Online supplementary figures
Title
Taxonomic, spatial and adaptive genetic variation of Beta section Beta and Beta vulgaris subsp.
maritima
Journal name
Theoretical and Applied Genetics
Author names
Marco ANDRELLO1, Karine HENRY2, Pierre DEVAUX2, Bruno DESPREZ2 and Stéphanie MANEL1
Affiliations
1
CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier – EPHE,
laboratoire Biogéographie et écologie des vertébrés, 1919 route de Mende, F-34293 Montpellier
Cedex 5, France
2
Florimond Desprez, F-59242 Cappelle En Pévèle, France
Corresponding author
Marco ANDRELLO. Tel: +33 4 67 61 33 31. Email: marco.andrello@gmail.com
Fig. S1 Genetic distinctiveness for the 923 well-classified B. vulgaris subsp. maritima accessions.
Colours synthesize the coordinates of accessions on the first SPCA principal component. The panels
show the results for different connections graph
Fig. S2 Correlations among the 19 initial bioclimatic variables, longitude and latitude of the 1264
sampling locations
7700
7400
7500
7600
BIC
7800
7900
Value of BIC
versus number of clusters
0
20
40
60
80
100
Number of clusters
Fig. S3 K-means method to identify the number of clusters on the 1264 accessions of Beta section
Beta. BIC: Bayesian Information Criterion.
Fig. S4 Spatial distribution of the 1264 accessions of Beta section Beta separately for each cluster of
the DAPC
-2,000,000
-2,200,000
Log-Likelihood L(K)
-2,400,000
-2,600,000
-2,800,000
-3,000,000
-3,200,000
-3,400,000
-3,600,000
-3,800,000
-4,000,000
0
1
2
3
4
5
6
7
8
9
10
Number of clusters K
4.5
4.0
3.5
L''(K)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
1
2
3
4
5
6
7
8
9
10
Number of clusters K
Fig. S5 Inference of the number of clusters, K, of the 1264 accessions of Beta section Beta with
STRUCTURE. Left: log-Likelihood as a function of the number of clusters K. Points and error bars
denote the mean ± two standard deviations across 10 replicates. Right: the L’’(K) statistic of Evanno
et al. (2005)
Fig. S6 K-means method to identify the number of clusters on the 923 well-classified accessions of B.
vulgaris subsp. maritima. BIC: Bayesian Information Criterion.
-1,000,000.0
Log-Likelihood L(K)
-1,500,000.0
-2,000,000.0
-2,500,000.0
-3,000,000.0
-3,500,000.0
-4,000,000.0
0
1
2
3
4
5
6
7
8
9 10
Number of clusters K
10.0
9.0
8.0
7.0
L''(K)
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0
1
2
3
4
5
6
7
8
9
10
Number of clusters K
Fig. S7 Inference of the number of clusters, K, of the 923 well-classified accessions of B. vulgaris
subsp. maritima with STRUCTURE. Top: log-Likelihood as a function of the number of clusters K.
Points and error bars denote the mean ± two standard deviations across 10 replicates. Bottom: the
L’’(K) statistic of Evanno et al. (2005)
Fig. S8 Genetic structure of the 923 well-classified accessions of B. vulgaris subsp. maritima with
STRUCTURE with K = 2 clusters. The colour scale indicates the membership probability to each cluster
Fig. S9 Spatial analysis of principal components (SPCA) of the 923 well-classified accessions of B.
vulgaris subsp. maritima. The eigenvalues are plotted in a plane defined by their variance and spatial
autocorrelation (Moran's I) components. The dashed vertical line is the maximum attainable
variance; the two horizontal dashed lines indicate the range of variation of Moran’s I
Fig. S10 Empirical cumulative distribution functions (ECDF) of p-values for LFMM run with K = 4 latent
factors on the 1264 accessions of Beta section Beta. « lambda » is the genomic inflation factor
Fig. S11 Empirical cumulative distribution functions (ECDF) of p-values for LFMM run with K = 8 latent
factors on the 1264 accessions of Beta section Beta. « lambda » is the genomic inflation factor
Fig. S12 Variation among replicates of LFMM on the 1264 accessions of Beta section Beta. The bars represent the z-scores of each of five replicates for a
random subset of 20 markers in the analysis of annual mean temperature. LFMM was run with K = 8 latent factors. The numbers on the x-axis are the
marker identifiers
Fig. S13 Ranks of individual markers in the correlations between environmental variables on the 1264
accessions of Beta section Beta for gINLAnd and LFMM with K = 4 latent factors. The significant
markers are coloured in yellow (gINLAnd), blue (LFMM) or green (significant with both methods)
Fig. S14 Ranks of individual markers in the correlations between environmental variables on the 1264
accessions of Beta section Beta for gINLAnd and LFMM with K = 8 latent factors. The significant
markers are coloured in yellow (gINLAnd), blue (LFMM) or green (significant with both methods)
Fig. S15 Empirical cumulative distribution functions (ECDF) of p-values for LFMM run with K = 2 latent
factors on the 923 well-classified accessions of B. vulgaris subsp. maritima. « lambda » is the
genomic inflation factor.
Fig. S16 Empirical cumulative distribution functions (ECDF) of p-values for LFMM run with K = 6 latent
factors on the 923 well-classified accessions of B. vulgaris subsp. maritima. « lambda » is the
genomic inflation factor.
Fig. S17 Variation among replicates of LFMM on the 923 well-classified accessions of B. vulgaris subsp. maritima. The bars represent the z-scores of each of
five replicates for a random subset of 20 markers in the analysis of annual mean temperature. LFMM was run with K = 6 latent factors. The numbers on the
x-axis are the marker identifiers
Fig. S18 Ranks of individual markers in the correlations between environmental variables on the 923
well-classified accessions of B. vulgaris subsp. maritima for gINLAnd and LFMM with K = 2 latent
factors. The significant markers are coloured in yellow (gINLAnd), blue (LFMM) or green (significant
with both methods)
Fig. S19 Ranks of individual markers in the correlations between environmental variables on the 923
well-classified accessions of B. vulgaris subsp. maritima for gINLAnd and LFMM with K = 6 latent
factors. The significant markers are coloured in yellow (gINLAnd), blue (LFMM) or green (significant
with both methods)