Plant Syst Evol (2014) 300:475–482 DOI 10.1007/s00606-013-0896-5

ORIGINAL ARTICLE

Analysis of genetic diversity in mira Koehne ex Sargent populations using AFLP markers

Tengfeng Li • Jiaren Liu • Yanan Xie • Qiuyu Wang • Fanjuan Meng

Received: 13 June 2013 / Accepted: 7 August 2013 / Published online: 25 August 2013 Ó Springer-Verlag Wien 2013

Abstract Koehne ex Sargent (syn. Persica plateau about 2000 years ago. For its high tolerance to mira (Koehne) Kov. et Kostina), native to China, is an drought, cold and barren soil, it can be utilized for soil excellent fruit tree due to its high ecological and eco- erosion control, vegetation restoration, rootstocks, and as nomical value. However, there is limited knowledge on the useful genes pool in cultivated breeding (Wang et al. genetic information of P. mira. In this study, the genetic 1997; Fang et al. 2008; Hao et al. 2009). In addition, P. relationships of 83 P. mira accessions from five popula- mira fruits are traditionally used not only as delicious food, tions were assessed using amplified fragment length poly- but also as remedy for irregular menses, fracture and morphism (AFLP). The results showed that AFLP was a congestion (Dong 1991). It is usually used as an orna- powerful tool to detect levels of genetic diversity of natural mental with pink flowers (Zhong 2008). Therefore, P. populations in P. mira. The similarity coefficient between mira is an excellent fruit tree due to its high ecological and accessions ranged from 0.12 to 0.76, with an average 0.57. economical value. 83 accessions were clustered into two major clusters at In recent years, due to climate change as well as human similarity coefficient of 0.225. The highest values of Ne, interference of their natural habitats, P. mira species are H and I occurred in ML population. Most of the genetic now considered to be threatened (Fu 2002; Zhong 2008). variations occur within population. There is no close Thus, it is urgent to conserve this plant germplasm. To relationship between geographic distance and genetic dis- date, considerable efforts have focused on its ecological tance. At the same time, ex situ conservation needs to be (Fang et al. 2008), morphological (Geng et al. 2008) and established for P. mira. physiological (Wang et al. 1997; Hao et al. 2009) charac- teristics. However, knowledge is still too limited to Keywords Prunus mira Koehne Genetic diversity understand the genetic diversity and population genetic Population AFLP marker structure of P. mira. To evaluate the genetic diversity of plant species, the reliable, fast and cheap molecular marker techniques Introduction should be developed. Amplified fragment length poly- morphism (AFLP) is a high efficiency molecular marker Prunus mira Koehne ex Sargent (syn. Persica mira (Koe- for the identification of genetic diversity of . Com- hne) Kov. et Kostina), native to China, belongs to a deli- pared to other molecular markers, AFLP have many cious and wild fruit tree, called by ‘Guanghetao’ due to advantages such as reproducibility, higher resolution, sen- smooth fruit-stone (Zhong et al. 2010). It is widely dis- sitivity and no prior sequence information (Mueller and tributed at altitudes from 2,500 to 3,500 m in the Tibetan Wolfenbarger 1999; Meudt and Clarke 2007). Therefore, AFLP has been successfully used to elucidate genetic diversity in many plant species (Russell et al. 1997; Cao & T. Li J. Liu Y. Xie Q. Wang F. Meng ( ) et al. 2006; Christensen et al. 2011). In addition, previous College of Life Science, Northeast Forestry University, Harbin 150040, China studies showed that AFLP technique was an efficient sys- e-mail: [email protected] tem to assess the genetic diversity of cultivated peach 123 476 T. Li et al. species (Manubens et al. 1999; Hu et al. 2005; Da Silva Leaves (200 mg) were ground to fine powder in liquid Linge et al. 2011). However, there is limited knowledge on nitrogen with mortar and pestle and transferred to extrac- the evaluation of genetic diversity of P. mira. tion buffer (2% CTAB (w/v), 100 mM Tris-HCl, pH8.5, In this study, the genetic relationships of 83 P. mira 20 mM EDTA, 1.4 M NaCl, 0.5 % SDS, 2 % b-mercap- accessions from five populations were analyzed by AFLP toethanol (v/v), and 1 % PVP (w/v)), then incubated at technique. The objective was to investigate the pattern and 65 °C for 30 min. Subsequently, the mixture was centri- level of genetic diversity among five populations of P. mira fuged at 15,000g for 10 min at 4 °C. The supernatant was in the Linzhi region of the Tibetan plateau. washed with equal volume of chloroform: isoamyl alcohol (24:1) twice and was centrifuged at 15,000g for 10 min at 4 °C. After centrifugation, the remaining pellets were Materials and methods washed twice with ethanol (70 %), dried at room temper- ature, and dissolved in TE buffer (10 mM Tris-HCl, pH Plant materials 8.0, 1 mM EDTA, pH 8.0). DNA concentration and quality were estimated using spectrophotometer and 0.8 % agarose A total of 83 P. mira accessions from five natural popu- gel electrophoresis. lations including Langxian (LX), Gongbujiangda (GBJD), Milin (ML), Linzhi (LZ) and Bomi (BM) in the Linzhi AFLP analysis region of the Tibetan plateau were collected in spring of 2012 (Table 1; Fig. 1). In each population, individuals AFLP reactions were performed according to the method (from 16 to 18) were randomly selected exceeding 50 m of Vos et al. (1995) with some modifications. All primers interval. Young and healthy leaves sampled from individ- and adaptors are listed in Table 2. 150 ng DNA was uals were stored at -80 °C until DNA extraction. digested by 5 U EcoRI and 5 U MseI (Promega,Madison, Wisconsin, USA) in a total of 40 ll volume at 37 °C for DNA extraction 3 h, then incubated at 75 °C for 15 min. Digested DNA products were then ligated with 1 ll EcoRI adapter Total genomic DNA was extracted using the modified (50 lM) and 1 ll MseI adapter (5 lM) at 16 °C for 16 h. CTAB method described by Bouhadida et al. (2011). After ligation, the mixture was diluted 10 times with

Table 1 List of 83 Prunus mira accessions from five populations Population code Symbol Source Size Longitude (E) Latitude (N) Altitude (m)

LX (D) Langxian, Nyingchi, Tibetan, China 18 93°110 29°060 3,000–3,500 GBJD (d) Gongbo’gyamda, Nyingchi, Tibetan, China 17 93°250 29°920 3,000–3,500 ML (s) Milin, Nyingchi, Tibetan, China 16 94°130 29°180 2,500–3,000 LZ (j) Nyingchi, Nyingchi, Tibetan, China 16 94°370 29°680 3,000–3,100 BM (*) Bowo, Nyingchi, Tibetan, China 16 95°750 29°920 2,600–3,100

Fig. 1 Geographical distribution of five populations N of Prunus mira. A LX, B GBJD, C ML, D LZ, E BM B E

D

A C

50 Km

123 Analysis of genetic diversity in Prunus mira Koehne ex Sargent populations 477

Table 2 Sequences of adapters Name Code 50-Sequence-30 and selective primers EcoRI forward adapter EcoRI- F CTC GTA GAC TGC GTA CC EcoRI reverse adapter EcoRI- R AAT TGG TAC GCA GTC TAC MseI forward adapter MseI- F GAC GAT GAG TCC TGA G MseI reverse adapter MseI- R TAC TCA GGA CTC AT Pre-selective primers (EcoRI) EcoRI- P GAC TGC GTA CCA ATT C Pre-selective primers (MseI) MseI- P GAT GAG TCC TGA GTA A Selective primers EcoRI?AA E11 GAC TGC GTA CCA ATT C–AA EcoRI?AC E12 GAC TGC GTA CCA ATT C–AC EcoRI?AG E13 GAC TGC GTA CCA ATT C–AG EcoRI?AT E14 GAC TGC GTA CCA ATT C–AT EcoRI?CA E21 GAC TGC GTA CCA ATT C–CA EcoRI?TA E41 GAC TGC GTA CCA ATT C–TA MseI?AA M11 GAT GAG TCC TGA GTA A–AA MseI?AC M12 GAT GAG TCC TGA GTA A–AC MseI?AG M13 GAT GAG TCC TGA GTA A–AG MseI?AT M14 GAT GAG TCC TGA GTA A–AT MseI?CA M21 GAT GAG TCC TGA GTA A–CA MseI?GC M32 GAT GAG TCC TGA GTA A–GC MseI?GG M33 GAT GAG TCC TGA GTA A–GG MseI?GT M34 GAT GAG TCC TGA GTA A–GT

ddH2O. The pre-amplified reaction was performed using After electrophoresis, the gel was removed to acetic acid pre-selective primers. The pre-amplified mixture composed solution (1 %) for 15 min and then was washed in ddH2O, of 5 ll diluted solution, 10 9 PCR buffer, 2 mM dNTPs, finally was stained with silver nitrate solution (0.2 %) for

20 mM MgCl2, 20 mM pre-amplification primer and 1 U 20 min. The stained gel was washed twice in ddH2O and Taq DNA polymerase in 20 ll volume. The pre-amplified was placed in a developing solution (3 % sodium carbon- reaction was performed in the following conditions: ated and 0.06 % formaldehyde), the development was denaturation at 94 °C for 3 min, followed by 30 cycles of stopped with 1 % acetic acid (Bassam et al. 1991). After 94 °C for 30 s, 56 °C for 30 s, 72 °C for 1 min and a final rinsing with ddH2O, the gel was dried and was extension for 72 °C for 5 min. The amplified products were photographed. diluted 30 times with ddH2O and used as the template for selective amplification. Selective amplification reactions Data analysis were performed in 20 ll volume containing 5 ll template,

10 9 PCR buffer, 2 mM dNTPs, 25 mM MgCl2,20mM Only reproducible, clear and well-resolved AFLP frag- selective amplification primer and 1 U Taq DNA poly- ments were scored as presence (1) or absence (0) for each merase. PCR selective amplification was conducted as primer pairs. To estimate the genetic diversity of P. mira, follows: initial denaturation at 74 °C for 3 min, then 13 some genetic diversity parameters in populations, i.e., cycles of 94 °C for 30 s, 65 °C for 30 s (-0.7 °C at each observed number of alleles per locus (Na); effective num- cycle) and 72 °C for 1 min followed by 23 cycles of 94 °C ber of alleles (Ne), Nei’s gene diversity (H, Nei 1973); for 30 s, 56 °C for 30 s, 72 °C for 1 min and 72 °C for Shannon’s information index (I, Lewontin 1972) were 5 min. estimated using POPGENE32 software (version 1.32, Yeh et al. 1999). Silver staining A dendrogram according to similarity coefficient and principal component analysis (PCoA) was performed using PCR product was added to 20 ll formamide dye (98 % NTSYS-pc software (Version 2.10, Rohlf 2000). Correla- formamide, 10 mM EDTA, 0.005 % xylene cyanol FF, and tion analysis between geographic distance and genetic 0.005 % bromophenol blue), denatured at 95 °C for 5 min, distance was calculated by SPSS 13.0 (SPSS Inc., USA) and separated in 6 % polyacrylamide gel for 2.5 h at 80 W. (Peakall and Smouse 2006). Genetic variances within and

123 478 T. Li et al. among populations were calculated by AMOVA software 24.538, this result was same to that of number of poly- (version, 1.55, Excoffier et al. 1992). morphic bands.

Clustering analysis and PCoA of all accessions Results The similarity coefficient between accessions ranged from Polymorphism of AFLP 0.12 to 0.76, with an average 0.57. A UPGMA dendrogram based on similarity coefficient was obtained and repre- In this study, 13 AFLP primer combinations were used for sented in Fig. 3. In this dendrogram, the 83 accessions amplification of all 83 accessions, of which 13 were were clustered into two major clusters (cluster I and cluster polymorphic (100 % polymorphism). A profile amplified II) at similarity coefficient of 0.225. The cluster I was by E13-M13 was shown in Fig. 2. Table 3 showed the mainly formed by accessions from LZ (10), ML (11) and amplified characteristics of all primer combinations. Total BM (11) consisted. The cluster II contains two sub-clus- number of bands ranged from 12 to 35 with an average of tered (cluster IIA and cluster IIB). The cluster IIA was formed by all accessions from GBJD (17) and parts of accessions from LZ (6), ML (5) and BM (5). In general, LZ Population ML Population there accessions were not clustered clearly according to M their population affinity. By contrast, all accessions from

1000bp LX population clustered together (cluster IIB). PCoA analysis was performed to gain more information on genetic relationship of all accessions from five popu- 800 bp lations. In the three-dimensional scatter plot (Fig. 4), the 83 accessions were divided into three groups, which is con- 500 bp sistent with UPGMA dendrogram of the cluster analysis.

300 bp Genetic variation and clustering of populations

100 bp Based on AFLP data, estimates of the genetic variation in five populations were compared and listed in Table 4. Mean

of effective number of alleles (Ne) was 1.460 varying from 1.392 to 1.551. Nei’s gene diversity (H) varied from 0.300 to Fig. 2 AFLP figure of LZ and ML populations amplified by primer 0.439 with a mean of 0.374. Shannon’s Information index combination M13-E13. M indicates DNA marker ladder; population (I) was from 0.428 to 0.652 with an average of 0.542. The codes are listed in Table 1 highest values of Ne, Nei and I occurred in ML population. According to Nei’s genetic distance (1973) the UPGMA Table 3 Characteristics of 13 primer combinations for all accessions dendrogram of five populations was constructed (Fig. 5). Primer Total number Number of Polymorphic Two populations of BM and LX were clustered together, combinations of bands polymorphic bands bands (%) then with GBJD, and then with ML, and finally with LZ. E11-M11 26 26 100.00 There is no close relationships between geographic dis- E11-M13 20 20 100.00 tance and genetic distance (r = 0.671; p [ 0.05) (Fig. 6). E11-M34 21 21 100.00 AMOVA analysis was performed to estimate the per- E12-M12 34 34 100.00 centage among and within genetic variation (Table 5). The E12-M32 12 12 100.00 AMOVA results showed that 25.52 and 74.48 % variation E12-M34 29 29 100.00 occurred among and within the populations, respectively. E13-M13 35 35 100.00 E14-M14 28 28 100.00 Discussions E14-M32 18 18 100.00 E14-M33 19 19 100.00 AFLP polymorphism E14-M34 21 21 100.00 E21-M21 27 27 100.00 Many wild peach have been used for breeding new peach E41-M11 29 29 100.00 cultivars or be rootstock in the world (Quilot et al. 2005; Mean 24.538 24.538 100.00 Tsipouridis and Thomidis 2005; Cao et al. 2011). 123 Analysis of genetic diversity in Prunus mira Koehne ex Sargent populations 479

Fig. 3 UPGMA dendrogram of 83 accessions based on Nei’s coefficient of AFLP marker. open triangle LX, filled circle GBJD, open circle ML, filled square LZ, asterisk BM

A

B

Table 4 Genetic diversity estimates of Prunus mira based on AFLP data

Population Na Ne HI

LX 1.962 1.408 0.300 0.428 GBJD 1.846 1.392 0.364 0.519 ML 2.000 1.551 0.439 0.652 LZ 2.083 1.502 0.387 0.562 BW 1.917 1.447 0.381 0.548 Mean 1.962 1.460 0.374 0.542

Na observed number of alleles, Ne effective number of alleles, H Nei’s gene diversity, I Shannon’s information index

Interpretation of genetic relationship of peach accessions helps desirable selection. However, because of imitated morphological variability, it is difficult to distinguish P. mira germplasm using morphological characteristics. In the earlier studies, AFLP markers may be better suited for peach cultivars discrimination and map QTL mapping (Hu Fig. 4 Principal coordinate analysis (PCoA) showing relationships of et al. 2005; Xu et al. 2006). In the present study, the high all accessions from five populations on three-dimensional scatter plot. open triangle LX, filled circle GBJD, open circle ML, filled square levels of polymorphism (100 % polymorphism) were LZ; asterisk BM found in P. mira using AFLP markers. Thus, our results

123 480 T. Li et al. showed that AFLP was an effective method to distinguish populations. It could be due to a reason that woody species all P. mira accessions and also indicated the relative with large geographic ranges, long life history (2000 years abundance of P. mira germplasm. or so), animal-ingested dispersal and out-crossing breeding systems may result in more genetic diversity within pop- Genetic diversity of Prunus mira ulations but less variation among populations (Hamrick and Godt 1996). Or flowers of P. mira are mainly polli- The UPGMA dendrogram did not produce significant nated by wind or insects. And P. mira seeds are also dis- groups related to the geographical origin (Fig. 3). The persed by bird or animal for long distances. These may be dendrogram showed two main clusters. In cluster I, parts of important factors promoting gene flow between popula- accessions from three populations (BM, ML and LZ) were tions of P. mira. Tan et al. (2012) also observed low distributed dispersedly. Our data revealed that there was no genetic difference between populations of P. mira using significant relationship between geographic distribution SRAP technique. However, only accessions from LX and genetic distance of populations (Fig. 6), and AMOVA population were clustered together (in cluster IIB), sug- analysis implied that most of the genetic variations gesting narrow genetic base and low genetic differentia- occurred within populations (Table 5), which suggested tion. These results may be attributed to remote geographic high gene flow and low genetic differentiation between setting, leading to low gene flow, genetic drift and lim- ited human disturbance.

Conservation recommendations for P. mira

A better understanding of genetic diversity is very essential for improvement and conservation practices of those important plant resources, which will help us to determine how to conserve (Rao and Hodgkin 2002). Now, P. mira has been listed as an endangered plant species in China since 1987 (Fu 2002). Hence, it is urgent to save P. mira germplasm resource. In situ conservation is considered the most appropriate way of conserving genetic diversity of plant species because it can keep the genetic structure of a population intact in dynamic pro- Fig. 5 Genetic relationship among five populations of Prunus mira cess while allowing the evolutionary process in environ- according to Nei’s genetic distance mental condition (Eriksson et al. 1993). However, in situ conservation has shortcoming because its implementation 0.25 needs appropriate political conditions and policy as well

0.2 y = 0.0007x - 0.026 as understanding of the natural environment and biolog- R2 = 0.45 ical characteristics of plant species (Gole et al. 2008). In 0.15 Tibet, it is difficult to conserve P. mira gene pool by in 0.1 situ conservation for its distinct natural environment. So, there is an urgent need for another method. In Tibet, our Genetic distance 0.05 observations show that P. mira has been in danger of 0 extinction in some of its original range, due to defores- 0 50 100 150 200 250 300 Geographic distance (Km) tation for agriculture and pasture, and over-exploitation from unregulated harvesting (Fang et al. 2008; Tan et al. Fig. 6 The relationship between geographic distance and genetic 2012). Therefore, ex situ conservation needs to be distance (Km) established for P. mira. Ex situ conservation of wild plant

Table 5 AMOVA analysis of genetic variance within and among populations based on AFLP data Source of variation df Sum of squares Mean squares Variation components Total variation (%) P value

Among populations 4 167.200 41.800 1.240 25.52 \0.001 Within populations 78 1,656.150 21.233 2.233 74.48 \0.001 Total 82 1,823.350 63.033 3.473 100

123 Analysis of genetic diversity in Prunus mira Koehne ex Sargent populations 481 species through seed banks is helpful in preserving Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular genetic diversity of wild plants (Hamilton 1994). Sam- variance inferred from metric distances among DNA haplotypes: application to Human mitochondrial DNA restriction data. pling collections in ex situ conservation may play an Genetics 131:479–491 important role in preserving genetic diversity and evolu- Falk DA, Holsinger RE (1991) Genetics and conservation of rare tionary potential of population (Volis et al. 2009). Thus plants. Oxford University Press, New York sampling guidelines for the ex situ conservation of P. Fang JP, Zhong ZC, Zhong GH (2008) The age structure of Tibetan Prunus mira Koehne Kov et Kpsl population in Tibet Linzhi mira need to be created. Accordingly, understanding the region. China For Sci Technol 22:53–56 knowledge of the distribution of population and molecular Francisco-Ortega J, Santos-Guerra A, Kim SC (2000) Plant genetic genetic data on P. mira will play a key role in estab- diversity in the Canary Islands: a conservation perspective. Am J lishment of appropriate and effective conservation strat- Bot 87:909–919 Fu LK (2002) China plant red data book: rare and endangered plants, egies (Falk and Holsinger 1991; Francisco-Ortega et al. I. Science Press, Beijing 2000). Therefore, as many population samples of P. mira Geng YF, Zhang JF, Li YP (2008) The technique of seedlings of as possible and molecular marker techniques should be Prunus mira Koehne. Pract For Technol 3:44–45 used to test the genetic diversity of P. mira from entire Gole TW, Borsch T, Denich M et al (2008) Floristic composition and environmental factors characterizing coffee forests in southwest range in Tibet in the future. Ethiopia. Forest Ecol Manag 255:2138–2150 Hamilton MB (1994) Ex Situ Conservation of Wild Plant Species: time to reassess the genetic assumptions and implications of seed Conclusion banks. Conserv Biol 8:39–49 Hamrick JL, Godt MJW (1996) Effects of life history traits on genetic diversity in plant species. Philos Trans Biol Sci 35:1291–1298 In conclusion, the AFLP markers are an efficient mean to Hao HP, Jiang CD, Shi L et al (2009) Effects of root temperature on separate all P. mira accessions. High genetic diversity was thermostability of photosynthetic apparatus in Prunus mira observed among P. mira accessions, suggesting they are a seeding. Chin J Plant Ecol 33:984–992 Hu D, Zhang A, Zhang D et al (2005) Genetic relationship of promising source of new genes for rootstock cultivar and ornamental peach determined using AFLP markers. HortScience breeding of peach. At the same time, ex situ conservation 40:1782–1786 needs to be established for P. mira. Lewontin RC (1972) Testing the theory of natural selection. Nature 236:181–182 Acknowledgments This study was supported the Fund for Foster- Manubens A, Lobos S, Jadue Y et al (1999) DNA isolation and AFLP ing Talents in Basic Science of the National Natural Science Foun- fingerprinting of nectarine and peach varieties (Prunus persica). dation of China (No. J1210053), the National Natural Science Plant Mol Biol Rep 17:255–267 Foundation of China (31170568; 31201594). Meudt HM, Clarke AC (2007) Almost forgotten or latest practice? AFLP applications, analyses and advances. Trends Plant Sci 12:106–117 Mueller UG, Wolfenbarger LL (1999) AFLP genotyping and References fingerprinting. Trends Ecol Evol 14:389–394 Nei M (1973) Analysis of gene diversity in subdivided. Proc Natl Bassam BJ, Caetano Anolles CG, Gresshoff PM (1991) Fast and Acad Sci USA 70:3321–3323 sensitive silver staining of DNA in polyacrylamide gels. Anal Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Biochem 196:80–83 Excel: population genetic software for teaching and research. Bouhadida M, Moreno M, Gonzalo MJ et al (2011) Genetic Mol Ecol Notes 6:288–295 variability of introduced and local spanish peach cultivars Quilot B, Ge´nard M, Lescourret F et al (2005) Simulating genotypic determined by SSR markers. Tree Genet Genomes 7:257–270 variation of fruit quality in an advanced peach9Prunus davidi- Cao CP, Finkeldey R, Siregar IZ et al (2006) Genetic diversity within ana cross. J Exp Bot 56:3071–3081 and among populations of Shorea leprosula Miq. and Shorea Rao VR, Hodgkin T (2002) Genetic diversity and conservation and parvifolia Dyer (Dipterocarpaceae) in Indonesia detected by utilization of plant genetic resources. Plant Cell Tiss Org 68:1–19 AFLPs. Tree Genet Genomes 2:225–239 Rohlf FJ (2000) NTSYS-pc: numerical and multivariate Cao K, Wang L, Zhu G et al (2011) Construction of a linkage map analysis system, Version 2.10. Exeter Software, New York and identification of resistance gene analog markers for root- Russell JR, Fuller JD, Macaulay M et al (1997) Direct comparison of knot nematodes in wild peach, . J Am Sol levels of genetic variation among barley accessions detected by Hortc Sci 136:190–197 RFLPs, AFLPs, SSRs and RAPDs. Theor Appl Genet 95:714–722 Christensen S, von Bothmer R, Poulsen G et al (2011) AFLP analysis Tan JP, Xl Zeng, Liao MA (2012) Genetic diversity of natural Prunus of genetic diversity in leafy kale (Brassica oleracea L. convar. mira populations detected by SRAP. Acta Prataculturae Sinica acephala (DC.) Alef.) landraces, cultivars and wild populations 21:213–220 in Europe. Genet Resour Crop Ev 58:657–666 Tsipouridis C, Thomidis T (2005) Effect of 14 peach rootstocks on Da Silva Linge C, Pacheco I, Fricano A et al (2011) Assessing genetic the yield, fruit quality, mortality, girth expansion and resistance diversity in peach by AFLP and SSR markers. Minerva to frost damages of May Crest peach variety and their Biotecnol 23:11–22 susceptibility on Phytophthora citrophthora. Sci Hortic Dong GZ (1991) The investigation of Prunus mira Koehne in Tibet. 103:421–428 Quarterly Forest by-product and Speciality in China 3:44–45 Volis S, Blecher M, Sapir Y (2009) Complex ex situ-in situ approach Eriksson G, Namkoong G, Roberds JH (1993) Dynamic gene for conservation of endangered plant species and its application conservation for uncertain futures. Forest Ecol Manag 62:15–37 to Iris atrofusca of the Northern Negev. BioRisk 3:137–160

123 482 T. Li et al.

Vos P, Hogers R, Bleeker M et al (1995) AFLP: a new technique for Biology and Biotechnology Center, Unversity of Alberta, DNA fingerprinting. Nucl Acids Res 23:4407–4414 Canada Wang JL, Hu SY, Wang ZK (1997) A comparative study on the Zhong ZC (2008) Studies on resource ecology of Prunus mira photosynthetic chanracteristics of Tibetan Prunus. Acta Hortic Koehne (Amygdalus mira Koehne kow et. Kpst) in Xizang Sin 24:197–198 (Tibet) Linzhi. Thesis for a MD degree in agricultural science Xu DH, Wahyuni S, Sato Y et al (2006) Genetic diversity and from Agricultural and Animal Husbandry of Tibet University, relationships of Japanese peach (Prunus persica L.) cultivars China revealed by AFLP and pedigree tracing. Genet Resour Crop Ev Zhong ZC, Fang JP, Pu Q (2010) Analysis on fruit growth rhythm and 53:883–889 output of Prunus mira in Nyingchi prefecture of Tibet. J Anhui Yeh FC, Yang RC, Boyle TBL et al (1999) POPGENE, the User Agri Sci 38:12047–12049 Friendly shareware for Populaiton Genetic Analysis. Molecular

123