ORIGINAL PAPER

Genetic diversity, linkage disequilibrium, and association mappinganalyses of peach (Prunus persica) landraces in China

Ke Cao & Lirong Wang & Gengrui Zhu & Weichao Fang &

Changwen Chen & Jing Luo

Received: 6 May 2011 /Revised: 14 November 2011 /Accepted: 14 February 2012 /Published online: 16 March 2012# Springer-Verlag 2012

Abstract The genetic diversity, population structure, andlinkage disequilibrium (LD) of peaches are greatly impor-tant in genome-wide association mapping. In the currentstudy, 104 peach landrace accessions from six Chinesegeographical regions were evaluated for fruit and phenolog-ical period. The accessions were genotyped with 53genome-wide simple sequence repeat (SSR) markers. AllSSR markers were highly polymorphic across the acces-sions, and a total of 340 alleles were detected, including59 private alleles. Of the six regions studied, the northernpart of China as well as the middle and lower reaches of theChangjiang River were found to be the most highly diversegenetically. Based on population structure analysis, thepeaches were divided into five groups, which well agreedwith the geographical distribution. Of the SSR pairs in theseaccessions, 18.07% (P<0.05) were in LD. The mean r2 valuefor all intrachromosomal loci pairs was 0.0149, and LDdecayed at 6.01 cM. The general linear model was used tocalculate the genome-wide marker-trait associations of 10complex traits. The traits include flesh color around the stone,red pigment in the flesh, flesh texture, flesh adhesion, fleshfirmness, fruit weight, chilling requirement, flowering time,ripening time, and fruit development period. These traits wereestimated by analyzing the 104 landraces. Many of the asso-ciated markers were located in regions where quantitative traitloci (QTLs) were previously identified. Peach associationmapping is an effective approach for identifying QTLs and

may be an alternative to QTL mapping based on crossesbetween different lines.

Keywords Genetic diversity . Linkage disequilibrium .

Association mapping . Peach landrace

Introduction

Peach (Prunus persica L.) was originally domesticated inChina 4,000–5,000 years ago (Faust and Timon 1995).Peach is still grown now as a delicious and healthy summerfruit in the Chinese provinces of Guangdong in the south toHeilongjiang in the north. According to topographic distri-bution, peach landrace cultivation in China is concentratedin seven main areas: the Qinghai–Tibetan Plateau, northwestChina, the YunGui plateau, northeast China, northern China,the middle and lower reaches of the Changjiang River, andsouthern China (Wang and Zhuang 2001). The diverseresources in these regions have already contributed manyuseful alleles to the cultivated gene pool (especially fruitquality-related alleles) that have become indispensable inpeach breeding.

Understanding the molecular genetic control of pheno-typic variations (such as yield and quality-related traits) isvery essential and remains a major task in the genetic studyof some important crops (Jin et al. 2010). Peach has agenome size of 220 Mb, which is approximately twice thesize of Arabidopsis. Given this relatively small size, peachwas used as the model species for studying genomics inRosaceae. Ever since, peach has received increasing atten-tion in the genetic dissection of simple or complex traits bylinkage mapping. So far, 23 monogenic morphological traitsassociated with adaptation, flower color, fertility, leaf shapeand color, plant habit, fruit quality, as well as pest resistance

Communicated by A. Abbott

K. Cao : L. Wang (*) :G. Zhu :W. Fang : C. Chen : J. LuoZhengzhou Fruit Research Institute,Chinese Academy of Agriculture Sciences,Zhengzhou 450009, People’s Republic of Chinae-mail: wyandck@126.com

Tree Genetics & Genomes (2012) 8:975–990DOI 10.1007/s11295-012-0477-8

have been described through linkage analysis. Quantitativetrait loci (QTLs) have also been identified for 23 horticultur-ally important traits, including bloom and ripening time, fruitquality, storage life, freestone trait, as well as pest resistance(Hancock 2008). Traditional QTL mapping is an importanttool in crop gene tagging. However, for the study of linkage,suitably designed crosses need to be performed. These crossessometimes lead to the development of mapping populations ornear-isogenic lines. Crossing is a serious limitation on the useof QTLmapping in some cases because the desired crosses arenot applicable in all cases (e.g., sterility in distant hybridiza-tion). Mapping populations examined for this purpose are alsosometimes too small (Pushpendra et al. 2005). Associationmapping is another effective approach for connecting pheno-type and genotype in plants when information on populationstructure and linkage disequilibrium (LD) is available(Thornsberry et al. 2001). Association mapping complementsand enhances previous QTL information for marker-assistedselection in rice (Agrama et al. 2007), wheat (Tommasini et al.2007), and maize (Yu and Buckler 2006).

Many important crops have complex population structuresthat arose from a long domestication and breeding history(Flint-Garcia et al. 2003). Understanding these structures isimportant to avoid identifying spurious associations in asso-ciation mapping. With the development of statistical methods,independent markers that are distributed throughout thegenome can be successfully used to detect population struc-tures (Pritchard et al. 2000). Previous studies on crop popula-tion structure as well as its effect on crop diversity and LD areabundant. Jin et al. (2010) detected 416 rice accessions, in-cluding landraces, and breeding lines collected mostly inChina, using 100 genome-wide simple sequence repeat(SSR) markers. A model-based population structure analysisdivided the rice materials into seven subpopulations. Of theSSR pairs in these accessions, 63% were in LD. The intra-chromosomal LD average was 25–50 cM for different sub-populations. Kwak and Gepts (2009) analyzed the genome-wide genetic composition at 26, mostly unlinked microsatel-lite loci in 349 accessions of the wild and domesticatedcommon bean from the Andean and Mesoamerican genepools. Nine wild or domesticated populations, including fourof Andean and four of Mesoamerican origins, were identified.In fruit trees, the genetic structure of sweet cherry was con-structed on 207 of 211 wild cherry varieties. The structureresults revealed three populations, namely, wild cherries, land-races, and modern varieties (Mariette et al. 2010). To date,only a single study has been conducted on 224 peach com-mercial varieties using 50 SSRs (Aranzana et al. 2010). LDanalyses of three peach subpopulations therein revealed highlevels of LD conservation in all populations extending up to13–15 cM, no association analysis was done in this paper.

In the current paper, we examined the 104 landraces,except for improved varieties, from six geographical regions

in China with a set of 53 SSRs that cover the peach genome.These markers were used to analyze the genetic diversity,population structure, and LD extent between SSR markerpairs. Association mapping was also performed for severalfruit and phenological periods of peach landrace accessionscollected in China. The results of the current study providemolecular information for exploring the QTLs of importantagronomic peach traits. The results will also help utilize andconserve Chinese peach landraces effectively.

Materials and methods

Plant materials

Considering that genetic recombination and artificial selec-tion would interfere with LD analyses, commercial varietieswere excluded from the dataset. A total of 104 peach land-races (Table 1) were selected from the National Clonal Germ-plasm Repository of Peach Centers (Zhengzhou, China).According to the classification established by Wang andZhuang (2001), the accessions were divided into six differentgeographical populations (Fig. 1), such as northwest China(NWC), the YunGui plateau (YGC), northeast China (NEC),northern China (NC), the middle and lower reaches of theChangjiang River (MLCJ), and southern China (SC). No wildpeach accession was used because a database of its fruit orphenological period traits is very difficult to obtain.

Phenotypic data

The resulting materials were grafted on the same rootstock(Prunus davidiana). They were planted in the National ClonalGermplasm Repository of Peach Center's orchard in Zhengzhou,China in the year 2000. The trees were trained in a Y shape andwere planted at a spacing of 5×2 m. Hand thinning was carriedout to reduce fruit load when required. The trees were grownunder standard conditions of irrigation, fertilization, as well aspest and disease control.

Fruit quality (red pigment in flesh, flesh color around thestone, flesh texture, stone adhesion to flesh, fruit weight,and flesh firmness without skin) and chilling requirementtraits were evaluated in 2007, phenological period (flower-ing time, ripening time, and fruit development period) traitswere evaluated over three consecutive years (2006–2009),and more integrate data (2007 and 2008) were chosen foranalysis. Fruit quality traits from each accession were eval-uated immediately after harvest. A fruit on a tree was con-sidered ripe based on Celia et al. (2010). For the evaluationof fruit quality parameters, a representative sample consist-ing of 10 fruits per tree was selected. Color-card readingswere recorded from the central section of the flesh to theflesh around the stone. To qualitatively score flesh color, six

976 Tree Genetics & Genomes (2012) 8:975–990

Table 1 Peach germplasm accessions included in the current study and their geographical locations in China

Geographicpopulation

Origin(province)

No. Accession name Geographic regions Province oforigin

No. Accession name

Northwest China Sinkiang 1 Kashi 1# Northern China Beijing 53 Wu Yue Xian

Sinkiang 2 Kashi 2# Beijing 54 Wu Yue Xian Bian Gan

Sinkiang 3 Kashi 3# Beijing 55 Shiwo Shui Mi

Sinkiang 4 Xinjiang Huang Rou Beijing 56 Fei Tao

Sinkiang 5 Xinjiang Pan Tao Beijing 57 Ju Hua Tao

Sinkiang 6 Tian Ren Tao Beijing 58 Yuan Yang Chui Zhi

Sinkiang 7 Tu-2 Beijing 59 Hong Hua Bi Tao

Sinkiang 8 Tie 4-1 Beijing 60 Bai Hua Shan Bi Tao

Sinkiang 9 Bi Nan I Tianjin 61 Tian Jin Shui Mi

Sinkiang 10 Mi Yang Shan Shanxi 62 Yangquan Rou Tao

Sinkiang 11 Moyu 8# Hebei 63 Ge Gu

Sinkiang 12 Li He Tian Ren Hebei 64 Hong Ya Zui

Sinkiang 13 Hetian Huang Rou Hebei 65 Da Xue Tao

Sinkiang 14 Yexian Huang Rou Tao Hebei 66 Shen Zhou Shui Mi

Sinkiang 15 Da Li He Huang Rou Hebei 67 Shenzhou Bai Mi

Sinkiang 16 Kashi Huang Rou Li Guang Hebei 68 Shenzhou Li He Shui Mi

Sinkiang 17 Yilixian Huang Rou Tao Henan 69 Ji Zhui Bai

Sinkiang 18 Hong Li Guang Henan 70 Yexian Dong Tao

Sinkiang 19 Huang Li Guang Henan 71 Ren Mian Tao

Gansu 20 Ying Chun Henan 72 Wu Bao Tao

Gansu 21 Bai Sha Henan 73 Jiang Tao

Gansu 22 Qi Tao Henan 74 Hong Chui Zhi

Gansu 23 Dunhuang Dong Tao Henan 75 Sa Hong Tao

Gansu 24 Kashi 4# Shandong 76 Hei Bu Dai

Gansu 25 Zhao Shu Huang Gan Shandong 77 Da Guo Hei Tao

Gansu 26 Gaotai 1# Shandong 78 Wu Hei Ji Rou Tao

Gansu 27 Tugou 1# Shandong 79 Shandong Si Yue Ban

Gansu 28 Zhang Huang 9# Shandong 80 Qingzhou Hong Pi Mi Tao

Gansu 29 Lin Huang 9# Shandong 81 Fei Cheng Hong Li 6#

Gansu 30 Lin Bai 10# Shandong 82 Fei Cheng Bai Li 17#

Gansu 31 Zhang Bai 5# The middle and lowerreaches of the ChangjiangRiver

Sichuan 83 Jiu Yang Qing Tao

Gansu 32 Zhang Bai Gan Sichuan 84 Qing Mao Zi Bai Hua

Shaanxi 33 Xi Jiao 2# Hubei 85 Liu Yue Bai

Shaanxi 34 Xi Jiao 3# Hubei 86 Da Hong Pao

Shaanxi 35 Qinling Dong Tao Hubei 87 Zao Chun Tao

YunGui plateau Yunnan 36 Qing Si Tao Anhui 88 Ying Zui

Yunnan 37 Huang Yan Anhui 89 Diao Zhi Bai

Yunnan 38 Bai Li Hu Jiangsu 90 Fen Shou Xing

Yunnan 39 Bai Nian Hu Jiangsu 91 Hong Shou Xing

Yunnan 40 Huang Nian He Jiangsu 92 Ping Bei Zi

Yunnan 41 Bai Nian He Jiangsu 93 Wan Pan Tao

Yunnan 42 Huo Lian Jin Dan Jiangsu 94 Lulin Shui Mi

Guizhou 43 Qing Tao Shanghai 95 Bai Dan Ban

Guizhou 44 Guizhou Shui Mi Shanghai 96 Bai Mang Pan Tao

Guizhou 45 Guangyi Bai Hua Tao Shanghai 97 Sha Hua Hong Pan Tao

Guizhou 46 Wangmo Xiao Mi Tao Zhejiang 98 Yu Lu Pan Tao

Northeast China Jilin 47 Hun Chun Tao Zhejiang 99 Fenghua Pan Tao

Jilin 48 8501 Zhejiang 100 Jiaqing Pan Tao

Jilin 49 8601 Zhejiang 101 Li He Pan Tao

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(0–5) color-card categories were used. Flesh texture (melt-ing, M, or non-melting, NM) and flesh adhesion (freestoneor clingstone) were scored using the rating scales appropri-ated for each quality by three researchers. These morpho-logical descriptions and criteria are all summarized inTable 2.

The average fruit weight (in grams per fruit) wasdetermined using a BS200s electronic balance (Sartorius,Goettingen, Germany) for each seedling tree. Flesh firm-ness without skin measurements were performed on theopposite equatorial sides of each fruit on each tree usinga FT-327 hand penetrometer (Breuzzi, Milan, Italy). Dataare given in kilograms per square centimeter. In thecurrent paper, the relative data for flowering and ripeningtimes, compared with the oldest full bloom data (March16) and the harvest maturity data (June 12), wererecorded for each accession. The mean date of the 3 yearswas also calculated. The fruit development period wasjudged as the date of the first full bloom to the harvestmaturity date. Chilling requirement was calculated simi-larly as reported by Fan et al. (2010).

SSR marker genotyping

Genomic DNAwas extracted from leaf tissues using a DNAextract kit (Sangon, Shanghai, China). Fifty-three SSR

markers (average density010 cM per marker) distributedon the eight linkage groups of the Prunus T×E referencemap (519 cM; Dirlewanger et al. 2004a) were selected(Table 3). The polymerase chain reaction, amplification, anddetection were performed as described in Cao et al. (2011).

All amplifications were scored as either a present (1) oran absent (0) band. The alleles were coded A, B, C, etc. indecreasing size order for each band. A single band in oneaccession was assumed to be homozygous.

Genetic diversity and phylogenetic analyses

The molecular diversity parameters of the six geograph-ical populations, such as number of alleles per locus(NA), observed heterozygosity (HO), Shannon’s informa-tion index (I), number of private alleles (NPA), geneticdistance (Nei et al. 1983), and pair-wise FST among thesix geographical populations were all estimated withPOPGENE version 1.31 developed by Yeh et al. (1999).Based on the genetic distances (Nei et al. 1983) of the sixgeographical populations, a neighbor-joining tree was con-structed. Gene diversity (GD) and the polymorphism informa-tion content (PIC) of the six geographical populations werecalculated with Powermarker version 3.25 (Liu and Muse2005, http://statgen.ncsu.edu/powermarker/). Alleles wereconsidered private if they occurred in less than 1% of theaccessions.

Nei’s genetic distances (Nei et al. 1983) among the104 accessions were calculated with the Powermarkersoftware. A dendrogram was constructed with the samesoftware using the neighbor-joining method with arith-metic mean.

Table 1 (continued)

Geographicpopulation

Origin(province)

No. Accession name Geographic regions Province oforigin

No. Accession name

Jilin 50 8701 Zhejiang 102 Chang Sheng Pan Tao

Jilin 51 8801 Southern China Guangxi 103 Xian Tao

Jilin 52 8903 Guangdong 104 Nanshan Tian Tao

Fig. 1 Peach landraces were divided into six large geographic regions,namely, northwest China (NWC), the YunGui plateau (YGC), northeastChina (NEC), northern China (NC), the middle and lower reaches ofthe Changjiang River (MLCJ), and southern China (SC)

Table 2 Four characteristic descriptions and their coding

Character Classification

Flesh color around thestone

No00; little01; moderate02; much03

Red pigment in flesh No00; little01; moderate02; much03;very much04

Flesh texture Non-melting00; hard melting01; softmelting02; wooliness03

Flesh adhesion Free00; semi-free01; cling02

978 Tree Genetics & Genomes (2012) 8:975–990

Population structure analysis

The model-based software Structure version 2.3.3 (Pritchardand Wen 2004, http://pritch.bsd.uchicago.edu/software.html) was used to infer the population structure (testingfrom K02 to K010) using a burn-in of 10,000 and a runlength of 100,000. Three independent runs yielded consis-tent results. A model-based clustering algorithm was appliedto identify subgroups with distinctive allele frequencies. Kwas chosen in advance, but was varied for independent runsof the algorithm. The most likely number of clusters (K) wasselected by comparing the logarithmized probabilities ofdata L(K) and ΔK according to Evanno et al. (2005).

LD and association analysis

As P. persica is a heterozygous species, it is not possible todistinguish the two possible double heterozygotes AB/aband Ab/aB when parentage is unknown (Barnaud et al.2006). Therefore, LD and association analysis were mea-sured using haplotypic data reconstructed by PHASE 2.1software (Stephens et al. 2001) based on raw populationgenotypic data prior TASSEL 2.0.1 software (http://www.maizegenetics.net/). The haplotypic data within whole link-age group were inferred using a Bayesian method. Then LDwas evaluated for each pair of SSR loci using the TASSEL2.0.1. All accession clusters were inferred by the Structuresoftware as a covariate. The pairs of loci were considered tohave a significant LD if P was <0.1. The significance(P values) of r2 for each SSR pair was determined by50,000 permutations. The LD decay was evaluated whenr200.1, and the curve was performed using curve regressionof SPSS software. The estimated genetic distance (in centi-morgans) between loci was inferred from the genome data-base for Rosaceae (Washington State University 2010).

The associations among marker alleles and different phe-notype data in 2007 and 2008, respectively, were performedwith the general linear model (GLM) method using theTASSEL 2.0.1 software. The P value determined whethera marker (QTL) was associated with the trait, and the r2

marker evaluated the magnitude of the QTL effects.

Results

Genetic diversity of all markers

All 53 SSR markers distributed across the genome every10-cM interval were used to evaluate the genetic diversity ofthe population. All of the markers were polymorphic andproduced a total of 340 alleles from the 104 accessions. Theaverage number of alleles per locus was 6.4, ranging from 2(CPPCT008 on chromosome 6) to 21 (BPPCT008 on chro-mosome 6). The average genetic diversity was 0.567, ranging

from 0.038 (CPPCT008) to 0.865 (BPPCT008). The averagePIC value was 0.533, ranging from 0.037 (CPPCT008) to0.853 (BPPCT008).

Genetic diversity of geographic populations

Genetic diversity (such as the gene diversity PIC) and ob-served heterozygosity for each geographic population wasassessed (Table 4). The number of alleles per locus variedfrom 1.7885 in SC to 3.8868 in the NC population. Ob-served heterozygosity ranged from 0.1584 in NWC to0.3046 in the MLCJ population. The MLCJ populationhad the highest gene diversity of 0.5616, and the secondhighest I index of 0.8086, with 3.1887 alleles per locus. TheNC population followed, with the second highest genediversity and the highest I index at with 3.8868 alleles perlocus. The PIC values varied from 0.2264 to 0.5202, withthe NC and SC populations showing the highest and lowest,respectively. Among the 340 alleles detected in all sixpopulations, 59 (17.35%) alleles were population-specificor private (NPA) in 26 loci of 34 accessions. The NC popu-lation had more private alleles (25 or 7.35%) than others.Among these private alleles, 12 unique private alleles werefound in the Bai Hua Shan Bi Tao accession, and fiveprivate alleles were found in the Nanshan Tian Tao. Insummary, the NC and MLCJ populations showed highestvalues for genetic variability, whereas the SC populationexhibited the lowest level of diversity.

Genetic relationships among geographic populations

The overall FST revealed a considerable and statisticallysignificant degree of differentiation among the peach pop-ulations in China. The FST for each locus ranged from0.0005 (CPPCT008) to 0.4693 (CPPCT018), with an aver-age FST of 0.1931. This result indicated that 19.31% of thetotal variation in allele frequency of the 104 accessions wascaused by the genetic differences among the clusters. Pair-wise comparison on the basis of the values of FST could beinterpreted as the standardized population distances be-tween two populations. FST values among pairs of pop-ulations were found to range from 0.0342 (between theNC and MLCJ populations) to 0.2558 (between the NECand SC populations), with an overall average of 0.1273(Table 5). This result indicated that genetic differentiationamong the clusters was highest in the combination of theNEC and SC populations. The genetic distance dataagreed with the FST estimates. Within the geographicalpopulations, the average Nei’s genetic distance betweenall pairs of populations was 0.1927 (Table 4). The lowestdistance value was obtained for the pair of populationsNC–MLCJ (0.0546), whereas the highest value wasobtained for populations NEC–SC (0.3948).

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A neighbor-joining tree of the seven geographical popula-tions (Fig. 2) was constructed based on pair-wise Nei’s geneticdistances calculated by the POPGENE software. Table 5shows that these populations could be clustered into fourgroups. The first group, the SC population, was very distinct.The second group was the NEC population, the third groupwas the YGC population, and the fourth group was constitutedby the NWC, NC, and MLCG populations. Among the

populations in the fourth group, NC and MLCG, which hadthe highest genetic differentiation, were classified into sub-groups. The results showed that the SC population had thelargest genetic distance from the others, followed by the NECand the YEC populations. These results were in agreementwith the geographical distances among these regions.

A dendrogram based on the genetic similarity matrixdivided the 104 landraces accessions into five clusters

Table 4 Summarized statistics of genetic diversity for the six geographical populations

Population Number NA Ho I GD PIC NPA

NWC 35 3.5094 0.1584 0.7295 0.4714 0.4382 11

YGC 11 2.6038 0.2200 0.6631 0.4750 0.4217 6

NEC 6 2.1698 0.1604 0.5556 0.3648 0.3183 2

NC 30 3.8868 0.2170 0.8730 0.5564 0.5202 25

MLCJ 20 3.1887 0.3046 0.8086 0.5616 0.5115 9

SC 2 1.7885 0.2212 0.4655 0.3019 0.2264 6

NWC northwest China, YGC the YunGui plateau, NEC northeast China, NC northern China,MLCJ the middle and lower reaches of the ChangjiangRiver, SC southern China, NA the number of alleles per locus, Ho the observed heterozygosity, I Shannon’s information index, GD gene diversity,PIC polymorphism information content, NPA the number of private alleles

Table 3 The 53 mappedSSR loci on the eight peachlinkage groups

Linkage group SSR Location (cM) Linkage group SSR Location (cM)

1 CPSCT008 9 5 CPSCT011 5.2

CPPCT027 23.1 UDP97-401 11

CPPCT026 33.9 BPPCT017 20.1

BPPCT020 52.6 BPPCT037 25.6

BPPCT016 55.2 BPPCT032 34.7

EPDCU2862 66.5 CPPCT025 37.4 (GN)

BPPCT028 77.4 CPSCT022 40.7

2 CPDCT044 12.5 BPPCT014 44

CPSCT044 23.6 6 CPPCT008 8.7

UDP96-013 27.8 BPPCT008 30.1

UDP98-411 27.8 CPPCT015 35.8

BPPCT030 38 UDP98-407 49.7 (GN)

pceGA34 43.9 BPPCT025 56.4

BPPCT034 48.6(GN) UDP98-412 72

UDP98-406 92.8(GN) CPPCT030 80.2

3 BPPCT007 11.2 7 CPPCT022 18.6

CPPCT018 18 UDP98-405 22.3

CPDCT008 28.4 BPPCT029 29.6

CPDCT025 36.4 CPPCT033 38.9

UDP96-008 36.4 CPSCT042 41.3

CPDCT027 46.4 pchcms2 51.4

4 CPSCT039 1.8 8 CPSCT018 0

pchgms2 7 BPPCT006 14.1

CPPCT005 10.4 UDP96-019 20.8

CPDCT045 16.8 CPPCT006 24.8

BPPCT023 47.4 UDP98-409 44.5

BPPCT031 112.2 (GN)

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(Fig. 4a, b). All accessions from the SC region, 5 of 11 fromthe YGC region, 13 of 30 from the NC region, and 8 of 20from the MLCG region were placed in the G1 cluster.Most accessions of the NWC and NEC populations wereplaced in the G5 and G3 clusters, respectively. However,a few accessions displayed as admixtures in the differentclusters. For example, the typical accessions of the NECpopulation, such as Hun Chun Tao, 8501, 8601, 8701,and 8801, were assigned to the G3 cluster, but oneaccession (8903) of the NEC population was placed inthe G1 cluster in the dendrogram (shown in dark red inFig. 2b). The χ2 test showed that these clusters revealedgenetic relationships fairly consistent with the geographicorigin of most accessions (Table 5).

Population structure

Association mapping requires population structures to betaken into account to avoid identifying spurious associations(Yu et al. 2006). The estimated likelihood values for a givenK in three independent runs yielded consistent results usingthe structure program. However, the distribution of log Pr(X/K) increased continuously with increased K values. Toovercome difficulties in determining the real value of K,another quantity criterion (ΔK) was used (Evanno et al.2005). In the current study, the value of ΔK for the 104peach accessions was highest at K05 (Fig. 3). This resultsuggested that these peach accessions can be grouped intofive populations, herein denoted as POP1, POP2, POP3,POP4, and POP5, respectively (Fig. 4, Table 6). Therefore,the respective Q matrix outputs of the five population runsfor the structure-based association analysis were used.

According to the membership pattern, when K05, the104 accessions were classified into five groups. POP2contained 20 accessions, of which 10 originated from theNC population. POP5 contained 21 accessions, mainly in-cluding the NWC population (Fig. 4). POP1, POP3, andPOP4 had more than one ancestral background, defined asan admixture, and these clusters included accessions fromthe YGC, NC, NWC, NEC, and MLCJ populations.

The highly significant association (Table 6, χ2097.65≥χ0.01,

202037.57) between the model-based clusters and the geograph-

ical populations was found after analyzing the relativity of allaccessions cluster, revealing that the model-based cluster has ageographical foundation. So, it will be more precise to use themodel-based cluster parameter as a covariant in analyzing asso-ciation than using the dendrogram based on genetic similaritiesparameter.

LD

An analysis of the extent and evolution of LD was a foun-dation for detecting the true associations within the mappingpopulation. LD extent of was assessed among all 1,378 pairsof the SSRs loci for all accessions. Across all accessions, as

Table 5 Pair-wise estimates of FST and Nei’s 1972 unbiased geneticdistances based on 53 SSR loci among the six geographicalpopulations

Population NWC YGC NEC NC MLCJ SC

NWC – 0.0967 0.1773 0.0693 0.0907 0.3759

YGC 0.0698 – 0.2160 0.1011 0.1107 0.3750

NEC 0.1253 0.1557 – 0.1153 0.1586 0.3948

NC 0.0459 0.0689 0.0864 – 0.0546 0.2875

MLCJ 0.0539 0.0771 0.1121 0.0342 – 0.2667

SC 0.2176 0.2407 0.2558 0.1827 0.1828 –

Nei’s 1972 unbiased genetic distance estimates appear above the diagonal,and pair-wise FST appears below the diagonal

NWC northwest China, YGC the YunGui plateau, NEC northeast China,NC northern China,MLCJ the middle and lower reaches of the ChangjiangRiver, SC southern China

Fig. 2 Unrooted neighbor-joining trees of the six geographical pop-ulations based on Nei’s genetic distances

0

50

100

150

200

250

300

2 3 4 5 6 7

K

ΔK

Fig. 3 Values of ΔK, with the modal values used to detect the true Kof five groups (K05)

Tree Genetics & Genomes (2012) 8:975–990 981

many as 249 (18.07%) of the total marker pairs were in LD(based on r2, P<0.05) after Bonferroni correction. Thenumber of LD for the marker pairs from the different chro-mosomes was 214, higher than the markers (35) on the samechromosomes.

At the whole population level, the r2 values amongall the SSR pairs ranged from 0.001 to 0.7525. Themean r2 value for all intrachromosomal loci pairs was0.0250, and the r2 values for the interchromosomal pairsranged from 0 to 0.2017, with an average of 0.0149. At

Fig. 4 Diversity analysis and population structure of 104 peach land-races using 53 SSRs loci. a Dendrogram based on genetic distance. bThe geographic origin of landraces: dark blue NEC population, lightblue YGC population, dark red NEC population, light red NC

population, dark green MLCJ population, and light green SC popula-tion. c Population stratification for K05 (each color represents adifferent subpopulation)

982 Tree Genetics & Genomes (2012) 8:975–990

the highly significant threshold of r2>0.1, 0.8% (11) ofthe marker pairs remained in LD, and the loci pairsmostly distributed among different chromosomes. Someunlinked SSR markers on different chromosomes hadhigher r2 values. For example, the r2 values were0.2018 (CPSCT022 on chromosome 5 and CPPCT005on chromosome 4), 0.2004 (BPPCT014 on chromosome5 and CPPCT005 on chromosome 4), 0.1786 (pchcms2on chromosome 7 and pchgms2 on chromosome 4), and0.1174 (BPPCT029 on chromosome 7 and BPPCT037on chromosome 5). Figure 5 showed the distribution of the r2

values of the interchromosomal pairs for the whole popula-tion. About 95.51% of the r2 values were below 0.05, and3.43% ranged from 0.05 to 0.10.

LD decay was investigated using the r2 (P<0.1) values ofthe SSRs pairs in the same chromosomes of the three maingeographic populations. As shown in Fig. 6, in NC popula-tion, the LD decays with distance, whereas in NWC andMLCJ populations, high levels of LD extend were not foundbetween pairs of SSRs with near linkage distance. And inwhole samples, the intrachromosomal LD was very commonfor distances of 40 cM. Occasionally, LD occurred betweenSSR loci that were further apart, and LD extended to 70 cM inmost cases. Data point distributions in the plot of LD (r2)decay against genetic distance (in centimorgans) within theeight chromosomes, followed by the equation y0−0.0579 ln(x)+0.2038, showed that LD was not a simple monotonicfunction of the distance between markers. The r2 betweenthe linked markers was mainly <0.1. LD extent for a particularregion can be estimated from an LD decay plot generatedusing the dataset obtained from a region of interest. Whensuch an LD decay plot is generated, the usual practice is tolook for the distance point where the LD value (r2) decreasesbelow 0.1 or for the half strength of D′ (D′00.5) based on thecurve of the nonlinear logarithmic trend line (Ibrokhim andAbdusattor 2008). In the present study, the LD across all thepopulations decayed below the critical r2 value of 0.1 within6.01 cM.

Association mapping based on structured population

An association map (P<0.01) of SSR loci with fruitquality and phenological period traits from the peachlandraces accessions was constructed. A GLM modelimplemented in TASSEL was used for the mapping.Out of the 53 polymorphic SSR markers used for theassociation mapping, 9 (17.0%) were associated withflowering time in 2007, 10 (18.9%) associated with that

Table 6 Associations among the dendrogram based on genetic similarities, the model-based clusters, and the geographical populations in peachlandraces

Geographicalpopulations

Total Dendrogram based on genetic similarities χ2 Model-based clusters χ2

G1 G2 G3 G4 G5 POP1 POP2 POP3 POP4 POP5

NWC 35 7 9 1 1 17 χ2078.63 3 1 11 0 20 χ2097.65

YGC 11 5 1 1 1 3 P<0.01 7 3 0 0 1 P<0.01

NEC 6 1 0 5 0 0 χ0.01, 202037.57 0 1 4 1 0 χ0.01, 20

2037.57NC 30 13 3 13 1 0 8 10 6 6 0

MLCJ 20 8 2 2 7 1 1 5 3 11 0

SC 2 2 0 0 0 0 0 0 1 1 0

Total 104 36 15 22 10 21 19 20 25 19 21

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.05 0.15 0.25 0.35 0.45

Fre

qu

ency

3.430.25 0.08 0.16

95.51

0

20

40

60

80

100

0-0.05 0.05-0.10 0.10-0.15 0.15-0.20 0.20-0.25

r 2

r 2

Freq

uenc

y (%

)

Fig. 5 Distribution of linkage disequilibrium r2 values for interchro-mosomal SSR pairs

Tree Genetics & Genomes (2012) 8:975–990 983

in 2008, and 8 (15.1%) were associated with chillingrequirement traits in 2007. Only five association markers,which had highest r2 values, were selected in the present study(Table 7).

The four SSR markers were significantly associated withthe red pigment in the flesh explained between 8.1 and14.5% phenotypic variations. One marker, CPPCT018 onchromosome 3 was associated with flesh color around thestone, explained 14.6% phenotypic variations. The genewhich controlled the trait was Cs, which was locatedon chromosome 3 previously (Yamamoto et al. 2001).BPPCT023 on chromosome 4 and BPPCT028 on chro-mosome 1 were associated with the flesh adhesion trait,and the corresponding gene, F, reported by Yamamotoet al. (2001) was located on chromosome 4. Two markerswere associated with flesh texture traits, and five with chillingrequirement. Six, 5, 10, 4, and 3 markers were associated withflesh weight, flesh firmness without skin, flowering time,ripening time, and fruit development period in 2007 and2008, respectively.

We also found that some loci were associated simulta-neously with two or more traits. For example, CPDCT045on chromosome 4 was associated with three traits (redpigment in flesh, ripening time, and fruit development peri-od), and BPPCT008 on chromosome 6 was associated withthree traits (red pigment in flesh, fruit weight, and floweringtime). Furthermore, nine markers were associated with twotraits. The details of the association mapping results for eachtrait are listed in Table 7.

Discussion

Genetic diversity in peach germplasm

The narrow genetic base of modern crop cultivars is aserious obstacle in sustaining and improving crop produc-tivity. The reason for this is the rapid vulnerability of genet-ically uniform cultivars with potentially new biotic andabiotic stresses (Esbroeck et al. 1999). The efficient exploi-tation of the genetic diversities among these plant germ-plasm resources in their origin centers is vital. Thisprocess helps overcome problems associated with the nar-rowness of the modern cultivar genetic base. However,identifying the genetic variants underlying quantitative croptrait variations is a challenging task for plant breeders.Linkage mapping, and more recently, association or LDmapping, have been applied to elucidate the genetic basesof natural variation in important quantitative traits.

Germplasm choice is critical to the success of associationmapping. The group of individuals should be selected froma natural population or germplasm collection with a widegenetic diversity. Peach landraces have evolved from theirwild progenitors by natural and human selection, leading tothe maintenance of high genetic diversity. In the currentstudy, a set of 104 landraces from the National ClonalGermplasm Repository of Peach Center (Zhengzhou) thatconsisted of six ecotypes was used. The abundant allelicvariation per locus is 6.4. This amount is similar to aprevious report of 6.36 alleles per locus (Aranzana et al.

Fig. 6 Pattern of linkagedisequilibrium indicating thecorrelation of allele frequency(r2, P<0.1) values againstgenetic distance (incentimorgans) between all locipairs in the same chromosomein NWC, NC, and MLCJpopulations and whole samples

984 Tree Genetics & Genomes (2012) 8:975–990

Table 7 Associations between the microsatellite and the 10 quantitative traits in peach landraces' trait

SSR markera Chromosome Map positionfrom the top (cM)

r2 (%)b P value Known loci and gene

Flesh color around the stone CPPCT018 3 18 14.6 0.0039 Cs (Yamamoto et al. 2001)

Red pigment in flesh CPDCT045 4 16.8 14.5 4.3×10−6

BPPCT008 6 30.1 14.1 0.0029

CPSCT022 5 40.7 8.3 6.8×10−4

CPDCT025 3 36.4 8.1 0.0037 MYB10 (Wang et al. 2010)

Flesh texture UDP96-013 2 27.8 13.1 0.0029BPPCT028 1 77.4 12.8 0.0011

Flesh adhesion BPPCT023 4 47.4 21.2 2.1×10−6 F (Yamamoto et al. 2001);endoPG (Peace et al. 2005)

BPPCT028 1 77.4 12.5 6.9×10−4

Flesh firmness without skin UDP97-401.2007 5 11 22.8 0.0041

UDP98-411.2007 2 27.8 20.6 0.0085 PME1 (Illa et al. 2010)

CPPCT026.2008 1 33.9 23.4 6.1×10−4 PME7 (Illa et al. 2010)

UDP98-412.2008 6 72 15.6 0.0089BPPCT029.2008 7 29.6 14.1 0.0065

Fruit weightc UDP97-401.2007 5 11 24.3 7.7×10−4 QTL (Abbott et al. 1997)

BPPCT007.2007 3 11.2 21.3 0.0024 QTL (Abbott et al. 1997)

CPPCT008.2007 6 8.7 20.4 0.001UDP96-019.2007 8 20.8 17.4 0.0031

CPPCT006.2007 8 24.8 14.9 0.0078

BPPCT008.2008 6 30.1 23.0 0.0052

Chilling requirementc CPPCT005 4 10.4 25.1 8.9×10−5

BPPCT014 5 44 20.9 6.5×10−4

UDP98-407 6 49.7d 20.6 0.0053

pchcms2 7 51.4 18.4 5.5×10−4

CPPCT015 6 35.8 17.8 0.0068

Flowering timec UDP98-407.2007 6 49.7d 49.9 1.7×10−10

CPPCT005.2007 4 10.4 49.9 6.1×10−12 QTL (Fan et al. 2010);QTL (Etienne et al. 2002)

BPPCT014.2007 5 44 49.8 6.7×10−12

pchcms2.2007 7 51.4 41.4 4.4×10−10

BPPCT025.2007 6 56.4 30.1 2.4×10−5

CPPCT015.2008 6 35.8 21.9 4.6×10−6

CPPCT022.2008 7 18.6 18.0 5.9×10−4

CPDCT044.2008 2 12.5 17.0 1.6×10−4

BPPCT008.2008 6 30.1 15.6 0.0025

UDP96-013.2008 2 27.8 15.5 1.4×10−4 QTL (Fan et al. 2010)

Ripening time BPPCT017.2007 5 20.1 32.1 3.4×10−5

UDP98-411.2007 2 27.8 22.8 0.0015 QTL (Verde et al. 2002)

pceGA34.2007 2 43.9 19.2 0.0029

CPDCT045.2007 4 16.8 10.9 0.003 QTL (Etienne et al. 2002)

UDP98-411.2008 2 27.8 11.0 0.0062Fruit development period pceGA34.2007 2 43.9 16.2 0.0065

UDP98-411.2007 2 27.8 16.1 0.0067

CPDCT045.2007 4 16.8 12.9 7.4×10−4 QTL (Etienne et al. 2002)

UDP98-411.2008 2 27.8 10.8 0.0043

a Only SSR markers with a significant marker-trait association are reported (P<0.01)b r2 indicates the percentage of the total variation explainedc Five association markers that had the highest r2 values were selectedd Loci position in the G×N linkage map (Dirlewanger et al. 2004b)

Tree Genetics & Genomes (2012) 8:975–990 985

2010) using peaches selected from Europe and America, andeven exceeded another report of 2.85 alleles per locus (Xieet al. 2010) using improved varieties from Fenghua (China)local accessions. These results may be an indication of ahigh level of genetic diversity among the Chinese landraces.

Few studies on peach landraces of different geographicalpopulations exist. Cheng and Huang (2009) reported anaverage I value of 0.319 (ranging from 0.087 to 0.520) in32 cultivars and landraces from China. Xie et al. (2010)evaluated the genetic diversity and identity of 94 accessions,including Fenghua local accessions and introduced culti-vars. The average PIC value was 0.34 for the Fenghuacultivars and was 0.46 for the introduced cultivars. TheFenghua accessions were also less heterozygous than theintroduced ones (Ho00.46 and 0.50, respectively). Theseresults suggested that the honey peach cultivars collectedin the Fenghua area had a lower level of genetic diversitythan the 46 introduced ones. The results of the present studyshowed that the PIC values of the studied geographicalpopulations varied from 0.2264 to 0.5202. The NC and SCpopulations showed the highest and lowest values, respec-tively. The SC population had the highest I (0.8730), where-as the NC population had the lowest value (0.4655).Compared with a previous study, a higher genetic diversitywas shown in the present study based on the higher I andPIC values because more landraces were used. Highergenetic diversities were found among the NC and MLCJpopulations than among the NWC and YGC populations.Among the peach geographical populations in China, NCand MLCJ attracted much attention given more landracesand very widely divergent phenotypes and genotypes. Thismay at least be helpful in selecting high differentiationparents for crossbreeding. This is the first report on thediversity of peach landraces selected from different geo-graphical populations. The higher genetic diversity in ourreport indicated that huge phenotypic variations increaseddetection power and allowed the quantification of moreallelic effects. The results showed that these alleles are moresuitable for association mapping.

The region with the highest genetic diversity is generallyconsidered as the center of origin of a species (Vavilov1951). Among the six populations, NC and MLCJ in centralChina, where the climate was suitable for peach cultivationand had the highest genetic diversity, are hence presumed asthe landrace origin. However, Wang and Zhuang (2001)believed that the NWC and YGC peach populations, whichdifferentiated from another wild species of peach, Prunusmira, were the landrace center of origin. The high geneticdiversity within NC and MLCJ may be the result of severalfactors, including larger area and higher phenotype diversi-ty. For example, more red flesh peach (Tian Jin Shui Mi, HeiBu Dai, Da Guo Hei Tao, and Wu Hei Ji Rou Tao) andornamental peach (Ren Mian Tao, Wu Bao Tao, Jiang Tao,

Hong Chui Zhi, and Sa Hong Tao) are found in NC com-pared with NWC. Therefore, our results indicated that newcharacters will be formed in plant evaluation.

Population structure and LD in peach germplasm

Understanding the population structure is important to avoididentifying spurious associations between phenotypes andgenotypes in association mapping (Pritchard et al. 2000).The genetic structure of peach had previously been ana-lyzed. Aranzana et al. (2010) detected six major groupswithin a set of 50 SSRs in 224 peach varieties, but thisanalysis used improved commercial accessions from Spainand the USA. Using the Structure software with K05, the104 peach landraces from China were significantly differ-entiated into five groups. Moreover, several accessions hadpartial ancestry in more than one background. For example,POP5 contained 21 accessions, and 20 of them originatedfrom the NWC region. However, one accession, Bai NianHu, belonged to the YGC population and was designated inPOP5. This accession probably had a complex breedinghistory that involved intercrossing and introgression amonggermplasms from diverse backgrounds (Mather et al. 2004).Model-based analyses of population structures may be help-ful in providing information for association mappinganalyses.

The number of markers needed for genome-wide LDscanning depends on the LD level. This requirement isbased on a preliminary estimate of 420–440 kb/cM for thecorrespondence between genetic and physical distances inpeach (http://www.rosaceae.org/peach/genome). In the pres-ent study, r2 values decreased to 0.1 within 6.01 cM, ap-proximately 2,524–2,644 kb. Thus, peach LD extendedfarther than grape (Barnaud et al. 2006) and oat (Newell etal. 2011) LDs, where the r2 values reached 0.1 within1,300–2,160 kb and 2.5 cM, respectively. Nonetheless, thisLD value is smaller than that of cotton (Abdurakhmonov etal. 2008) and durum wheat (Maccaferri et al. 2005), whichhad LD values approximately 10 cM and larger than30–40 cM at r200.1. Hence, peach LD extent, as measuredby the r2 decline, seems to be moderate compared with otherspecies. The moderate LD blocks in peach suggested thepotential for conducting an effective LD mapping of com-plex traits with a fewer number of markers than that requiredfor the grape genome. Considering that the peach genomehas a total recombinational length of approximately 520 cM(Dirlewanger et al. 2004a) and the LD block distance of6.01 cM such a mapping would require about ≥86 poly-morphic markers distributed uniformly across the genomefor a minimum coverage of LD blocks in the genome ofvarious germplasms.

LD level may vary across genomes because of differentrecombination rates, selective pressures, mating systems

986 Tree Genetics & Genomes (2012) 8:975–990

(selfing vs. out-crossing), effective population sizes, and soon. Several reports suggested a longer size of LD blocks inother narrow-based germplasm crop groups than in broad-based germplasm groups (Remington et al. 2001; Gupta etal. 2005). As a selfing species, peach landraces are supposedto have a higher LD level than improved varieties because offew past recombination events. However, in the presentstudy, the LD across the whole population of peach land-races decayed below the critical r2 value of 0.1 within6.01 cM, which was less than that in the improved peachvariety (Aranzana et al. 2010). The r2 values larger than 0.1therein were maintained to up to 13.3 cM (M peach) and15.2 cM (NM peach). These results are the same for cotton,in which the genome-wide LD block size averages at ther2≥0.1 threshold were less than 10 cM in the landracegermplasm, and more than 30 cM in the improved varietygermplasm (Abdurakhmonov et al. 2008). However, ourestimate of genome-wide averages for LD extent inferredby the SSR markers in peach was larger than those inferredby the single nucleotide polymorphism reported in Arabi-dopsis (25–250 kb; Nordborg et al. 2002) and maize (200–400 bp; Tenaillon et al. 2001). Consequently, LD patterns inthe specific regions or population groups in peach may notbe adequately reflected. Additional LD quantification ineach targeted region or population group is required for aneffective association mapping (Abdurakhmonov et al.2008).

Association mapping in peach

Prunus fruit development, growth, ripening, and senes-cence all include major biochemical and sensory changesin texture, color, and flavor. The genetic dissection ofthese complex processes has important applications incrop improvement.

A structure-based association mapping was performedfor peach fruit and phenological period traits using agenome-wide association map based on haplotypic data. Atotal of 27 marker-trait associations were selected to beanalyzed using 53 different SSR markers (Table 7). Thesetrait-associated SSR markers from our study were comparedwith other reported SSR markers from QTL mapping anal-yses of various experimental populations. Among theseassociations, some were in regions where QTLs associatedwith the given trait had previously been identified. However,some associations were not consistent with the results of otherpublished linkage maps because different marker systemswere used (Abdurakhmonov et al. 2008).

Considering that the genome parallels with referencegenetic map, every linkage groups in the genetic map indi-cate the corresponding chromosomes. In the present study,one SSR marker, CPPCT018 (18 cM on the G3 of T×E map)on chromosome 3 (Yamamoto et al. 2001) was associatedwith

the flesh color around the stone, also associated with Cs (17–18 cM on G3 of T×E map) gene, reported priviously by QTLmapping. Four SSR markers associated with the red pigmentin fruit flesh were distributed in chromosomes 3, 4, 5, and 6.Because the expression of MYB10 genes correlated with fruitanthocyanin levels (Wang et al. 2010), hence, MYB10 wasidentified as the candidate gene for the red pigment in theflesh. After blasting with the peach genome database (http://www.rosaceae.org/peach/genome), the MYB10 (GenBankaccession: EU155160) was found to be located in about29.4 cM on chromosome 3. So, CPDCT025 (36.4 cM on theG3 of T×E map) may be an association locus around theMYB10 gene in the present study.

The M and the NM traits of peach are two oppositephenotypes of flesh texture (Bailey and French 1932). TheM texture shows a prominent softening in the last stage ofripening before a complete melting. The lack of softening inthe NM phenotype is related to the loss of endopolygalac-turonase (endoPGase) activity, the enzyme responsible forcleaving pectins (polygalacturonic acid chains) from the cellwall in the M fruits (Lester et al. 1996). Moreover, the fleshadhesion to stone, according to Bailey and French (1932) iscontrolled by the “freestone” locus, where the freestone (F)allele is dominant over the clingstone (ff). Bailey and French(1932) suggested that the flesh adheres to the stone and theflesh texture gene is linked on the same chromosome. Fromrecent studies on progenies segregated for endocarp adher-ence and flesh texture (M and NM), four alleles for theendoPGase enzyme were found responsible for thethree flesh phenotypes: freestone and clingstone-M andclingstone-NM endoPGase were mapped to the linkagegroup G4, as previously reported (Peace et al. 2005). Sub-sequently, the endoPGase gene of peach was cloned(Morgutti et al. 2006, GenBank accession: DQ659241)and was located in chromosome 4 blasted with peach ge-nome. In the present study, three SSR markers in chromo-somes 1, 2, and 4 were associated with flesh texture andflesh adhesion to stone traits. Among them, BPPCT023(physical location: 14731772 on chromosome 4) associatedwith endoPGase (DQ659241, physical location: 22684623–22686568 on chromosome 4) was found in the same regionhosting F. F is the major gene controlling flesh texture andflesh adhesion to the stone. BPPCT028 associated with fleshtexture on chromosome 1 explained 12.8% phenotype var-iation and also associated with the flesh color around thestone. This finding indicated that a common factor influenc-ing the two traits exists.

Peaches are characterized by a rapid loss of firmness atthe end of the ripening process. Cell wall changes areparticularly important in this phenomenon. Such cell wallchanges include the dismantling of its structure, the degra-dation of the polymers of which it is composed, and the lossof turgor pressure in the fruit (Brummell 2006). These

Tree Genetics & Genomes (2012) 8:975–990 987

changes correlated with the concerted action of two pro-teins, endoPGases and pectin methylesterases (PMEs).PME isoforms detected in cell walls are encoded by a multi-gene family. The main PME function is polyuronide deme-thylation so that polyuronide can be degraded byendoPGase. Recently, Murayama et al. (2009) identifiedPpPME2 upregulation by PpPG2 during peach fruit soften-ing. The endoPGase was located on chromosome 4, asdescribed previously, but PME was located in a differentlinkage group in different papers. Ogundiwin et al. (2009)located two PME genes, PME1 on G1 (approximately50 cM on G1 of the T×E map) and PME5 on G7 (approx-imately 56 cM on G7 of the T×E map). Soon after, Illa et al.(2010) located PME1 on G2 (45 cM) and PME7 on G1(50 cM). Our results showed that five markers associatedwith flesh firmness were distributed in chromosomes 1, 2, 5,6, and 7. Among these markers, CPPCT026.2008 which islocated in chromosome 1 explained the higher phenotypevariation and indicated that the PME gene maybe wasessential to fruit firmness.

Fruit weight is a complex trait that follows a quantitativeinheritance depending on fruit shape and development peri-od. Dirlewanger et al. (1999) considered that near the Sgene, which is a dominant gene controlling fruit shape(i.e., flat or round). This finding is in agreement with thefact that flat fruits are generally lower in weight than roundfruits. Based on QTLmapping, Abbott et al. detected the QTLsof fruit weight in groups 3 and 5 of peach. Dirlewanger et al.(1999) and Etienne et al. (2002) detected these QTLs in group6. CPPCT008.2007 and BPPCT008.2008 were found in chro-mosome 6 associated with fruit weight. BPPCT007.2007 andUDP97-401.2007 were found in chromosomes 3 and 5, re-spectively. These results were similar with that of Abbott et al.(1997).

Chilling requirement refers to the duration of low temper-atures required for the release of temperate trees from endo-dormancy. Fan et al. (2010) constructed a linkage map usedby a peach F2 population of 378 genotypes developed fromtwo genotypes with contrasting chilling requirements forQTL mapping. The study detected five QTLs of chillingrequirement. Among these QTLs, qCR1a and qCR7 showedvery prominent effects and were declared to be the majorQTLs. qCR1a explained 40.5–44.8% of the phenotypicvariance, and qCR7 explained 17.8–24.9%. In comparison,the T×E map showed that qCR1a was located in G1 atapproximately 70 cM, and qCR7 in G7 was at approxi-mately 38 cM. The two QTLs not only facilitate marker-assisted breeding for low chill cultivars, but also pave theway for future fine mapping and map-based cloning ofgenes controlling the chilling requirement. Especially, themajor QTL (qCR1a) spans only 2 cM, which overlaps withthe peach EVG region. The “evergreen” trait, controlled by theEVG gene, has been described as a trait that did not have a

terminal growth (i.e., no terminal bud formation) unless killedby frost (Rodriguez et al. 1994). The EVG gene is located inG1 (Wang et al. 2002). Table 7 shows that the most highlysignificant association found was CPPCT005 on chromosome4, which explained 25.1% of the phenotypic variation. Andanother association, pchcms2 may be associated with qCR7(Fan et al. 2010). However, no marker-trait association wasfound in region around that qCR1a and EVG gene.

Flowering time depends on the chilling requirement nec-essary to fulfill rest and on the growing degree hour accu-mulation to reach full bloom. QTL mapping results forflowering times in various genomic regions in Prunus havebeen reported. Using the T×E Prunus reference map oflinkage groups, four QTLs on G1, G4, G6, and G7 weredetected by Joobeur (1998) in an almond×peach F2 popu-lation. Two QTLs on G2 and G7 were found by Dirlewangeret al. (1999) in a peach F2 population. One QTL on G4 wasdetermined by Verde et al. (2002) in a peach backcross(BC1) population. Fan et al. (2010) detected four QTLs inchromosomes 1, 2, 4, and 7. Among these QTLs, qBD1a(G1, 70 cM) and qBD7a (G7, 35 cM) had very prominenteffects. In conclusion, several QTLs were detected in chro-mosomes 1, 2, 4, 6, and 7. In the present study, 10 QTLswere found in chromosomes 2, 4, 5, 6, and 7, and thelocation of trait-marker associations in chromosomes 2, 4,and 7 was close to the results reported by Fan et al. (2010).Furthermore, five markers associated with chilling require-ment were also found be associated with flowering time.These results showed that the same genes may affect twotraits in Peach.

In the current study, four SSR markers were associatedwith ripening date and were mainly distributed on chromo-somes 2, 4, and 5. The location of some QTLs is similarwith previous results (Verde et al. 2002; Etienne et al. 2002).In 2008, only one marker, UDP98-411, was associated withripening time. It was also found in 2007, which exhibits agood repetitiveness. No relationship was found betweenflowering and ripening times, same results as described byLayne and Bassi (2008).

Fruit development is regulated by complex interactionsbetween physiological and environmental factors. Few stud-ies on the QTLs of fruit development period exist. Threemarkers on chromosomes 2 and 4 which contribute for fruitdevelopment period were found in the current study.Because fruit development period was calculated from flow-ering and ripening time, same markers were found in asso-ciation with the traits of fruit development period andripening time.

Identifying the genetic variants that underlie complextraits was very essential to plant genetics. Two mainapproaches are available for mapping the relevant genesand identifying the variants associated with these complextraits: linkage mapping in families and population-based

988 Tree Genetics & Genomes (2012) 8:975–990

genetic association studies (Agrama et al. 2007). In theory,genetic association mapping is more powerful than linkagestudies in identifying variants with weak effects that maycontribute risks to common complex traits (Risch andMerikangas 1996). As an example, except for BPPCT023linked with F gene, another marker, BPPCT028, which wasnot reported before showed a weaker effect associated withflesh adhesion. Our results have shown that LD studies areefficient means of dissecting complex agronomic characters.However, further studies are necessary to confirm the asso-ciation results in specific biparental crossing populationsand consequently avoid identifying spurious associations.

Acknowledgments We thank Maria Jose Aranzana Civit, IRTA(Spain) for useful suggestions during manuscript editing. We alsoacknowledge the financial support of the Fundamental Research Fundof the Central Institute of the Chinese Academy of Agricultural Sciences(0032011017) and Modern Agro-industry Technology Research System(nycytx-31-1-2).

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