is an average over a hundred independent core collections. The score of the set nested core collections retained to represent the diversity of our Medicago truncatula collection (CC8 to CC96) are represented by grey circles.