Chloroplast Microsatellite Diversity in Phaseolus vulgaris

Abstract

Evolutionary studies that are aimed at defining the processes behind the present level and organization of crop genetic diversity represent the fundamental bases for biodiversity conservation and use. A Mesoamerican origin of the common bean Phaseolus vulgaris was recently suggested through analysis of nucleotide polymorphism at the nuclear level. Here, we have used chloroplast microsatellites to investigate the origin of the common bean, on the basis of the specific characteristics of these markers (no recombination, haploid genome, uniparental inheritance), to validate these recent findings. Indeed, comparisons of the results obtained through analysis of nuclear and cytoplasmic DNA should allow the resolution of some of the contrasting information available on the evolutionary processes. The main outcomes of the present study are: (i) confirmation at the chloroplast level of the results obtained through nuclear data, further supporting the Mesoamerican origin of P. vulgaris, with central Mexico representing the cradle of its diversity; (ii) identification of a putative ancestral plastidial genome, which is characteristic of a group of accessions distributed from central Mexico to Peru, but which have not been highlighted beforehand through analyses at the nuclear level. Finally, the present study suggests that when a single species is analyzed, there is the need to take into account the complexity of the relationships between P. vulgaris and its closely related and partially intercrossable species P. coccineus and P. dumosus. Thus, the present study stresses the importance for the investigation of the speciation processes of these taxa through comparisons of both plastidial and nuclear variability. This knowledge will be fundamental not only from an evolutionary point of view, but also to put P. coccineus and P. dumosus germplasm to better use as a source of useful diversity for P. vulgaris breeding.

Introduction

The wild forms of the common bean Phaseolus vulgaris grow across a wide geographic area of the Americas, from northern Mexico to northwestern Argentina (Toro et al., 1990). Morphological, biochemical, and molecular data have indicated that the wild populations from Mexico, Central America, and Colombia differ from those of southern Peru, Bolivia, and Argentina (Gepts et al., 1986; Delgado-Salinas et al., 1988; Koenig and Gepts, 1989; Gepts and Debouck, 1991; Becerra-Velásquez and Gepts, 1994; Papa and Gepts, 2003; Angioi et al., 2009a; Kwak and Gepts, 2009; Rossi et al., 2009). Indeed, these two groups represent two geographically distinct and isolated gene pools (Mesoamerica and Andes, respectively) that were already present before domestication of the common bean (for reviews, see Papa et al., 2006; Bitocchi et al., 2012, 2013). This complex scenario is further characterized by the presence within the wild forms of a third gene pool that is characteristic of a restricted area in northern Peru and Ecuador (Debouck et al., 1993). Along with accessions from the two main gene pools, wild populations collected in this restricted area have been analyzed according to a portion of the gene encoding for the seed-storage protein phaseolin (Kami et al., 1995). This study showed that the “Inca” phaseolin type I is not present in Central and South America. Moreover, this phaseolin appears to be ancestral to the other phaseolin sequences of P. vulgaris, suggesting that the northern Peru and Ecuador populations were those from which the common bean originated and subsequently spread into Central and South America (Kami et al., 1995). This hypothesis was the most credited until the study of Bitocchi et al. (2012) that analyzed the genetic diversity at five nuclear gene fragments across a wide sample of wild P. vulgaris accessions, where they showed that the wild forms of P. vulgaris originated in Mesoamerica, and most likely in central Mexico. This study also indicated that both the Andean and the northern Peru and Ecuador gene pools originated through different migration events from central Mexico. This conclusion was suggested by the evidence of a bottleneck that occurred in the Andes prior to domestication (Rossi et al., 2009; Nanni et al., 2011; Bitocchi et al., 2012) and to the presence of high genetic structure in Mesoamerica (Bitocchi et al., 2012), with the different genetic groups identified having diverse relationships with the wild populations from northern Peru and Ecuador and from the Andes.

Chloroplast microsatellite (cpSSR) markers are widely used in population genetics and evolutionary studies of plants (for review, see Provan et al., 2001). Due to their specific characteristics, which include a haploid and non-recombinant genome and uniparental inheritance, they have become very useful tools to investigate different evolutionary processes. These include, e.g., historical bottlenecks, founder effects, identification of progenitors of cultivated species, and the role of introgression in crop evolution (for review, see Provan et al., 2001).

In the present study, we used a set of cpSSRs to analyze a wide sample of wild P. vulgaris accessions from the Americas. These cpSSRs have been demonstrated to be very useful to study the diversity and evolution of several legume species, and in particular of P. vulgaris and P. coccineus (Angioi et al., 2009a,b, 2010). The main aim was to investigate the origin of the common bean and to compare the results with those obtained by analyses based on nuclear nucleotide diversity (Bitocchi et al., 2012). Indeed, at the nuclear level, recombination might have affected the data obtained, although to reduce this problem, fragments of a few hundreds of base pairs were used. Thus, the comparison and combination of nuclear and plastidial polymorphism analyses should give complementary insights into the evolutionary history of the common bean, especially considering that such analyses can often provide contrasting information on evolutionary processes (Birky, 1988; McCauley, 1995; Ennos et al., 1999; Provan et al., 1999; Weising and Gardner, 1999; Ishii et al., 2001; Lira et al., 2003; Ueno et al., 2005).

Finally, cpSSR genotyping of a smaller set of P. coccineus accessions was carried out, with the aim being to gain information about the evolutionary relationship between P. coccineus and P. vulgaris.

Materials and Methods

Plant materials

A total of 109 wild accessions of P. vulgaris were analyzed in the present study. These materials encompassed the entire geographical distribution of this species, from northern Mexico to northwestern Argentina, and included seven wild accessions from northern Peru and Ecuador that are characterized by the ancestral phaseolin type I (Debouck et al., 1993; Kami et al., 1995). The geographical distribution of these common bean accessions is shown in Figure 1. Ten wild accessions of P. coccineus were also included. Each accession is represented by an individual plant genotype. A complete list of the accessions studied, along with their “passport” information, is given in Table A1 in Appendix.

Figure 1.

Geographical distribution of the P. vulgaris accessions used in the present study. Latitude and longitude are expressed in the Universal Transverse Mercator system. MW, Mesoamerican wild; AW, Andean wild; PhI, northern Peru and Ecuador.

The seeds were provided by the United States Department of Agriculture (USDA) Western Regional Plant Introduction Station in the USA, the International Center of Tropical Agriculture (CIAT) in Colombia, and the Laboratory of Plant Genetics (D3A) at the Polytechnic University of Marche (UNIVPM) in Italy. Most of these accessions had already been characterized using different types of molecular markers, such as amplified fragment length polymorphism (AFLP; Rossi et al., 2009) and nucleotide data (Nanni et al., 2011; Bitocchi et al., 2012). Moreover a small subset of accessions (15 wild P. vulgaris, eight wild P. coccineus) were analyzed previously by Angioi et al. (2009a) with the same set of cpSSRs.

PCR and cpSSR genotyping

Genomic DNA was extracted from each accession from young leaf tissue of a single, greenhouse-grown plant, using the miniprep extraction method (Doyle and Doyle, 1987). A total of 17 cpSSRs derived from the literature (Weising and Gardner, 1999; Chung and Staub, 2003; Angioi et al., 2009a) were used for the genetic characterization of the whole sample. One of the two SSR primers was end-labeled with a phosphoramidite fluorescent dyes, 6-FAM or HEX. A list of the cpSSRs used in this study is given Table A2 in Appendix. The amplifications were conducted using a Perkin-Elmer 9700 Thermal Cycler (PE Applied Biosystems) in a total volume of 25 μl, which contained 25 ng template DNA, 10 pmol of each primer, 200 μM dNTPs, 1× Taq polymerase buffer, 2.5 mM MgCl₂ and 1 U Taq polymerase (Promega). The PCR conditions were as reported in Table A2 in Appendix. Multiplex PCRs were performed (including two primer pairs that were differently end-labeled, with amplification of SSRs of different sizes under the same amplification conditions). SSR genotyping was carried out using the ABI PRISM 3100-Avant Genetic Analyzer with the GeneScan 7.0 analysis software (PE Applied Biosystems).

Genetic diversity analysis

The percentage of polymorphic loci, the average number of observed alleles per locus (Na), the effective number of alleles per locus (Ne; Kimura and Crow, 1964), the number of private alleles (Np), and the expected heterozygosity (He; Nei, 1978) estimates based on allele frequencies, were computed using the Arlequin software, version 3.5 (Excoffier and Lischer, 2010). The whole sample, and the following partitions of the accessions were considered for these analyses: P. coccineus; P. vulgaris; and within the common bean sample according to the gene pool, the Andean wild (AW), Mesoamerican wild (MW), and northern Peru and Ecuador (PhI) populations.

The differences between the AW and MW populations for the genetic diversity estimates (Ne and He) were tested using Wilcoxon signed-ranks non-parametric test for two groups, arranged for paired observations (i.e., one pair of estimates for each locus; Wilcoxon, 1945; Sokal and Rohlf, 1995).

An ad hoc statistic (ΔH) was used to compare the diversity between the two main gene pools (AW, MW); this estimate measures the loss of diversity of one population compared to another, and it was originally proposed by Vigouroux et al. (2002): ΔH = 1 − (He_POP1/He_POP2), where POP1 refers to the population that shows the lower level of genetic diversity (He) compared to the other population (POP2).

Principal component analysis

Using the JMP software, version 8 (SAS Institute, Inc., 2008), principal component analysis (PCA) was performed from allele frequencies. The same analysis was carried out also to investigate the genetic relationships among the P. vulgaris accessions.

Population structure analysis

A Bayesian model-based approach that was implemented in the Bayesian analysis of population structure (BAPS) software, version 5.3 (Corander et al., 2003), was used to infer the hidden genetic population structure of the whole sample (109 P. vulgaris and 10 P. coccineus accessions), and thus to assign the genotypes into genetically structured groups/populations (K). A spatial genetic mixture analysis was conducted (Corander et al., 2008). This method uses a Markov chain Monte Carlo simulation approach to group samples into variable user-defined numbers (K) of clusters. The best partition of populations into K clusters is identified as the one with the highest marginal log-likelihood. We carried out 10 repetitions of the algorithm for each K ranging between 2 and 20.

The genetic diversity statistics described above were also computed for the genetic groups highlighted by the BAPS analysis (hereafter referred to as clusters). The differences between the clusters identified according to the genetic diversity estimates (Ne, He) were tested using the Wilcoxon signed-ranks non-parametric test for two groups, arranged for paired observations (Wilcoxon, 1945; Sokal and Rohlf, 1995), and the Bonferroni correction for multiple comparisons.

Divergence between populations

The divergence among the P. coccineus and P. vulgaris populations defined a priori according to the gene pools (AW, MW, PhI) were estimated as F_ST (Weir and Cockerham, 1984), D (Jost, 2008), and R_ST (Slatkin, 1995). In contrast to F_ST and D, R_ST contains information not only about the frequency with which particular alleles occur, but also on the evolutionary distance between them, inasmuch as it is measured as the expected squared difference in repeat numbers between alleles. For this reason, it is intended to take advantage of this additional information to provide greater insight into the patterns of relationships among populations (for review, see Holsinger and Weir, 2009). These correspond to the infinite allele and the step-wise mutation models. The significance of the estimates was obtained through permutation tests, using 10,000 permutations. The same divergence estimates were also computed for clusters identified by BAPS analysis. The Arlequin software, version 3.5 (Excoffier and Lischer, 2010), was used.

Comparison of results based on cpSSR data with those obtained using nucleotide data

The sequences of five gene regions (from 500 to 900 bp) for 71 accessions were available from Bitocchi et al. (2012). These five gene fragments include four legume anchor (Leg) markers, developed by Hougaard et al. (2008), and one gene fragment, PvSHP1, developed by Nanni et al. (2011); PvSHP1 is a homolog of the SHATTERPROOF (SHP1) gene, which is involved in the control of fruit shattering in Arabidopsis thaliana. These data allowed a comparison of the data from the population structure analyses carried out using cpSSRs and nuclear sequences. Thus, for the 71 accessions shared between this study and that of Bitocchi et al. (2012), a population structure analysis was carried out using both the cpSSRs and the nucleotide data. For the nucleotide data, the procedures were as described in Bitocchi et al. (2012), while for the cpSSRs, the procedures were the same as reported in the above section.

To compare the geographical distributions of the clusters identified through the cpSSR and nucleotide data, spatial interpolation of membership coefficients (q) was performed according to the kriging method, with each of the clusters identified by population structure analysis, which was implemented in the R packages spatial (http://www.r-project.org/). In the case of the cpSSRs, due to the non-recombinant nature of these markers, which does not allow admixture, the membership coefficients were represented by one or zero (i.e., membership or non-membership to one cluster); thus, the interpolation for plastidial data represents an approximation.

The association between the results obtained by the BAPS analyses carried out with the cpSSR and nucleotide data was tested by analysis of contingency tables with the likelihood ratio chisquared (χ²) test, which was performed using the JMP 8.0 software (SAS Institute, Inc., 2008).

Results

Each of the primer pairs produced a single and clear amplification, and all of the 17 loci studied were polymorphic considering the whole analyzed sample. The size of the amplification products ranged from 79 bp (ccmp3) to 378 bp (ccSSR19). Overall, the number of alleles per locus (Na) ranged from two (cp2) to 12 (ccSSR20); in parallel the same two markers showed the lowest and the highest genetic diversity, He = 0.13 and He = 0.85, respectively (Table A3 in Appendix).

Considering the P. coccineus sample, six out of the 17 loci were monomorphic. For the polymorphic loci, Na ranged from two (cp2, ccSSR2, ccSSR4, ccSSR12, and ccSSR16) to six (ccSSR20). One locus (cp2) was monomorphic in the P. vulgaris sample. For the remaining 16 loci, Na ranged from two (cp3 and ccSSR12) to 11 (ccSSR20). The highest level of genetic diversity was detected for the ccSSR20 locus, as an He of 0.84 for both P. vulgaris and P. coccineus (Table A3 in Appendix).

Genetic diversity analysis

Genetic diversity estimates were computed considering the whole sample and the following major subdivisions: different species (P. vulgaris, P. coccineus) and within the P. vulgaris Andean (AW), Mesoamerican (MW), and northern Peru and Ecuador accession (PhI) populations.

As showed in Table 1, the common bean was characterized by a higher level of genetic diversity (Na, Ne, Np, and He) than P. coccineus. However, the large difference between the size of the two samples suggests caution in the consideration of these estimates.

Table 1.

Genetic diversity estimates computed for all of the 17 cpSSR loci considering the whole sample, the P. vulgaris and P. coccineus samples, and the three P. vulgaris populations defined according to the gene pools.

Accession	N	% polymorphic loci	Na	Ne	Np	Np (freq. ≥ 0.05)	He
All	119	100	5.1	2.6	na	na	0.54
P. vulgaris	109	94.1	4.4	2.5	45	29	0.51
P. coccineus	10	64.7	2.4	1.8	12	12	0.36
P. vulgaris populations
MW	55	88.2	3.9	2.5	7	3	0.54
AW	47	82.4	3.2	1.9	4	3	0.40
PhI	7	82.4	2.5	2.2	3	3	0.49

N, sample size; Na, mean number of observed alleles per locus; Ne, mean effective number of alleles per locus; Np, number of private alleles; Np (freq. ≥ 0.05), number of private alleles with frequency higher than 0.05; He, expected heterozygosity; MW, Mesoamerican wild; AW, Andean wild; PhI, northern Peru and Ecuador; na, not applicable.

Among the three P. vulgaris populations, the MW accessions showed the highest genetic diversity for all of the parameters (Table 1). In particular, considering the populations that represent the two major common bean gene pools (Mesoamerican and Andean), the MW showed a higher level of genetic diversity (Ne = 2.5 and He = 0.54) compared to the AW accessions (Ne = 1.9 and He = 0.40; Table 1). This difference was significant for both the genetic diversity estimates Ne and He (P < 0.02; Wilcoxon signed-ranks non-parametric test for two groups, arranged for paired observations). There was a 26% reduction in genetic diversity (ΔH) of the AW population compared to the MW population.

Principal component analysis

The relationships among all of the individuals considered, including both the P. vulgaris and P. coccineus accessions, were investigated by PCA (Figure 2). The first (PC1) and second (PC2) principal components explain 43.03 and 26.82%, respectively. Three main groups were identified by this analysis, one including eight wild P. coccineus accessions, one including all of the seven PhI, two WA, and 39 WM accessions and one P. coccineus accession, and the remaining 45 WA and 16 WM accessions, and even if more distant, one P. coccineus accessions.

Figure 2.

Genetic relationships within the whole set of accessions, as determined by principal component analysis. MW, Mesoamerican wild; AW, Andean wild; PhI, northern Peru and Ecuador.

Principal component analysis was also performed to investigate the genetic relationships among the P. vulgaris accessions (Figure 3). The first (PC1) and second (PC2) principal components explain 45.73 and 23.65%, respectively. This analysis identified two major groups, as A and B (Figure 3). The majority of the MW accessions (73%; including five of the six Colombian accessions) belonged to group A, along with three AW accessions from northern Argentina (Salta and Tucumán Provinces) and all of the seven PhI accessions. Group B included almost all of the AW accessions (94%) and 15 MW accessions, 14 of which were from central Mexico, and only one from Colombia.

Figure 3.

Genetic relationships within the P. vulgaris accessions, as assessed by principal component analysis. MW, Mesoamerican wild; AW, Andean wild; PhI, northern Peru and Ecuador; (A,B), major groups identified by PCA analysis.

Population structure

The population structure analysis identified four different clusters (C1, C2, C3, C4) as the best partition of the whole sample (all of the 10 best marginal log-likelihood values were for K = 4, with the highest of −1,996.54; Table 2). Cluster C1 was characterized by almost all of the AW accessions (98%) and 13 MW accessions from Central Mexico. Cluster C2 included 21 MW and three PhI accessions, along with two P. coccineus genotypes. There were accessions from all of the three common bean populations in cluster C3 (4, 1, 21 for the PhI, AW, MW populations, respectively), while cluster C4 was exclusive to the remaining eight P. coccineus accessions. The geographical distribution of the P. vulgaris accessions based on the BAPS cluster membership is showed in Figure 4.

Table 2.

Distribution of the accessions into the four cpSSR clusters (C1, C2, C3, C4) identified by the BAPS analysis.

Accession	Cluster
	C1	C2	C3	C4
MW	13	21	21	–
AW	46	–	1	–
PhI	–	3	4	–
P. coccineus	–	2	–	8
Overall	59	26	26	8

MW, Mesoamerican wild; AW, Andean wild; PhI, northern Peru and Ecuador.

Figure 4.

Geographical distribution of the P. vulgaris accessions based on the BAPS cluster membership.

The genetic diversity estimates for the BAPS clusters are showed in Table 3. The three clusters characteristic of P. vulgaris accessions (C1, C2, C3) showed similar levels of genetic diversity (Ne = 2.0, 2.1, 1.8, and He = 0.42, 0.45, 0.36, for C1, C2, C3, respectively). Cluster C4 showed the lowest Ne (1.6) and He (0.29) estimates. However, there were no significant differences in the levels of genetic diversity between these four clusters (Wilcoxon signed-ranks non-parametric tests, after Bonferroni correction).

Table 3.

Genetic diversity estimates computed for the 17 cpSSRs considering the four clusters (C1, C2, C3, and C4) identified by BAPS analysis.

Cluster	N	% polymorphic loci	Na	Ne	Np	Np (freq. ≥ 0.05)	He
C1	59	88.2	3.4	2.0	6	5	0.42
C2	26	88.2	3.2	2.1	3	0	0.45
C3	26	88.2	3.1	1.8	7	3	0.36
C4	8	52.9	1.9	1.6	10	10	0.29

N, sample size; Na, mean number of observed alleles per locus; Ne, mean effective number of alleles per locus; Np, number of private alleles; Np (freq. ≥ 0.05), number of private alleles with frequency higher than 0.05; He, expected heterozygosity.

Divergence between populations

The genetic divergence between the P. vulgaris populations (MW, AW, PhI) and the P. coccineus accessions was estimated as F_ST, D, and R_ST. The F_ST and D estimates were very similar, as expected for populations that have a very low number of unique alleles (Whitlock, 2011), and thus only the F_ST data are shown. The lowest, and non-significant, differentiation was between the PhI and MW populations (F_ST = 0.08; R_ST = 0.12; both non-significant; Table 4). Considering the comparisons among the P. vulgaris populations, the divergence between AW and PhI (F_ST = 0.21; R_ST = 0.70; both significant P ≤ 0.001) was greater than that between AW and MW (F_ST = 0.13; R_ST = 0.24; both significant P ≤ 0.01). The highest values of F_ST were those in the comparisons with the P. coccineus population; however, the MW population showed the lowest levels of differentiation with P. coccineus (F_ST = 0.33; P ≤ 0.001) compared to the other P. vulgaris populations [F_{ST(PhI-P. coccineus)} = 0.38, P ≤ 0.001; F_{ST(AW-P. coccineus)} = 0.49, P ≤ 0.001; Table 4]. The R_ST showed a similar trend, with the MW population being less differentiated than P. coccineus (R_ST = 0.58, P ≤ 0.001), and PhI [R_{ST(PhI-P. coccineus)} = 0.60, P ≤ 0.001], and AW [R_{ST(AW-P. coccineus)} = 0.78, P ≤ 0.001; Table 4].

Table 4.

Genetic divergence (F_ST and R_ST, below and above the diagonal, respectively) within the P. vulgaris populations and with P. coccineus.

	MW	AW	PhI	P. coccineus
MW	–	0.13*	0.08	0.58**
AW	0.24*	–	0.21**	0.78**
PhI	0.12	0.70**	–	0.60**
P. coccineus	0.33**	0.49**	0.38**	–

Significance obtained by 10,000 permutations: **P ≤ 0.001; *P ≤ 0.01.

The same divergence estimates were computed considering the four genetic clusters (C1, C2, C3, C4) identified by the BAPS analysis (Table 5). All of the divergence estimates (for both F_ST and R_ST) were significantly different from zero (P ≤ 0.001). We observed less differentiation (lower F_ST and R_ST) among the three clusters predominated by the P. vulgaris accessions (C1, C2, C3), than between any of these and C4, which was comprised exclusively of P. coccineus accessions. When considering these comparisons with the P. coccineus cluster (C4), the lowest F_ST was with the C2 cluster [F_ST(C2–C4) = 0.39]. R_ST gave a slightly different pattern, with comparisons involving the C3 cluster showing the lowest R_ST (Table 5).

Table 5.

Genetic divergence (F_ST and R_ST, below and above the diagonal, respectively) between the four cpSSR clusters identified by population structure analysis.

	C1	C2	C3	C4
C1	–	0.54**	0.37**	0.90**
C2	0.26**	–	0.15**	0.81**
C3	0.28**	0.37**	–	0.68**
C4	0.50**	0.39**	0.56**	–

Significance obtained by 10,000 permutations: **P ≤ 0.001.

Nucleotide data versus cpSSRs

The availability of sequence data for five gene fragments for 71 out of the 109 P. vulgaris accessions allowed a comparison between these different kinds of data (plastidial and nuclear). Three clusters were identified by the analysis carried out with cpSSRs. They corresponded to clusters (C1, C2, and C3) determined previously using all the accessions, while the Cluster C4 was not determined due to the exclusion, in this comparative analysis, of the P. coccineus accessions. Six clusters (B1, B2, B3, B4, B5, and B6), as in Bitocchi et al. (2012) were identified with nuclear nucleotide data. The distribution of the accessions into the nucleotide data and cpSSR clusters is reported in Table 6. Figures 5A,B shows the geographical distribution of these clusters. The analysis of contingency tables indicated a significant association (P < 0.0001; likelihood ratio χ² test) between the genetic clusters obtained with these different markers (Figure 5C). In particular, cluster C1 was represented by clusters B3, B4, and B6, while cluster C2 included the B1, B2, and B5 clusters. In contrast, cluster C3 did not show any associations, although it is represented by accessions from the gene pools from Mesoamerica (B1, B2, B3), the Andes (B6), and northern Peru and Ecuador (B5).

Table 6.

Distribution of the 71 accessions shared between nucleotide and cpSSR data into the six nucleotide data clusters (B1, B2, B3, B4, B5, and B6) and the four cpSSR clusters (C1, C2, C3, C4) identified by the BAPS analysis.

Accession	cpSSR cluster			Nucleotide data cluster
	C1	C2	C3	B1	B2	B3	B4	B5	B6
MW	7	15	4	12	7	5	2	–	–
AW	40	–	1	–	–	–	–	–	41
PhI	–	3	1	–	–	–	–	4	–
Overall	47	18	6	12	7	5	2	4	41

MW, Mesoamerican wild; AW, Andean wild; PhI, northern Peru and Ecuador.

Figure 5.

Spatial interpolation of the membership coefficients (q) for the clusters identified by the population structure analysis using cpSSR (A) and for the nucleotide data (B), and results of the association test between these (C). q*, for cpSSRs, the geographical representation of the membership coefficients represents an approximation to easily compare the results obtained for the two different markers; indeed cpSSR q* values are represented by one or zero (i.e., membership or non-membership to one cluster), even if the spatial interpolation gives intermediate values. Only the 71 accessions shared between this study and that of Bitocchi et al. (2012) are included in this analysis. Latitude and longitude are expressed in the Universal Transverse Mercator system.

Discussion

The main aim of the present study was to investigate the complex evolutionary history that characterizes P. vulgaris through an analysis of its genetic diversity at the plastidial DNA level, in comparison with the study of Bitocchi et al. (2012) that was based on nuclear nucleotide data. Thus, taking into account the specific characteristics of the plastidial genome (haploidy, lack of recombination, uniparental inheritance), we used cpSSRs to contribute to the existing knowledge of the evolution of the common bean and its closely related species, and to provide new insights, especially considering that comparisons of data obtained through analyses of nuclear and cytoplasmic DNA can provide contrasting information on evolutionary processes (Birky, 1988; McCauley, 1995; Ennos et al., 1999; Provan et al., 1999; Weising and Gardner, 1999; Ishii et al., 2001; Lira et al., 2003; Ueno et al., 2005).

The data obtained here are in agreement with the Mesoamerican origin of P. vulgaris, thus confirming the findings of Bitocchi et al. (2012), where the nucleotide diversity at five nuclear gene fragments in a wide sample of wild P. vulgaris accessions was analyzed (mostly shared with the present study). Moreover, the absence of phaseolin type I in the Mesoamerican gene pool might be due to its extinction in Mesoamerica, or it might still be present, but just not included in the samples analyzed in the literature.

The first outcome was the reduction in the genetic diversity (26%) in the Andean gene pool, compared to that of Mesoamerica. This has already been shown, even if to different extents, by analyses carried out with different nuclear molecular markers (SSRs: 7%, Kwak and Gepts, 2009; AFLPs: 45%, Rossi et al., 2009) and sequence data (90%, Bitocchi et al., 2012). In particular, the loss of diversity detected with cpSSRs is intermediate between the SSRs and AFLPs, as is their mutation rate (10⁻³–10⁻⁵ mutations per generation; Provan et al., 1999; Marshall et al., 2002). Indeed SSRs are characterized by a very high mutation rate (10⁻³–10⁻⁴ mutations per generation; Estoup and Angers, 1998; Mariette et al., 2001; Udupa and Baum, 2001; Vigouroux et al., 2002; Thuillet et al., 2005; Garoia et al., 2007) and AFLPs by a lower one (10⁻⁶–10⁻⁵ mutations per generation; Mariette et al., 2001; Gaudeul et al., 2004; Kropf et al., 2009). Consistent with the evidence obtained for the nuclear genome (Kwak and Gepts, 2009; Rossi et al., 2009; Nanni et al., 2011; Bitocchi et al., 2012), our data provide further evidence of the bottleneck that occurred before domestication of the common bean in the Andes, which led to impoverishment of the genetic diversity also at the plastidial level in the present gene pool. Moreover, this confirms the strong relationship between the mutation rate and the time needed for a population to recover the genetic diversity that can be lost after a bottleneck: the higher the mutation rate, the shorter the time needed (Glémin and Bataillon, 2009; Rossi et al., 2009; Nanni et al., 2011; Bitocchi et al., 2012, 2013).

Moreover, the BAPS analysis allows the division into three main clusters for the P. vulgaris accessions (C1, C2, C3). The Andean accessions are almost all included in cluster C1, with the only exception being an accession from southern Peru that belongs to cluster C3. Considering the nuclear data, cluster C1 is significantly associated with clusters B3, B6, and B4. This supports the close relationship between the Andean (B6) and the MW accessions from central Mexico (B3; Bitocchi et al., 2012), which indicates that these MW accessions represent the most probable plant material that spread and adapted to the southern part of the Andes.

Cluster C2 is characterized by the Mesoamerican accessions assigned using nucleotide data to clusters B1 and B2, and three of the seven PhI accessions, while cluster C3 groups the accessions that are representative of all of the gene pools (Mesoamerican, Andean, and northern Peru and Ecuador). These data provide further confirmation of the evidence highlighted by the nuclear data (Bitocchi et al., 2012); indeed, the Mesoamerican population is highly subdivided also at the plastidial level, and all of the genetic groups identified are present in particular in Central Mexico, which indicates this geographical area as the center of origin of P. vulgaris.

However, an interesting and novel outcome is revealed by the cpSSRs, which is probably due to the different characteristics of the nuclear and plastidial genome (and in particular to the presence of recombination for the nuclear genome): the identification of cluster C3 as a genetic group that incorporates accessions that are representative of all of the gene pools (MW, AW, PhI) and are not significantly associated with any genetic cluster identified with the nuclear data. In particular, almost all of the MW in cluster C3 are from Central Mexico, with the only exception being one Colombian genotype; moreover, cluster C3 comprised four PhI accessions and one AW accession. The wide distribution in cluster C3 can be interpreted as evidence that these accessions carry the ancestral plastidial genome that spread over the entire distribution that is now covered by P. vulgaris. This pattern is also confirmed by the R_ST divergence estimations, where cluster C3 shows the lowest values compared to all of the other clusters, including most of the various alleles, when the size of the alleles is considered as a measure of the evolutionary distance among alleles. However, the same does not hold when the infinite allele model is considered: F_ST. Indeed, for F_ST, C2 shows the lowest divergence. This appears to be determined by the higher diversity (He) of C2 compared to C3, but not as alleles number (richness), with C2 showing the more uniform distribution of allele frequencies. Thus, we can speculate that the different results obtained for R_ST and F_ST might be the result of the more precise estimation of allele divergence using R_ST and because C3 has more skewed allele frequencies due to the drift (e.g., a bottleneck).

The membership of the two P. coccineus genotypes to cluster C2 suggests that this cluster can be considered as having been derived from an ancestral lineage from which P. vulgaris separated from P. coccineus. Alternatively, this might result from post speciation introgression from P. vulgaris (with P. vulgaris as the maternal parent of the initial hybridization). This putative introgression of plastidial DNA from P. vulgaris to P. coccineus is consistent with the hypothesis that the P. dumosus species originated from a cross of P. vulgaris as maternal and P. coccineus as paternal parent, followed by successive backcrosses from P. coccineus as paternal donor (Schmit et al., 1993; Llaca et al., 1994; Angioi et al., 2009a). Indeed, P. dumosus is closer to P. coccineus according to nuclear DNA comparisons (Piñero and Eguiarte, 1988; Delgado-Salinas et al., 1999), while according to chloroplast DNA comparisons it appears to be more closely related to P. vulgaris (Llaca et al., 1994; Angioi et al., 2009a). These outcomes reveal the complexity of the evolution of P. vulgaris within the evolutionary history of its closely related species, P. coccineus and P. dumosus (Schmit et al., 1993; Delgado-Salinas et al., 1999, 2006; Chacón et al., 2007), both of which are found in Mesoamerica (Schmit and Debouck, 1991; Freytag and Debouck, 2002). In spite of the marked differences in mating systems and life cycles, P. coccineus (predominantly allogamous and perennial), P. vulgaris (predominantly autogamous and annual), and P. dumosus (intermediate characteristics between P. coccineus and P. vulgaris) are partially intercrossable, although only when P. vulgaris is the female parent (Mendel, 1866; Wall, 1970; Shii et al., 1982; Hucl and Scoles, 1985). However, further studies should be carried out here, to compare a larger sample that includes genotypes from all three of these sister species and uses both nuclear and plastidial DNA analyses.

Conclusion

Chloroplast SSRs are widely used for evolutionary and phylogenetic studies as they have been demonstrated to be effective indicators of the genetic structure of a population. Therefore, we used this alternative form of analysis (with respect to nuclear data) with the aim of obtaining a more detailed picture of the history of the common bean. These cpSSR data strongly support the nuclear data of Bitocchi et al. (2012), that indicated a clear Mesoamerican origin of this species, and in particular, they support Central Mexico as, with high probability, the cradle of common bean diversity.

A novel outcome was also provided by these analyses based on the polymorphism at the chloroplast DNA level: the identification of a genetic group (cluster C3) that includes accessions distributed from northern Mexico to Peru that appear to carry a putative ancestral plastidial genome.

Finally, the present study highlights the potential to evaluate the evolutionary history of P. vulgaris within the evolution of the whole species complex that includes P. vulgaris, P. coccineus, and P. dumosus. A deeper study of the formation and evolution of these closely related and intercrossable species will be intriguing from an evolutionary point of view. At the same time, such data should be particularly relevant for common bean breeding programs, as demonstrated by the increasing interest in the development of interspecific lines (P. vulgaris-P. coccineus and P. vulgaris-P. dumosus crosses) for the introgression of important traits; e.g., resistance to biotic and abiotic stress in P. vulgaris elite germplasm (Singh et al., 2009; Klaedtke et al., 2012).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix

Table A1.

List of accessions used in this study.

Accession code1	Synonyms	Species	Donor2	Pop code3	Country	Department, state, or province	Latitude	Longitude	BAPS cluster (cpSSR)	BAPS cluster (nucleotide data); q ≥ 0.64
G21113	LEROI COL-14, NI-922	P. vulgaris	CIAT	MW	Colombia	Cundinamarca	44,833	−73,933	C2	/
G22304	LEROI COL-13, NI-1142	P. vulgaris	CIAT	MW	Colombia	Cundinamarca	44,833	−73,933	C3	/
G21115•	LEROI COL-23, NI-926	P. vulgaris	CIAT	MW	Colombia	Cundinamarca	45,333	−73,917	C2	B1
G21117•	LEROI COL-28, NI-937	P. vulgaris	CIAT	MW	Colombia	Cundinamarca	46,667	−74.4	C2	B1
G22303•	LEROI COL-22, NI-1141	P. vulgaris	CIAT	MW	Colombia	Cundinamarca	45,333	−73,917	C1	B1
G23462•	LEROI COL-15, NI-1256, X-636	P. vulgaris	CIAT	MW	Colombia	Cundinamarca	50,833	−73,617	C2	B1
G2771	GENTRY 22274; PI318702	P. vulgaris	CIAT	MW	Mexico	Nayarit	211,667	−104.37	C3	/
G11051	DGD-451	P. vulgaris	CIAT	MW	Mexico	Jalisco	207,667	−103.4	C3	/
G12927	M7278-G, PI417689	P. vulgaris	CIAT	MW	Mexico	Jalisco	20.7	−102.35	C3	/
G12957	M7424-C, PI417786	P. vulgaris	CIAT	MW	Mexico	Jalisco	20.9	−102.37	C1	/
G23418	DGD-2111	P. vulgaris	CIAT	MW	Costa Rica	San Jose	98,667	−84,117	C2	/
G23558	OAXACA 112	P. vulgaris	CIAT	MW	Mexico	Oaxaca	163,333	−95,233	C2	/
G24366	JSG & LOS-150	P. vulgaris	CIAT	MW	Mexico	Jalisco	204,833	−103.4	C1	/
PI417775	G12949, M7408-P	P. vulgaris	USDA	MW	Mexico	Jalisco	20.64	−102.41	C3	/
W612107	CR-93-004	P. vulgaris	USDA	MW	Costa Rica	Puntarenas	8.95	−83,038	C2	/
G9989•	HM7395-BULK	P. vulgaris	CIAT	MW	Mexico	Jalisco	20.5	−104.82	C3	B1
G19906•	DGD-1610	P. vulgaris	CIAT	MW	Guatemala	Sacatepequez	14.45	−90.7	C2	B1
G19907•	DGD-1611	P. vulgaris	CIAT	MW	Guatemala	Sacatepequez	14.45	−90,817	C2	B1
G19909•	DGD-1619	P. vulgaris	CIAT	MW	Guatemala	Sacatepequez	14.55	−90,833	C2	B1
G22837•	GN 84127/BB 8480, P16-001	P. vulgaris	CIAT	MW	Mexico	Chihuahua	269,333	−106.42	C2	B1
G23463•	GN 84154, L 625	P. vulgaris	CIAT	MW	Mexico	Chihuahua	283,333	−108.5	C2	B1
G24378•	JSG & LOS-199	P. vulgaris	CIAT	MW	Mexico	Oaxaca	16.4	−97,083	C2	B1
G50899•	LEROI MEX-26, NI-1144	P. vulgaris	CIAT	MW	Mexico	Durango	237,833	−105.37	C2	B1
G11056•	DGD-490	P. vulgaris	CIAT	MW	Mexico	Jalisco	205,667	−104.77	C3	B2
G20515•	M8137B-1	P. vulgaris	CIAT	MW	Mexico	Puebla	19.8	−97,783	C2	B2
G23429•	DGD-2325	P. vulgaris	CIAT	MW	Mexico	Puebla	189,667	−98,383	C2	B2
G24571•	JSMM-4002	P. vulgaris	CIAT	MW	Mexico	Oaxaca	171,667	−97,983	C2	B2
G24572•	JSMM-4006	P. vulgaris	CIAT	MW	Mexico	Oaxaca	159,833	−96,517	C3	B2
G24599•	JAG-180	P. vulgaris	CIAT	MW	Mexico	Chiapas	164,833	−92,517	C2	B2
G50415•	JAG-209	P. vulgaris	CIAT	MW	Mexico	Hidalgo	20.85	−98,717	C2	B2
G11050•	DGD-439	P. vulgaris	CIAT	MW	Mexico	Michoacan	196,833	−101.27	C1	B4
G12922•	M7278-A, PI417683	P. vulgaris	CIAT	MW	Mexico	Jalisco	20.7	−102.35	C3	B3
G12979•	M7439T	P. vulgaris	CIAT	MW	Mexico	Jalisco	201,167	−104.37	C1	B3
G23415A•	DGD-2077	P. vulgaris	CIAT	MW	Mexico	Queretaro	211,333	−99,617	C1	B3
G23652•	M2058	P. vulgaris	CIAT	MW	Mexico	Puebla	19.8	−97,783	C1	B3
G12865•	GENTRY 22199, PI318696	P. vulgaris	CIAT	MW	Mexico	Jalisco	193,333	−103.25	C1	B3
G12873•	PI325678, GENTRY22492	P. vulgaris	CIAT	MW	Mexico	Morelos	19	−99.25	C1	B4
G10012	MORELOS 646, V-1434	P. vulgaris	CIAT	MW	Mexico	Morelos	188,833	−99.15	C2	/
G12872	GENTRY 22404, PI325677	P. vulgaris	CIAT	MW	Mexico	Morelos	189,667	−99.1	C1	/
G12877	GENTRY 22530, PI325683	P. vulgaris	CIAT	MW	Mexico	Morelos	18.95	−99,217	C3	/
G12896	M7230, PI417629	P. vulgaris	CIAT	MW	Mexico	Michoacan	201,333	−102.08	C3	/
G12924	M7278-C, PI417685	P. vulgaris	CIAT	MW	Mexico	Jalisco	20.7	−102.35	C3	/
G12930	M7278-L, PI417692	P. vulgaris	CIAT	MW	Mexico	Jalisco	20.7	−102.35	C3	/
G13018	MORELOS 654, V-1438	P. vulgaris	CIAT	MW	Mexico	Morelos	188,833	−99.15	C1	/
G13505	MORELOS 635, NI-404	P. vulgaris	CIAT	MW	Mexico	Morelos	188,833	−99.15	C2	/
G12866	GENTRY 22202, PI318697	P. vulgaris	USDA	MW	Mexico	Jalisco	19,683	−103.48	C1	/
CHWENN2		P. vulgaris	UNIVPM	MW	Mexico	Chiapas	164,833	−92,517	C3	/
CHWETE16		P. vulgaris	UNIVPM	MW	Mexico	Chiapas	164,833	−92,517	C3	/
DGW15		P. vulgaris	UNIVPM	MW	Mexico	Durango	237,833	−105.37	C3	/
111d		P. vulgaris	UNIVPM	MW	Mexico	Chiapas	164,833	−92,517	C3	/
JAL97		P. vulgaris	UNIVPM	MW	Mexico	Jalisco	204,833	−103.4	C3	/
MOW5		P. vulgaris	UNIVPM	MW	Mexico	Morelos	189,667	−99.1	C3	/
MXW17		P. vulgaris	UNIVPM	MW	Mexico	–	***	***	C3	/
PUW21		P. vulgaris	UNIVPM	MW	Mexico	Puebla	***	***	C3	/
G23415	DGD-2077	P. vulgaris	CIAT	MW	Mexico	Queretaro	211,333	−99,617	C1	/
G23423C	DGD-2157	P. vulgaris	CIAT	AW	Perù	Apurimac	−13.85	−72,967	C1	/
W617481	PI638874	P. vulgaris	USDA	AW	Argentina	Jujuy	−22,267	−64,683	C1	/
W617500	PI640966	P. vulgaris	USDA	AW	Argentina	Salta	−24.65	−65,367	C1	/
W617501	PI640967	P. vulgaris	USDA	AW	Argentina	Salta	−24.65	−65,367	C1	/
G7225	APURIMAC 76	P. vulgaris	CIAT	AW	Perù	Apurimac	−13,667	−72,883	C1	/
W617467	PI638865	P. vulgaris	USDA	AW	Argentina	Tucuman	−26,217	−65,527	C1	/
G7469•	NI-029	P. vulgaris	CIAT	AW	Argentina	***	***	***	C1	B6
G10024•	NI-190	P. vulgaris	CIAT	AW	Argentina	***	***	***	C1	B6
G12856•	PI260405, SMITH PV-1	P. vulgaris	CIAT	AW	Perù	Huanuco	−10,333	−76,183	C3	B6
G19888•	DGD-623	P. vulgaris	CIAT	AW	Argentina	Jujuy	−24,167	−65.6	C1	B6
G19889•	DGD-624	P. vulgaris	CIAT	AW	Argentina	Jujuy	−24.25	−65,283	C1	B6
G19891•	DGD-628	P. vulgaris	CIAT	AW	Argentina	Salta	−25,117	−65,617	C1	B6
G19892•	DGD-629	P. vulgaris	CIAT	AW	Argentina	Salta	−25.15	−65.65	C1	B6
G19893•	DGD-630, NEEMA S-211/S-226	P. vulgaris	CIAT	AW	Argentina	Salta	−24,633	−65,483	C1	B6
G19895•	DGD-637, NEEMA T-711/T-717	P. vulgaris	CIAT	AW	Argentina	Tucuman	−26,433	−65,517	C1	B6
G19896•	DGD-639	P. vulgaris	CIAT	AW	Argentina	Tucuman	−26,217	−65,583	C1	B6
G19897•	DGD-643, NEEMA T-911/T-917	P. vulgaris	CIAT	AW	Argentina	Tucuman	−27,317	−65,917	C1	B6
G19898•	DGD-644	P. vulgaris	CIAT	AW	Argentina	Tucuman	−27,333	−65.95	C1	B6
G19901•	DGD-649	P. vulgaris	CIAT	AW	Argentina	Tucuman	−26,933	−65.7	C1	B6
G21194•	DGD-621	P. vulgaris	CIAT	AW	Argentina	Jujuy	−24,117	−65,417	C1	B6
G21197•	DGD-1711	P. vulgaris	CIAT	AW	Argentina	Jujuy	−24.05	−65.45	C1	B6
G21198•	DGD-1712	P. vulgaris	CIAT	AW	Argentina	Jujuy	−24,067	−65,367	C1	B6
G21199•	DGD-1713	P. vulgaris	CIAT	AW	Argentina	Jujuy	−23,917	−65.35	C1	B6
G21201•	DGD-1716	P. vulgaris	CIAT	AW	Argentina	Salta	−22.25	−65	C1	B6
G23420•	DGD-2147	P. vulgaris	CIAT	AW	Perù	Junin	−11.2	−75,483	C1	B6
G23421•	DGD-2152	P. vulgaris	CIAT	AW	Perù	Junin	−12,017	−74,883	C1	B6
G23422•	DGD-2156	P. vulgaris	CIAT	AW	Perù	Apurimac	−14	−73,167	C1	B6
G23426•	DGD-2295	P. vulgaris	CIAT	AW	Perù	Apurimac	−13,617	−73.2	C1	B6
G23444•	DGD-2497	P. vulgaris	CIAT	AW	Bolivia	Chuquisaca	−19.3	−64,317	C1	B6
G23445•	DGD-2501	P. vulgaris	CIAT	AW	Bolivia	Tarija	−21,533	−64,867	C1	B6
G23455•	DGD-2581	P. vulgaris	CIAT	AW	Perù	Cuzco	−13.5	−72,483	C1	B6
W617466•	PI638864	P. vulgaris	USDA	AW	Argentina	Tucuman	−26,233	−65,483	C1	B6
W617468•	PI638866	P. vulgaris	USDA	AW	Argentina	Tucuman	−27,817	−65,783	C1	B6
W617469•	PI638867	P. vulgaris	USDA	AW	Argentina	Tucuman	−27,797	−65,785	C1	B6
W617470•	PI640964	P. vulgaris	USDA	AW	Argentina	Tucuman	−26,383	−65,467	C1	B6
W617471•	PI638868	P. vulgaris	USDA	AW	Argentina	Tucuman	−26,383	−65,533	C1	B6
W617472•	PI638869	P. vulgaris	USDA	AW	Argentina	Tucuman	−26.95	−65.7	C1	B6
W617473•	PI638870	P. vulgaris	USDA	AW	Argentina	Salta	−26.1	−65.6	C1	B6
W617474•	PI640965	P. vulgaris	USDA	AW	Argentina	Salta	−25,161	−65,611	C1	B6
W617475•	PI638871	P. vulgaris	USDA	AW	Argentina	Salta	−25,167	−65,617	C1	B6
W617476•	PI638872	P. vulgaris	USDA	AW	Argentina	Salta	−25,166	−65,649	C1	B6
W617478•	PI638873	P. vulgaris	USDA	AW	Argentina	Salta	−24,896	−65,801	C1	B6
W617486•	PI638875	P. vulgaris	USDA	AW	Argentina	Jujuy	−22,267	−64,683	C1	B6
W617499•	PI661807	P. vulgaris	USDA	AW	Argentina	Salta	−24.9	−65,483	C1	B6
W617502•	PI640968	P. vulgaris	USDA	AW	Argentina	Salta	−24,717	−65,483	C1	B6
W618821•	PI638897, DGD3038	P. vulgaris	USDA	AW	Bolivia	Chuquisaca	−19,283	−64,333	C1	B6
W618826•	PI638898, DGD3044	P. vulgaris	USDA	AW	Bolivia	Chuquisaca	−19,283	−64,333	C1	B6
G23581	DGD-2765	P. vulgaris	CIAT	PhI	Ecuador	Azuay	−3.2	−79,183	C3	/
G23582	DGD-2769	P. vulgaris	CIAT	PhI	Ecuador	Chimborazo	−22,667	−78,967	C3	/
G23724	DGD-2881, PI557544, W6 8245	P. vulgaris	CIAT	PhI	Ecuador	Loja	−43,167	−79,933	C3	/
G21245•	DGD-1962	P. vulgaris	CIAT	PhI	Perù	Cajamarca	−71,167	−78,783	C2	B5
G23585•	DGD-2855	P. vulgaris	CIAT	PhI	Perù	Cajamarca	−6.35	−79.4	C2	B5
G23587•	DGD-2858	P. vulgaris	CIAT	PhI	Perù	Cajamarca	−6.35	−79.4	C3	B5
G23726•	DGD-2889	P. vulgaris	CIAT	PhI	Ecuador	Chimborazo	−19,667	−78.95	C2	B5
PI535280	TARS212, 78-G-4	P. coccineus	USDA	–	Guatemala	Sacatepequez	14.43	−90.95	C2	/
PI535287	TARS222, 78-G-15	P. coccineus	USDA	–	Guatemala	Sacatepequez	14.67	−90.75	C2	/
PI325584	ACAHUATE	P. coccineus	USDA	–	Mexico	Puebla	19,816	−978,166	C4	/
PI417608	M7417-G	P. coccineus	USDA	–	Mexico	Jalisco	20,866	−102,367	C4	/
PI417611	M7423-A	P. coccineus	USDA	–	Mexico	Jalisco	20,866	−102,366	C4	/
PI417592	M7399-V	P. coccineus	USDA	–	Mexico	Jalisco	25.56	−106.37	C4	/
PI430191	M7402-U	P. coccineus	USDA	–	Mexico	Chihuahua	28.6	−107,167	C4	/
PI430192	M7402-V	P. coccineus	USDA	–	Mexico	Chihuahua	28.6	−107,167	C4	/
CX 03		P. coccineus	UNIVPM	–	Mexico	Morelos	***	***	C4	/
CF19		P. coccineus	UNIVPM	–	Mexico	Morelos	***	***	C4	/

¹Population code: WM, wild Mesoamerican; WA, wild Andean; PhI, Phaseolin I type.

²•(dot) indicates the P. vulgaris accessions shared with the study of Bitocchi et al. (2012), showing high-quality sequences for all of the five Leg markers analyzed in Bitocchi et al. (2012); these accessions were used to compare the population structure results obtained using both cpSSR and nucleotide data.

³CIAT, International Centre for Tropical Agriculture; USDA, United States Department of Agriculture; UNIVPM, Università Politecnica delle Marche.

⁴q, percentage of membership to one of the six clusters identified using nucleotide data; a q threshold value of 0.6 was considered to assign accessions to clusters.

Table A2.

List of SSR used in this study.

Locus	Primer sequence 5′–3′		PCR conditionsa	Reference
ccSSR2	fw-AATCCTGGACGTGAAGAATAA	rev-AATCCCTCTCTTTCCGTTGA	1	Chung and Staub (2003)
ccSSR4	fw-AGGTTCAAATCCTATTGGACGCA	rev-TTTTGAAAGAAGCTATTCARGAAC	1	Chung and Staub (2003)
ccSSR7	fw-CGGGAAGGGCTCGKGCAG	rev-GTTCGAATCCCTCTCTCTCCTTTT	1	Chung and Staub (2003)
ccSSR8	fw-TTGATCTTTACGGTGCTTCCTCTA	rev-TCATTACGTGCGACTATCTCC	1	Chung and Staub (2003)
ccSSR9	fw-GAGGATACACGACAGARGGARTTG	rev-CCTATTACAGAGATGGTGYGATTT	1	Chung and Staub (2003)
ccSSR11	fw-TTGGCTACTCTAACCTTCCC	rev-ACCATAGAAACGAWGGAACCCACT	2	Chung and Staub (2003)
ccSSR12	fw-CCAAAAACTTGGAGATCCAACTAC	rev-TTCCATAGATTCGATCGTGGTTTA	1	Chung and Staub (2003)
ccSSR15	fw-GCTTATGACCTCCCCCTCTATGC	rev-TGCATTACAGACGTATGATCATTA	1	Chung and Staub (2003)
ccSSR16	fw-TACGAGATCACCCCTTTCATTC	rev-CCTGGCCCAACCCTAGACA	1	Chung and Staub (2003)
ccSSR18	fw-TCGTTGGATTTCTTCDGGACATTT	rev-CCCAATATCATCATACTTACRTGC	1	Chung and Staub (2003)
ccSSR19	fw-CTATGCAGCTCTTTTATGYGGATC	rev-TCCARGTAATAAATGCCCAAGTT	1	Chung and Staub (2003)
ccSSR20	fw-CCGCARATATTGGAAAAACWACAA	rev-GCTAARCAAATWGCTTCTGCTCC	1	Chung and Staub (2003)
ccmp2	fw-GATCCCGGACGTAATCCTG	rev-ATCGTACCGAGGGTTCGAAT	1	Weising and Gardner (1999)
ccmp3	fw-CAGACCAAAAGCTGACATAG	rev-GTTTCATTCGGCTCCTTTAT	3	Weising and Gardner (1999)
cp1	fw-CAAAATCAAAAGAGCGATTAGG	rev-GTCAAACCCATGAACGGACT	1	Angioi et al. (2009a)
cp2	fw-TCTGTTTTGACCATATCGCACT	rev-GTCCATAAATAGATTCCCGAAAAA	4	Angioi et al. (2009a)
cp3	fw-TCGGTGTAAATTGATAAAACGAAA	rev-TGCCTAGCAAAAGACTCTAAGAAAG	4	Angioi et al. (2009a)

^aPCR conditions: 1: 5 min at 94°C; 35 cycles of 1′ at 94°C 1′ at 50°C 1′ at 72°C; 35′ at 72°C; 2: 5′ at 94°C; touch down cycles 53–45°C with −1°C/2 cycles, 1′ at 72°C;20 cycles of 1′ at 94°C, 1′ at 45°C 1′ at 72°C; 35′ at 72°C; 3: 5′ at 94°C; touch down cycles 53–43°C with −1°C/2 cycles, 1′ at 72°C; 20 cycles of 1′ at 94°C 1′ at 43°C. 1′ at 72°C; 35′ at 72°C; 4: 5′ at 94°C; 30 cycles of 30 s at 94°C 30 s at 48°C 30 s at 72°C; 35′ at 72°C.

Table A3.

Number of alleles (Na) and gene diversity (He, Nei, 1978) in the overall, P. vulgaris and P. coccineus samples for each of the 17 cpSSRs used.

Locus	cp1	cp2	cp3	ccmp2	ccmp3	ccSSR2	ccSSR4	ccSSR7	ccSSR8	ccSSR9	ccSSR11	ccSSR12	ccSSR15	ccSSR16	ccSSR18	ccSSR19	ccSSR20
OVERALL
He	0.48	0.13	0.15	0.68	0.52	0.66	0.63	0.71	0.65	0.61	0.79	0.51	0.60	0.33	0.46	0.44	0.85
Na	4	2	6	4	4	4	6	7	6	4	9	3	3	3	6	3	12
Allelic range (bp)	111–114	180–183	154–171	196–199	79–94	167–170	244–249	299–308	224–265	133–136	164–183	203–206	262–264	353–355	260–266	376–378	312–324
P. vulgaris
He	0.43	0.00	0.02	0.67	0.48	0.65	0.63	0.66	0.64	0.60	0.76	0.44	0.61	0.30	0.49	0.46	0.84
Na	4	1	2	4	3	4	5	6	6	3	8	2	3	3	6	3	11
Allelic range (bp)	111–114	180	170–171	196–199	83–94	167–170	245–249	299–308	224–265	133–135	164–174	205–206	262–264	353–355	260–266	376–378	314–324
P. coccineus
He	0.00	0.36	0.82	0.00	0.64	0.36	0.36	0.64	0.00	0.64	0.64	0.36	0.00	0.53	0.00	0.00	0.84
Na	1	2	5	1	3	2	2	4	1	4	3	2	1	2	1	1	6
Alleles (bp)	112	180–183	154–170	196	79–84	167–168	244–245	303–307	260	133–136	164–183	203–206	263	353–354	263	376	312–320