Genetic diversity and structure of Prunus padus populations in South Korea based on AFLP markers

Figures & data

Figure 1. Location of eleven Prunus padus populations in South Korea for this study.

Table 1. Number of AFLP amplified bands and polymorphic bands per primer-restriction enzyme combinations from Prunus padus populations.

Primer-restriction enzyme combinations	Total number of amplified bands	Number of polymorphic bands
E-AAG + M-CTG^a	34	24
E-ACA + M-CAC	43	35
E-ACA + M-CTA	31	21
E-ACG + M-CAT	29	22
E-ACT + M-CAA	35	29
E-ACT + M-CTA	27	22
E-AGC + M-CAA	39	31
Total	238	184

^aRestriction enzyme E- and M- are EcoRI and MseI, respectively.

Table 2. Genetic diversity parameters estimated from Prunus padus populations.

Population	N^a	Ae	%P	I	He	Hj	FIS
Mt. Baekhwa	20	1.36 (0.03)^b	73.4	0.333 (0.019)	0.217 (0.014)	0.254 (0.013)	0.780
Mt. Cheonggye	32	1.27 (0.02)	69.6	0.268 (0.018)	0.169 (0.013)	0.189 (0.013)	0.828
Mt. Chiak	30	1.47 (0.03)	87.5	0.416 (0.018)	0.275 (0.013)	0.303 (0.012)	0.757
Mt. Hwaak	37	1.37 (0.03)	85.9	0.347 (0.018)	0.223 (0.013)	0.240 (0.013)	0.885
Mt. Hwa	34	1.41 (0.03)	78.8	0.372 (0.019)	0.245 (0.014)	0.276 (0.013)	0.548
Mt. Odae	20	1.32 (0.03)	78.3	0.314 (0.018)	0.199 (0.013)	0.221 (0.013)	0.887
Mt. Seorak	33	1.53 (0.03)	92.9	0.462 (0.016)	0.308 (0.012)	0.333 (0.011)	0.305
Mt. Sobaek	34	1.46 (0.03)	92.4	0.419 (0.017)	0.273 (0.013)	0.290 (0.013)	0.848
Mt. Taebaek	32	1.39 (0.03)	87.5	0.375 (0.017)	0.241 (0.013)	0.263 (0.012)	0.873
Mt. Yongmun	23	1.34 (0.03)	77.7	0.319 (0.019)	0.205 (0.014)	0.229 (0.013)	0.867
Mt. Duta	30	1.32 (0.03)	71.7	0.304 (0.019)	0.195 (0.013)	0.217 (0.013)	0.857
Mean		1.38 (0.01)	81.4	0.357 (0.006)	0.223 (0.004)	0.256 (0.013)	0.767

^aN: sample size; A_e: number of effective alleles; %P: percentage of polymorphic loci; I: Shannon’s diversity index; H_e: expected heterozygosity; Hj: expected heterozygosity by Bayesian method with non-uniform prior distribution of allele frequency (Zhivotovsky, 1999); F_IS: inbreeding coefficient estimated by an approximate Bayesian computation.

^bNumbers in parentheses are standard deviations.

Table 3. Distribution of genetic variations of Prunus padus populations from analysis of molecular variance (AMOVA).

Source of variation	d.f.	Sum of squares	Variance components	Percentage of variance	ΦST
Among populations	10	2817.461	8.664	25%	0.245**
Within populations	314	8374.917	26.672	75%
Total	324	11192.378	35.336	100.0%

**p < 0.01.

Table 4. Inbreeding coefficient (f) and population differentiation (θ^II) of Prunus padus using different models based on Bayesian method.

Model	f				θ^II				DIC^b
	Mean	SD^a	2.5%	97.5%	Mean	SD	2.5%	97.5%
Full	0.963	0.036	0.586	0.995	0.278	0.007	0.263	0.294	8905.1
f = 0	–	–	–	–	0.203	0.007	0.190	0.216	8963.8
θ = 0	0.976	0.024	0.910	0.999	–	–	–	–	22288.1
f=free	0.500	0.289	0.025	0.976	0.265	0.015	0.234	0.292	9800.7

^aSD: standard deviation.

^bDIC: deviance information criterion.

Figure 2. UPGMA dendrogram based on Nei's genetic distance of each Pruns padus population.

Figure 3. Principal components analysis (PCA) for Prunus padus populations. Axis 1 and axis 2 were explained 38.22 and 20.28% of genetic variation, respectively.

Figure 4. Results of Bayesian clustering analysis. (A) The mean log-likelihood value (±standard deviation) of the data was ten repetitions for each K value. (B) The value of delta K (ΔK) was changed as each K value. (C) Assignment probabilities at the individual and population levels estimated using Bayesian clustering. Each individual is shown as a vertical line divided into segments representing the estimated membership proportion.

Figure 5. Geographic distribution pattern of two clusters estimated by using Bayesian clustering.

Zhivotovsky LA. 1999. Estimating population structure in diploids with multilocus dominant DNA markers. Mol Ecol. 8(6):907–913.

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