Molecular diversity and association mapping of quantitativetraits in Tibetan wild and worldwide originated barley(Hordeum vulgare L.) germplasm

Dongfa Sun • Wenbin Ren • Genlou Sun •

Junhua Peng

Received: 29 May 2010 / Accepted: 14 September 2010 / Published online: 28 September 2010

� Springer Science+Business Media B.V. 2010

Abstract Molecular diversity of 40 accessions of

Tibetan wild barley (TB), 10 Syrian (SY), 72 North

American (NA), 36 European (EU), 9 South American

(SA) and 8 Australian (AU) varieties were character-

ized using multiple microsatellite loci. The 42 SSR

primers amplified 278 alleles across the 175 barley

accessions tested in the present study. The average

gene diversity for the whole sample was 0.3387

whereas the mean value for the each population was as

follows: TB = 0.3286, SY = 0.2474, EU = 0.299,

AU = 0.2867, NA = 0.3138, SA = 0.2536. Cluster-

ing analysis based on Nei’s original genetic distance

showed that the EU and NA barley populations were

grouped together. The TB population was well sepa-

rated from the other 5 barley populations. Associations

between microsatellite markers and 14 quantitative

traits were also investigated. Significant associations

were found for 18 microsatellite marker loci. The

number of marker loci associated with each trait

ranged from one (stem diameter, filled grains per plant,

grain weight per plant, length of main spike and awn

length) to seven (plant height). The percentage of the

total variation explained by each marker ranged from

4.59% (HVM2 associated with plant height) to 17.48%

(Bmac90 associated with density of main spike). This

study provides candidate markers for further QTL

mapping of these traits and for marker-assisted

selection.

Keywords Barley germplasm � Tibetan wild barley �Microsatellite � Molecular diversity �Association mapping

Introduction

Barley is one of the main grain crops, and also

ranking the fourth staples in the world, and is used for

animal feed, brewing malts, and human consumption

(Hayes et al. 2003). Barley can be grown in a wide

range of environments such as the Arctic, high

mountains and desert oases (Harlan 1976). It was

suggested that worldwide cultivated barley originated

from either the Fertile Crescent (Ancient Egypt, the

Levant and Mesopotamia) (Harlan 1976), or the

Qinghai-Tibet plateau region in China (Vavilov 1926;

Brucher and Aberg 1950; Feng et al. 2006).

The amount of genetic variation in germplasm and

genetic relationships between genotypes are critical

information for effective conservation and utilization

D. Sun (&) � W. Ren � G. Sun

College of Plant Science and Technology, Huazhong

Agricultural University, Wuhan 430070, Hubei, China

e-mail: sundongfa1@mail.hzau.edu.cn

G. Sun

Biology Department, Saint Mary’s University, Halifax,

NS B3H 3C3, Canada

J. Peng

Wuhan Botanical Garden/Institute, Chinese Academy

of Sciences, Wuhan 430074, Hubei, China

123

Euphytica (2011) 178:31–43

DOI 10.1007/s10681-010-0260-6

of genetic resources (Kresovich et al. 1995). Knowl-

edge on the amount of genetic variation present, and

the location of the genetic determinants of diversity

may be useful for discovering new genes (Jana 1999;

Matus and Hayes 2002). Assessment of genetic

diversity in barley germplasm and their genetic

relationships will provide useful information for

breeding programs. The degree of genetic diversity

in germplasm can be characterized by using mor-

phology, pedigree and molecular techniques. Molec-

ular techniques are currently the most feasible

strategy for characterizing diversity in wild and

cultivated germplasm (Matus and Hayes 2002).

Genetic diversity of wild and cultivated barley have

been characterized using RFLPs (Graner et al. 1990;

Petersen et al. 1994; Russell et al. 1997), RAPDs

(Dawson et al. 1993; Frank and Ana 2001), AFLPs

(Turpeinen et al. 2003), and SSRs (Saghai-Maroof

et al. 1994; Struss and Plieske 1998; Matus and Hayes

2002; Feng et al. 2006). It has been demonstrated that

SSR markers possess a number of advantages over

other types of molecular markers, such as codomi-

nance, highly polymorphisms, locus specificity, good

reproducibility, and random distribution throughout

the genome. In recent years, a large number of SSR

markers in barley have been developed and exten-

sively utilized in barley genomic mapping and marker-

assisted breeding (Liu et al. 1996; Ramsay et al. 2000;

Varshney et al. 2007; Williams et al. 2008).

Conventional barley linkage mapping has been

conducted using experimental populations derived

from a bi-parental cross. Many quantitative trait loci

(QTL) have been reported. However, the effects of

these QTL often turn out to be unique to a specific

genetic background which has limited their applica-

tion in breeding programs (Achleitner et al. 2008).

Association mapping strategy seems to be a promising

approach to overcome the limitations of conventional

QTL mapping and has received increased attention of

plant geneticists during the last few years (Kraakman

et al. 2004; Gupta et al. 2005; Breseghello and Sorrells

2006; Peng et al. 2009; Stracke et al. 2009). The

principal of association mapping (AM) is based on

linkage disequilibrium (LD) and to utilize historic

patterns of recombination that have occurred within a

sample of individuals to detect correlations between

genotypes and phenotypes within these individuals

(Zondervan and Cardon 2004). The natural decay rate

of LD with distance occurs in inbreeding species at a

considerably slower than in outbreeding species

because effective recombination in inbreeding species

is severely reduced and genetic polymorphisms remain

correlated over longer physical distances (Nordborg

et al. 2002; Caldwell et al. 2006). Association between

traits and markers across germplasm collections were

established in oat, rice, maize, wheat and barley

(Kraakman et al. 2004; Peng et al. 2009). In barley,

Igartua et al. (1999) detected marker-trait associations

for heading date in 32 cultivars. Ivandic et al. (2003)

found association between markers and the traits of

water-stress tolerance (Chromosome 4H) and powdery

mildew resistance in 52 wild barley lines. Using 236

AFLP markers and a collection of 146 modern two-

row spring barley cultivars, the markers associated

with yield and yield stability were identified (Kraak-

man et al. 2004). Barley is a inbreeding species, its

genome contains extensive blocks of chromatin in LD

(Malysheva-Otta et al. 2006), and thus is suitable for

association mapping. Association mapping has so far

been applied in the cultivated population, rarely used

in the heterogeneous populations mixing cultivated

and wild ancestors.

The objectives of this study were to analyze the

genetic diversity and genetic relationships among

Tibetan wild and barley germplasm of worldwide

origin, and to test preliminarily genetic associations

between SSR markers and morphological and agro-

nomical traits using heterogeneous population with

cultivated and wild barley. The results of this study

will help utilize, conserve and manage the barley

germplasm effectively.

Materials and methods

Plant materials and phenotyping

One hundred and seventy-five barley accessions of

worldwide origin were used for this study. Among the

175 accession, 40 originated from Chinese Tibet, 10

from Syria, 72 from North America, 36 from different

parts of Europe, 9 from South America, and 8 from

Australia. The names, countries of origin and row

numbers of the accessions are listed in Table 1.

All accessions were evaluated for agronomic and

morphological traits in replicated field experiments.

Briefly, the barley accessions were sown in three

replications in the experimental field of Huazhong

32 Euphytica (2011) 178:31–43

123

Table 1 The code, name, origin and row type of 175 barley accessions used in this study

Code Name Origin Row Code Name Origin Row

L35 H01 Tibet 2 0121 Anerle USA 2

L36 H03 Tibet 2 0122 Clark USA 2

L37 H04 Tibet 6 0123 Wal8892-78 USA 2

L38 H04a Tibet 2 0124 Wal4583-77 USA 2

L39 H09 Tibet 6 0125 Gus USA 6

L40 H010 Tibet 6 0126 Columbia USA 6

L41 H011 Tibet 6 0127 W84-145 USA 6

L42 H017 Tibet 6 0128 W84-144 USA 6

L43 H018 Tibet 2 0129 W84-143 USA 2

L44 H022 Tibet 2 0130 W84-142 USA 6

L45 H029 Tibet 2 0131 W84-141 USA 6

L46 H032 Tibet 2 0132 W84-140 USA 2

L47 H033 Tibet 2 0133 W84-138 USA 2

L48 H052 Tibet 2 0134 W84-137 USA 2

L49 H079 Tibet 6 0135 W84-136 USA 6

L50 H094 Tibet 2 0136 W84-134 USA 2

L51 H0230 Tibet 2 0137 Clipper/volla Syria 2

L52 H0246 Tibet 6 0138 W84-146 USA 6

L53 H0334 Tibet 2 0139 W84-147 USA 6

L54 H0544 Tibet 2 0140 W84-150 USA 2

L55 H0545 Tibet 2 0141 W84-151 USA 6

L56 H0546 Tibet 2 0142 W84-152 USA 6

L57 H0547 Tibet 2 0143 W84-155 USA 6

L58 H0548 Tibet 2 0144 W84-156 USA 6

L59 H0551 Tibet 2 0145 W84-158 USA 2

L60 H0551 Tibet 2 0146 W84-159 USA 6

L61 H0552 Tibet 2 0147 W84-160 USA 6

L62 H0564 Tibet 2 0148 W84-161 USA 6

L63 H0567 Tibet 2 0149 W84-163 USA 6

L64 H0569 Tibet 2 0150 W84-164 USA 6

L65 H0590 Tibet 2 0151 W84-165 USA 6

L66 H0609 Tibet 2 0152 W84-166 USA 2

L67 H0609 Tibet 2 0153 W84-184 USA 2

L68 H0640 Tibet 6 0154 W84-106 USA 6

L69 H0641 Tibet 2 0155 W84-108 USA 2

L70 H0642 Tibet 2 0156 W84-109 USA 6

L71 H0648 Tibet 2 0157 W84-110 USA 6

L72 H0650 Tibet 2 0158 W84-112 USA 6

L73 H0651 Tibet 2 0159 W84-113 USA 2

L74 Yeshengerlenghei Tibet 2 0160 W84-114 USA 2

077 Monker USA 6 0161 W84-115 USA 2

078 Mute USA 6 0162 W84-130 USA 6

079 Bananza USA 6 0163 W84-129 USA 2

080 Dalt Canada 6 0164 W84-128 USA 6

Euphytica (2011) 178:31–43 33

123

Table 1 continued

Code Name Origin Row Code Name Origin Row

081 Ho208 Canada 2 0165 W84-127 USA 6

082 Mpyt Canada 2 0166 W84-125 USA 6

0100 Arenter13 Australia 2 0167 W84-123 USA 2

0101 Ns293 Australia 2 0168 W84-120 USA 2

0169 W84-117 USA 2 0348 Leger Canada 6

0170 W84-116 USA 6 0349 Atlanta Canada 6

0171 W84-113 USA 6 0350 Micmac Canada 2

0172 W84-154 USA 6 0431 Nutans244 Russia 2

0173 W84-148 USA 6 0432 Nutans106 Russia 2

0174 W84-165 USA 2 0433 Mockobkun121 Russia 2

0175 W84-119 USA 2 0434 Australia no. 1 Australia 6

0192 Soufara‘‘s’’ Syria 2 0435 Arupo‘‘s’’ Mexico 2

0193 C18887/C15961 Syria 2 0438 Karla Mexico 6

0194 Thunk27 Syria 6 0439 Bedford Mexico 6

0195 Owinn Syria 6 0440 Ideal Mexico 6

0196 Lignee527 Syria 6 0441 Gloria‘‘s’’/Com‘‘s’’ Mexico 6

0197 Martin46 Syria 6 0442 Gloria‘‘s’’/Com‘‘s’’ Mexico 6

0198 Atlas46 Syria 6 0443 Con‘‘s’’/Collo‘‘s’’ Mexico 6

0199 DeirAha106 Syria 6 0444 H. vulgare 1 Turkey 6

0200 Clipper/volla Syria 6 0445 H. vulgare 2 Turkey 2

0212 Grivita Romania 2 0462 Australia no. 2 Australia 6

0213 Produtiv Romania 2 0465 Rubust USA 6

0214 W12269 Australia 6 0466 Hazen USA 6

0215 Oregen line USA 2 0467 Morex USA 2

0216 Asse USA 6 0567 Karan15 Mexico 6

0217 Ris/1508 Sweden 6 0571 Ai471 Germany 6

0220 Quibenras Columbia 6 0572 Ai127 Germany 2

0221 Dram England 6 0573 Ai128 Germany 2

0222 Lignee640 France 6 0575 Ai473 Germany 2

0223 7109 Sweden 6 0576 Ai472 Germany 2

0224 7198 Sweden 2 0577 Ai125 Germany 2

0225 7200 Sweden 6 0578 Ai474 Germany 2

0226 Lina Sweden 6 0579 Ai508 Germany 2

0227 Alva Sweden 2 0582 Ai126 Germany 2

0228 Harry Sweden 2 0585 Ai509 Germany 2

0229 Kristina Sweden 2 0586 Ai510 Germany 2

0230 Pallas Sweden 6 0588 Ai471 Germany 6

0231 Cd15425 USA 6 0589 Ai124 Germany 2

0240 Washington USA 2 0590 Ai475 Germany 6

0342 Grand prix Netherland 2 0597 Farniwait Hungary 2

0343 Rorin Netherland 2 5441 Baudin Australia 2

0344 Atem Netherland 2 5442 Hamelin Australia 2

0346 Lauriner Canada 6 5443 Gairdner Australia 2

0347 Kinkora Canada 6

34 Euphytica (2011) 178:31–43

123

Agricultural University, Wuhan, China in 2005. The

leaf area (LA), stem diameter (SD), grains per plant

(GPP), filled grains per plant (FGPP), grain weight

per plant (GWP), plant height (PH), spikelets on main

spike (SMS), grains on main spike (GMS), grain

weight on main spike (GWMS), length of main spike

(LMS), density of main spike (DMS), length of the

1st internode (LFIN), length of spike neck (LSN), and

awn length (AL) were measured. The mean and

variance were calculated for each trait.

DNA isolation

The cetyltrimethylammonium bromide (CTAB)

method was used to extract genomic DNA from about

0.6 to 1.0 g of young leaf-tissue of each accession (Stein

et al. 2001). The quality of DNA was checked using

0.8% agarose gel electrophoresis, and the DNA

concentration was measured using a spectrophotometer.

SSR genotyping

In order to have a good coverage of the barley genome,

42 primer pairs, six from each chromosome, were

selected from the published genetic maps (Karakousis

et al. 2003; Varshney et al. 2007). Polymerase chain

reaction (PCR) was carried out in a final volume of 15

ll, containing 3 ll of the 20 ng ll-1 genomic DNA,

1.5 ll of 109 PCR buffer (with 15 mM Mg2?), 0.3 ll

of 10 mM dNTP mixture, 2.0 ll of a 2.5 lM solution

of the forward and reverse primers, and 0.6 units of

Taq DNA polymerase (TakaRa Biotechnology,

Dalian, China). DNA amplifications were performed

in a thermocycler using the following touchdown PCR

protocol: 1 cycle of 3 min at 94�C, followed by 15

cycles 94�C for 30 s, 30 s at 60�C (decreasing 1�C per

cycle), 45 s at 72�C. Another 25 cycles of 30 s at 94�C,

30 s at 50�C, 45 s at 72�C. The reaction ended with a

5 min extension at 72�C.

After PCR amplification, 6 ll of 98% (v/v)

formamide electrophoresis loading dye containing

0.25% (w/v) bromophenol blue, 0.25% (w/v) xylene

cyanole FF and 100 mM EDTA were added to each

reaction mixture. The PCR products were denatured

and separated on 6% denaturing polyacrylamide gel.

The gels were visualized with silver staining. Fig-

ure 1 shows polymorphism pattern of SSR marker

Ebmac775 as an example.

Genetic diversity analysis

The SSR markers were treated as co-dominant markers.

The collection of the barley germplasm was classified

into six groups based on their geographic origins, Tibet

(TB), Syria (SY), Europe (EU), North America (NA),

South America (SA) and Australia (AU). To determine

the magnitude of genetic variation in the 175 barley

accessions, POPGENE program (Yeh and Yang 2000)

was used to calculate allelic richness (A), proportion of

polymorphic loci (P), Shannon’s information index (I)

(Shannon and Weaver 1949), genetic differentiation

among population (Fst) and gene diversity (He). Gene

diversity was estimated according to the formula of Nei

(1973) for each locus He = 1 - RPij2, where Pij is the

frequency of the jth allele for ith locus summed across

all alleles of the locus. Nei’s (1972) genetic distance (D)

was calculated for each pair of population and for each

pair of tested entries. Dendrogram based on Nei’s

genetic distances (Nei 1972) were generated using the

unweighted pair group method with arithmetic average

(UPGMA). All the analyses were conducted using

POPGENE 1.32 Window based computer package

(Yeh and Yang 2000).

Association mapping

Mean phenotypic values of the 14 quantitative traits

(LA, SD, GPP, FGPP, GWP, PH, SMS, GMS,

Fig. 1 An example of amplification profiles of SSR marker Ebmac775 in the 40 barley accessions. Lanes 1–40 corresponds to the

code L35 to L74 in Table 1. Lane 41: DNA ladder

Euphytica (2011) 178:31–43 35

123

GWMS, LMS, DMS, LFIN, LSN and AL) were

subjected to association analysis based on the whole

set of 175 accessions used in this study. Association

analysis was performed based on the general linear

model by using computer software TASSEL 2.0.1

(http://www.maizegenetics.net/tassel) account for pop-

ulation structure estimates from STRUCTURE2.1

software (Prichard et al. 2000, http://pritch.bsd.uchicago.

edu/structure.html). The number of permutation run

was set as 10,000 to obtain the permutation-based test

of marker significance and the experiment-wise

P value for marker significance. Markers were con-

sidered to be associated with the traits if the markers

are significant (P \ 0.05) in all the three tests, F test,

permutation-based test, and experiment-wise test.

Results

Allelic variation at SSR loci

A total of 278 alleles were detected by 42 pairs of

primers across the 175 barley accessions with a mean

of 6.62 alleles per primer pair. The highest number of

alleles (6.0) for all accessions, regardless of their

origins, was observed in Ebmac501 locus, following

by Awbms22-2, Ebmac032 and Ebmac785 with 5

alleles. The effective number of alleles (ne) by

Kimura and Crow (1964) was estimated and it was

between 1.000 and 3.8805 (Awbms22), with means of

1.7097 ± 0.6329. The mean Nei’s gene diversity was

0.3388 ± 0.2236 with highest one detected in locus

Awbms22 (0.7423).

For each group of the barley accessions, the total

number of detected SSR alleles in Tibetan wild type,

Syria, Europe, Australia, North America and South

America barley was 264, 219, 249, 233, 265 and 219,

respectively. The average number of alleles per locus

ranged from 1.78 in Syrian and Australian barley

population to 2.154 in North American barley popu-

lation. The highest number of alleles (5) was observed

in locus Ebmac501, Awbms22, Ebmac785, and

Ebmac824 in Tibetan wild barley. Ebmac501 dis-

played highest number alleles (5) in both Syrian and

European barley. Three loci, Ebmac032, Ebmac501,

and Awbms22, each possessed 5 alleles in North

American barley. Five loci, Ebmac501, Awbms22,

Ebmac785, Bmac90, and Ebmac775, each contained 4

alleles in Australian barley. The highest number of

alleles (4) was detected in Ebmac032, Ebmac775,

Ebmac785, Awbms22, and Ebmac824 in South Amer-

ican barley. The polymorphic loci in population was

78.86% in Tibetan wild type, 65.85% in Syrian,

76.42% in European, 67.48% in Australian, 77.24% in

North American, and 60.98% in South American

barley.

Comparison of gene diversity

The statistics describing the genetic diversity found at

each locus in each population was calculated. Consid-

ering the geographic origin of the experimental barley

accessions, the average gene diversity for the whole

sample was 0.3387, whereas the mean value for each

population was as follows: TB = 0.3286, SY =

0.2474, EU = 0.299, AU = 0.2867, NA = 0.3138,

SA = 0.2536. Large variation of gene diversity

among loci was found. The lowest gene diversity (0)

was detected in 22 loci for all populations. The lowest

number of loci (26) with 0 of gene diversity was

detected in Tibet wild barley. Gene diversity of [0.50

in all six populations was observed for the loci,

Ebmac032, Awbms22, and Ebmac785. The highest

gene diversity in each population was found at

Ebmac824 (0.7478) in Tibetan barley, Ebmac775

(0.6916) in South American barley, Ebmac501 in

Syrian (0.7600), European (0.7145), Australian

(0.7188) and North American barley (0.7176). Seven-

teen loci with gene diversity [ 0.6 were found in

Tibetan wild barley, which is twice the number of loci

detected in any other populations.

The total genetic diversity can be divided into

within and among populations. In our case, the

proportion of each component varied from locus to

locus. The averaged genetic variation among popu-

lations (Fst) was 13.55% with the highest value of

28.25% detected for loci, HVM27 and Bmac167.

Cluster analysis

Based on Nei’s original genetic distance, cluster

analysis was carried out using the UPGMA method

and resulted in a dendrogram shown in Fig. 3. The EU

and NA barley populations showed a smallest genetic

distance (0.0419), and were grouped together. The

largest genetic distance was found between TB and SY

barley. TB population was also well separated from the

other 5 barley populations.

36 Euphytica (2011) 178:31–43

123

In order to demonstrate the phylogenetic relation-

ships of 175 barley accessions, a dendrogram based

on the Nei’s genetic distance was shown in Fig. 2.

The 175 barley accessions could be divided into two

mega-groups. Mega-group I included 144 accessions

that can be further divided into four subgroups and

L37022305880589L66L67059002170216L47012601300131016201720173016501660167L49079L50015901600168017001710770220L73L7401940195L6104330225022602270228059704440126012701350141014201470148014901460156015701430144012901400145017401330134013501530169016101520158022104340780125046602140438043904400441L530230L720196015401500151

IA

a

b

c

d

USA

Europe

USA6 row

USA2 row

6 rowe

IB

L3502120462L36L38L46L700573L64L65L68L39L40L42L41L48L54L59L60L58L55L56L57L43L45L69L71L62L6304670567

L44L51L520197019801990200022208101370192022903440342034304310163057805750164057604320572012101220123012403500435544154425443044505770582019202240585058601320100082021301010175021508001550579024005710231034703490348034604650442044301380139

ICa

b

2 row

6 row

II

6 row

2 row

I

ID

Fig. 2 Dendrogram of 175 barley accessions based on the Nei’s (1972) original genetic distance calculated from data of 123 SSR

loci using the UPGMA method. The code for each accession refers to Table 1

Euphytica (2011) 178:31–43 37

123

mega-group II contained 31 accessions in which 26

belong to Tibetan wild barley. The pattern of

clustering did not show clearly separation with the

geographic distribution of barley collections, but the

accessions originated from same geographic more or

less were grouped together. Subgroup IA-a contained

15 accessions collected from USA and 3 accessions

of Tibetan wild barley (L47, L49 and L50). All

accessions in IA-c are six-row USA barley. IA-d

group contained 10 two-row accessions and 2 six-row

accessions (0135 and 0158) from USA. IC-a group

was composed of 41 accessions originated from

different geographic origins, in which all of them are

two-row barley except accession 080 and 0164. All

the 9 accessions in IC-b group are six-row barley. As

indicated in Fig. 2, among the 40 accessions of

Tibetan wild barley, 26 (65%) accessions belonged to

the mega-group II, and the other 14 (35%) belonged

to mega-group I.

Association analysis

Association mapping was performed to find SSR

markers potentially associated or linked with 14

quantitative traits. Table 2 gives an overview of

markers with their genome positions and correlations

with traits. The number of loci associated with each

trait ranged from one (SD, FGPP, GWP, LMS and

AL) to seven (PH). The percentage of the total

variation explained by each marker was from 4.59%

(HVM2 associated with PH) to 17.48% (Bmac90

associated with DMS).

Discussion

Genetic diversity of 175 barley accessions of world-

wide origin was characterized by 42 SSR markers.

The average number of alleles per SSR was 6.62,

which is close to what Ivandic et al. (2002) reported

in wild barley samples from the Fertile Crescent (5.5

alleles per locus), but is much lower than 16.3 alleles

per locus detected by Matus and Hayes (2002) in a

population of 147 barley accessions. Other investi-

gators have reported the average number of allele per

locus in barley, ranging from 2.1 (Becker and Heum

1995) to 18 (Saghai-Maroof et al. 1994). The number

and set of SSR markers and accession sampling may

have contributed to these discrepancies in the

estimated allele number per locus among different

studies.

Considering the entire germplasm array, the aver-

age gene diversity value for the 42 SSRs surveyed is

0.3387, which is lower than the He value found in

both scald resistant (0.43) and sensitive (0.40)

Turkish barley accessions (Dizkirici et al. 2008).

Anderson et al. (1993) referred the gene diversity as

the polymorphic information content (PIC). The gene

diversity values in our study is much lower than the

PIC value reported for Hordeum vulgare subsp

spontaneum group (0.79) and subsp. vulgare (0.75)

by Matus and Hayes (2002) in 147 genotypes. The

PIC values of 0.14–0.78 were detected in SSR studies

in barley by Pillen et al. (2000). This may be caused

by different sets of SSR primers used in different

studies. Cho et al. (2000) reported lower PIC values

for the Genbank-derived SSR primers than those for

the genomic library-derived SSR primers.

Previous population genetic studies have found that

wild barley in Qinghai-Tibet plateau of China is a rich

source of genetic diversity (Zhang et al. 1994; Feng

et al. 2006). The results of our survey showed that

Tibetan wild barley displayed the highest Shannon

diversity index value (0.5254) and the highest value of

gene diversity (0.3286), indicating a higher genetic

diversity than other barley sub-populations. In a direct

comparison of diversity for 19 allozyme loci between

25 landrace populations of cultivated barley from Iran

and 28 wild barley populations from Israel, Brown and

Munday (1982) showed that Iranian landraces pos-

sessed a 73% of genetic diversity, and suggested a rank

in terms of allozyme diversity, wild barley [ land-

races [ composite crosses. Contrary to a common

0.01 0.03 0.05 0.07 0.09

TB

SY

EU

NA

AU

SA

Fig. 3 The genetic relationships among the six barley

populations calculated using UPGMA method based on the

Nei’s (1972) genetic distance

38 Euphytica (2011) 178:31–43

123

Table 2 SSR markers associated with 15 traits and chromosome location

Traitsa Alleles R2b p-Mc p-Pd p-adje Chromosome

LA Bmag394 0.0849 \0.0001 0.0001 0.00014 5HS

LA Ebmac871 0.0804 0.0006 0.0006 0.0293 3HS

LA Bmac213 0.111 0.0001 0.0002 0.0043 1HL

LA Bmag718 0.1011 \0.0001 0.0001 0.0001 1HL

SD HVM30 0.1283 \0.0001 0.0001 0.0001 5HL

GPP HVM30 0.0699 0.0003 0.0007 0.0127 5HL

GPP Ebmac658 0.067 0.0004 0.0013 0.024 4HL

GPP Bmac167 0.0629 0.0006 0.0005 0.0278 7HS

FGPP Ebmac705 0.1013 0.0002 0.0025 0.0101 3HS

GWP Bmac167 0.0599 0.0007 0.0006 0.0369 7HS

PH HVM30 0.0781 \0.0001 0.0001 0.0001 5HL

PH Ebmac705 0.1054 \0.0001 0.0002 0.0001 3HS

PH Ebmac871 0.0826 \0.0001 0.0002 0.0001 3HS

PH HVM2 0.0459 0.0014 0.001 0.0927 1HL

PH Bmag718 0.0518 0.0006 0.0008 0.0408 1HL

PH Bmag867 0.0827 \0.0001 0.0001 0.0001 6HL

PH Bmac167 0.0537 0.0005 0.0002 0.0224 7HS

SMS HVM30 0.1392 \0.0001 0.0001 0.0001 5HL

SMS Bmag394 0.0866 \0.0001 0.0003 0.0001 5HS

SMS Bmac84 0.1006 \0.0001 0.0002 0.0001 4HL

GMS HVM30 0.1291 \0.0001 0.0001 0.0001 5HL

GMS Bmag394 0.0634 0.0008 0.001 0.0493 5HS

GMS Bmac213 0.115 0.0001 0.0002 0.0041 1HL

GMS Hvm68 0.065 0.0007 0.0005 0.0339 4HL

GMS Bmac84 0.1054 \0.0001 0.0001 0.0001 4HL

GWMS HVM30 0.0748 0.0004 0.0001 0.0037 5HL

GWMS Ebmac684 0.0771 0.0002 0.0003 0.0052 5HL

GWMS Bmag394 0.0971 \0.0001 0.0001 0.0001 5HS

GWMS Bmac213 0.1061 0.0002 0.0001 0.0043 1HL

GWMS Ebmac640 0.1099 \0.0001 0.0008 0.0001 2HS

GWMS Bmac84 0.0661 0.0006 0.0006 0.0288 4HL

LMS Ebmac640 0.1107 \0.0001 0.0029 0.0001 2HS

DMS Bmac90 0.1748 \0.0001 0.0001 0.0001 1HS

DMS HVM2 0.1184 \0.0001 0.0001 0.0001 1HL

DMS Ebmac501 0.159 \0.0001 0.0001 0.0001 1HS

DMS Bmac213 0.0898 0.0005 0.0004 0.0273 1HL

LFIN HVM30 0.0673 \0.0001 0.0001 0.0001 5HL

LFIN Ebmac705 0.0932 \0.0001 0.0001 0.0001 3HS

LFIN Ebmac871 0.0707 \0.0001 0.0002 0.0001 3HS

LFIN Hvm54 0.0538 \0.0001 0.0001 0.0001 2HL

LFIN Bmac167 0.0472 0.0002 0.0004 0.0107 7HS

LSN HVM30 0.0933 \0.0001 0.0001 0.0001 5HL

LSN Ebmac705 0.0671 0.0006 0.0007 0.0318 3HS

LSN Ebmac871 0.1265 \0.0001 0.0001 0.0001 3HS

Euphytica (2011) 178:31–43 39

123

impression, electrophoretic assay for genetic diversity

at 16 isozyme loci in wild barley and indigenous

primitive varieties of cultivated barley collected from

43 locations in four eastern Mediterranean countries,

Jordan, Syria, Turkey and Greece, did not reveal a

significant excess of diversity in wild barley (Jana and

Pietrzak 1988). A comparative analysis of genetic

polymorphism in wild and cultivated barley from

Tibet using isozyme and ribosomal DNA markers indi-

cated that little genetic differentiation was detected

among the three barley groups (H. vulgare ssp.

vulgare, H. vulgare ssp. agriocrithon, and H. vulgare

ssp. spontaneum) (Zhang et al. 1994).

The UPGMA dendrogram grouped the 175 barley

accessions into two mega-groups. Among the 40

accessions of Tibet wild barley, 26 (65%) accessions

were grouped into mega-group II, and the other 14

(35%) belonged to mega-group I. No clear separation

of grouping between wild barley and cultivated

barley was detected. This is in consistent to the

similar study (Matus and Hayes 2002), in which wild

barley accessions were clustered into multiple groups

and associated with other barley varieties. Certain

degree of divergence between two-row and six-row

barley types was observed in our survey. Subgroup

IA-c contained six-row USA barley. IA-d group

consists of 10 two-row and 2 six-row barley acces-

sions. IC-a group is composed of 41 accessions

originated from different geographic origins, in

which all of them are two-row barley except acces-

sion 080 and 0164. All the 9 accessions in IC-b group

are six-row barley. The similar tendency of grouping

was also reported by Matus and Hayes (2002). The

number of fertile florets per rachis node (two-row vs.

six-row), which was controlled by two major gene

loci, the vrs1 locus located in the centromeric region

of chromosome 2 (2H) and int-c on the short arm of

chromosome 4 (4H) (Franckowiak and Lundqvist

1997; Lundqvist and Franckowiak 1997), defines the

two major germplasm groups within cultivated

Hordeum.

As indicated in Fig. 2, North American two-row

barley and six-row barley accessions were separated

from each other (IA-c, IA-d, IC-a and IC-b). Our

result is consistent to the finding of Matus and Hayes

(2002), and further confirms that the North American

two-row and six row malting barley germplasm pools

have distinct profiles (Marquez-Cedillo et al. 2000).

Matus and Hayes (2002) found that the North

America two-row malting varieties are distinct from

the two-row malting accessions from Japan, Austra-

lia, and Europe. In our survey, part of two-row North

America barley accessions were clustered into multi-

ple groups and associated with two-row barley

accession originated from Europe, Australia, Syria,

and South America.

At population level, UPGMA-dendrogram based on

Nei’s original genetic distance showed that North

America barley is closely related to Europe barley.

Australia barley population is sister to Europe and

North America barley populations (Fig. 3). An expla-

nation might be that North American cultivars and

European cultivars have common ancestors. North

American varieties were originally introduced from

Table 2 continued

Traitsa Alleles R2b p-Mc p-Pd p-adje Chromosome

LSN HVM2 0.051 0.0003 0.0002 0.0089 1HL

LSN Hvm54 0.0574 0.0001 0.0002 0.0044 2HL

LSN AWBMS22 0.0808 0.0001 0.0002 0.0042 7HL

AL HVM11 0.1241 \0.0001 0.0003 0.0001 6HS

Number of permutations run = 10,000a LA leaf area; SD stem diameter; GPP grains per plant; FGPP filled grains per plant; GWP grain weight per plant; PH plant height;

SMS spiklets on main spike; GMS grains on main spike; GWMS grain weight on main spike; LMS length of main spike; DMS density

of main spike; LFIN length of the 1st internode; LSN length of spike neck; AL awn lengthb R2: the fraction of the total variation explained by the marker after fitting the other model effectsc p-M: probability based on the F test for specific markersd p-P: the permutation based test for marker significance of individual markerse p-adj: the experiment-wise P value for marker significance that controls the error-rate for the entire set of hypotheses

40 Euphytica (2011) 178:31–43

123

European within the past 100 years. The study of

Fischbeck (2003) stated that barley seeds were intro-

duced to North America from many countries, espe-

cially from Central, Northern, and Eastern Europe. In

most cases, there has been little germplasm infusion

from outside North America since the initial introduc-

tions (Matus and Hayes 2002).

The molecular marker data present here can provide

an opportunity to examine preliminary evidence for

marker-trait associations. The challenge in association

analysis is to separate the population structure from the

genetic linkage as causes for marker-trait association.

The 175 accessions were artificially divided into six

populations according to their origins for genetic

analysis, however, no trait-associated markers were in

the set of markers discriminating the six populations,

so we can concluded that identified marker-trait

associations were not a consequence of population

structure, but very probably were indeed caused by

linkage (Kraakman et al. 2004).

Association between markers and traits was tested

in three ways. Markers were considered to be

associated with the traits only when the markers are

significant (P \ 0.05) in all the three tests, F test,

permutation-based test, and experiment-wise test. In

the aspect of traits analyzed, the following 10

markers were associated with more than 2 traits:

Hvm30 (was associated with SD, GPP, PH, SMS,

GMS, GWMS, LFIN and LSN); Both Bmag394 (LA,

SMS, GMS, and GWMS); Ebmac871 (with LA, PH,

LFIN, and LSN); Bmac213 (LA, GMS, GWMS and

DMS); Ebmac705 (FGPP, PH, LFIN and LSN);

HVM2 (PH, DMS and LSN), and Bmac84 (SMS,

GMS and GWMS). These markers could be of great

use to correlate traits that were not reported to be

correlated or to support a correlation between traits

that was suspected or need more investigation. For

example, all the four markers, Bmag394 (5H),

Bmag394 (5H), HVM30 (5H) and Bmac84 (4H) were

associated with SMS, GMS and GWMS, suggesting

that these three traits might correlate to each other.

The markers significantly associated with traits

were compared to the markers associated QTL

identified in barley (http://grain.jouy.inra.fr/GG2/

Barley/).Up to date, 63 markers significantly associ-

ated with plant height (PH) have been identified. The

QTLs controlling PH were located on chromosome

1H to 7H. Our association mapping has identified 7

markers associated with PH, which located on

chromosome 1H, 3H, 5H, 6H and 7H. Two loci on

3HS associated with PH gave higher percentage of

the total variation explained by each marker. The

dwarfing gene uzu, and denso were mapped onto the

region of chromosome 3HL (Laurie et al. 1993:

Zhang 2003). Wang et al. (2010) identified two QTLs

controlling plant height on 3HL. Comparison with the

consensus map, the positions of two markers identi-

fied here are different from those reported in previous

studies. Eighteen markers located on chromosome 1H

to 7H have found to be significantly associated with

GWP previously. Our association analysis revealed

that Bmac167 on 7H was associated with GWP. Our

survey detected 5 markers associated with GMS

which are different from the 9 markers reported

(http://grain.jouy.inra.fr/GG2/Barley/). Six markers

on 2H and 6H associated with LFIN have been

identified. In present study, we found, with aid of

association mapping, that 5 SSR loci on 2H, 3H and

5H were significantly associated with LFIN. Up to 28

markers located on 1H to 7H were associated with

grain weight/spike. As shown in Table 2, six loci on

1H, 2H, 4H and 5H were found to be associated with

GWMS. In addition to the four loci on 2H associated

with stem diameter, we detected another one loci on

5HL associated with this trait.

Previous studies demonstrated that both QTL

analysis and association mapping are suitable tools

for mapping quantitative loci in barley (Bezant et al.

1997; Kraakman et al. 2004; Cockram et al. 2008;

Stracke et al. 2009). We found 18 loci associated with

14 quantitative traits using heterogeneous population

for association mapping. According to Breseghello

and Sorrells (2006) and Peng et al. (2009), the

markers identified via association mapping need to

be validated for individual cultivars involved in

crosses before they can be applied to marker-assisted

selection in the progeny.

Acknowledgments This project is supported by the National

Natural Science Foundation of China (30630047) and China

863 Project (2006AA10Z1C3).

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