Genome-wide QTL analysis for pre-harvest sproutingtolerance in bread wheat

Amita Mohan Æ Pawan Kulwal Æ Ravinder Singh Æ Vinay Kumar ÆReyazul Rouf Mir Æ Jitendra Kumar Æ Manoj Prasad Æ H. S. Balyan ÆP. K. Gupta

Received: 25 July 2008 / Accepted: 24 October 2008 / Published online: 15 April 2009

� Springer Science+Business Media B.V. 2009

Abstract A framework linkage map comprising

214 molecular marker (SSR, AFLP, SAMPL) loci

was prepared using an intervarietal recombinant

inbred line (RIL) mapping population of bread wheat.

The RIL population that was developed from the

cross SPR8198 (red-grained and PHS tolerant geno-

type) 9 HD2329 (white-grained and PHS susceptible

genotype) following single seed descent segregated

for pre-harvest sprouting (PHS). The RIL population

and parental genotypes were evaluated in six differ-

ent environments and the data on PHS were collected.

Using the linkage map and PHS data, genome-wide

single-locus and two-locus QTL analyses were con-

ducted for PHS tolerance (PHST). Single-locus

analysis following composite interval mapping

(CIM) detected a total of seven QTL, located on

specific arms of five different chromosome (1AS,

2AL, 2DL, 3AL and 3BL). These seven QTL

included two major QTL one each on 2AL and

3AL. Two of these seven QTL were also detected

following two-locus analysis, which resolved a total

of four main-effect QTL (M-QTL), and 12 epistatic

QTL (E-QTL), the latter involved in 7 QTL 9 QTL

interactions. Interestingly, none of these M-QTL and

E-QTL detected by two-locus analysis was involved

in Q 9 E and Q 9 Q 9 E interactions, supporting

the results of ANOVA, where genotype 9 environ-

ment interaction were non-significant. The QTL for

PHS detected in the present study may be efficiently

utilized for marker-aided selection for enhancing

PHST in bread wheat.

Keywords Bread wheat � Pre-harvest sprouting �Main-effect QTL � Epistatic QTL

Introduction

Pre-harvest sprouting (PHS) in bread wheat (Triticum

aestivum L.) is characterized by premature germina-

tion of kernels in a mature spike prior to harvest

(usually under wet and humid conditions), and is due

to early breakage of seed dormancy (Sharma et al.

1994; Groos et al. 2002). The PHS severely reduces

yield and quality of the end product of wheat grain

(Varughese et al. 1987; Lenton 2001). Flour obtained

from sprouted grains loses its thickening power due

A. Mohan � R. Singh � V. Kumar � R. R. Mir �J. Kumar � H. S. Balyan � P. K. Gupta (&)

Molecular Biology Lab, Department of Genetics & Plant

Breeding, C.C.S. University, Meerut 250004, India

e-mail: pkgupta36@gmail.com

P. Kulwal

Biotechnology Centre, Department of Agriculture Botany,

Dr. Panjabrao Deshmukh Agricultural University, Akola

444104, India

M. Prasad

National Institute for Plant Genome Research, JNU

Campus, Aruna Asaf Ali Marg, P.O. Box 10531,

New Delhi 110067, India

123

Euphytica (2009) 168:319–329

DOI 10.1007/s10681-009-9935-2

to starch breakdown, so that the baked products have

a smaller volume and a compact, sticky crumb

structure (Appels et al. 2003; Kottearachchi et al.

2006). The loss of quality due to PHS is mainly

caused by high a-amylase activity.

PHS and dormancy are related complex traits that

are together controlled by many genes and environ-

mental factors. Several other factors like spike and

plant morphology, kernel colour, presence of germi-

nation inhibitors in different tissues and physical

barriers to water penetration in the spike are also

associated with these traits (Nilson-Ehle 1914; King

1984; Salmon et al. 1986; Morris and Paulsen 1988;

Paterson et al. 1989; Gale 1989; Groos et al. 2002;

Himi et al. 2002; Rathjen et al. 2007; Farley and

Adkins 2007). In the past, inheritance of the genetic

components of PHS tolerance and seed dormancy in

wheat was studied using classical Mendelian and

molecular approaches. The initial studies suggested

that PHS tolerance and seed dormancy are quantita-

tive traits and are controlled by multiple genes.

Subsequent studies involving quantitative trait loci

(QTL) analysis using diverse mapping populations

identified a number of genes/QTL involving all the

21 different chromosomes (Anderson et al.1993;

Bailey et al. 1999; Roy et al. 1999; Zanetti et al.

2000; Kato et al. 2001; Mares and Mrva 2001; Groos

et al. 2002; Flintham et al. 2002; Kulwal et al. 2004,

2005; Mori et al. 2005; Mares et al. 2005; Ogbonnaya

et al. 2006, 2008; Imtiaz et al. 2008; Chen et al.

2008). In rice and barley also, QTL for PHS/

dormancy were identified on all the chromosomes

in each case (Gao et al. 2008; Ullrich et al. 2008).

Thus, the genetics of PHS in cereals is complex,

which makes the development of PHS tolerant cereals

a major challenge for crop geneticists and plant

breeders.

In our own laboratory, using the same RIL

population (SPR8198 9 HD2329), we initially iden-

tified two QTL for PHST in wheat, one each on

chromosome arms 6BS and 7DL (Roy et al. 1999),

and later identified a major QTL for PHST on

chromosome arm 3AL (Kulwal et al. 2005). The

present study is an extension of this earlier work, and

involved single-locus and two-locus QTL analyses,

which together led to the identification of nine main

effect QTL and 12 epistatic QTL that were involved

in seven QTL 9 QTL (Q 9 Q) interactions.

Materials and methods

Plant material and data recording

A mapping population in the form of 90 recombi-

nant inbred lines (RILs) derived from a cross

SPR8198 (red-grained; PHS tolerant) 9 HD2329

(white-grained; PHS susceptible) was procured from

Punjab Agricultural University, Ludhiana, India. The

details about the RIL population used during the

present study are available elsewhere (Kulwal et al.

2005). The RIL population along with two parental

genotypes was evaluated in replicated trials with

three replications during 2000–2001 at three differ-

ent locations (i.e. Meerut, Pantnagar and Ludhiana),

which are the major wheat growing areas of

Northern India. During 2001–2002 crop season,

the evaluation was again carried out in simple lattice

design experiments (two replications) at the same

three locations. This allowed collection of data from

six environments. In each environment, five spikes

from each of the two parents and from each of the

90 RILs from each replication were harvested as and

when they attained physiological maturity, charac-

terized by the loss of green colour by [75% florets

of the spike. There were no rains at the time of

harvesting of the sipkes, so that the level of PHS in

harvested spikes was determined in the laboratory

(Baier 1987). Data on PHS were scored on a scale

of 1–9 with score of 1 for genotypes with no visible

sprouting and a score of 9 for the genotypes with

complete sprouting. This scale corresponds to 1

through 10 scale of McMaster and Derera (1976)

with slight modifications (for details, see Kulwal

et al. 2004).

Genotype 9 environment interaction and rank

correlations

For the study of genotype 9 environment interac-

tions, analysis of variance (ANOVA) was con-

ducted by PROC GLM in SAS (SAS Institute

1996), using genotypes as fixed effects and all the

other sources as random effects. Spearman’s rank

correlations between 15 possible pairs of ranks of

RILs for PHS in six different environments were

determined using online statistical software (Wessa

2008).

320 Euphytica (2009) 168:319–329

123

DNA isolation and PCR primers for DNA

markers (SSR, AFLP/SAMPL)

DNA was isolated from leaves of one month-old field

grown plants using a modified CTAB method

(Saghai-Maroof et al. 1984).

A set of 778 SSR markers (gwm, wmc, gdm, cfa,

cfd and barc) was used; in each case, the forward

primer was synthesized with M13 tail, which was

fluorescently labeled either with IRdye 700 or with

IRdye 800. For AFLP analysis, 16 primer combina-

tions (two EcoRI primers, E35 and E36, each in

combination with eight MseI primers) were used.

Similarly, for SAMPL (selective amplification of

microsatellite polymorphic loci) analysis, nine primer

combinations (SAMPL primer S6 in combination

with four MseI primers, and SAMPL primer S7 in

combination with five MseI primers) were used. The

primers E36 (for AFLP) and S6 (for SAMPL) were

labeled with 6-FAM (6-carboxifluorescein) while the

primers E35 (for AFLP) and S7 (for SAMPL) were

labeled with NED [N-(1-naphthyl)-ethyletediamine].

The sequences for AFLP primers are available in Vos

et al. (1995) and those of SAMPL primers are

available in Witsenboer et al. (1997). The PCR

primers for SSR, AFLP and SAMPL analysis were

synthesized on contract either by Illumina (USA), or

by Microsynth (Switzerland), or by Applied Biosys-

tems (USA) depending upon convenience.

SSR, AFLP and SAMPL analyses

For SSR analysis, DNA amplification was carried out

in an Eppendorf Master Cycler 9600 using the

recommended annealing temperatures for the respec-

tive SSR primers (Roder et al. 1998). For visualizing

PCR products, the forward primer having M13 tail,

labeled with an IRDye (IRdye 700 or IRdye 800), and

the corresponding unlabelled reverse primer were

used in PCR reactions. The forward primers labeled

with IRDye 700 or IRDye 800 were used in separate

PCR reactions, and the amplification products from

two PCRs were mixed prior to loading of gels to

achieve high-throughput. PCR amplification was

carried out in 10 ll reaction mixtures containing

50 ng of template DNA, 200 nM of each unlabeled

primer, 1.0 pmole of IRDye 700/IRDye 800 labeled

primer, 0.2 mM each of dNTP, 2.5 mM MgCl2, 1X

PCR buffer and 0.5 U Taq Polymerase. The amplified

PCR products were separated on LI-COR 4300 DNA

analyzer using a 6.5% gel matrix. All marker data

were scored by visual inspection.

For AFLP/SAMPL analysis, restriction digestion,

ligation of adapters and pre-amplification were car-

ried out using AFLP System I Kit, Life Technology,

USA following Vos et al. (1995) with some modi-

fications. Selective amplification of the pre-amplified

DNA was carried out in an Applied Biosystems 9600

Thermal Cycler (PE/Applied Biosystems). The PCR

products were resolved on 4% PAGE using ABI

PrismTM 377 DNA sequencer (PE/Applied Biosys-

tems), using dROX (dichlororhodamine dye) as an

internal size standard, and the data were analyzed

using GeneScan� analysis software version 3.2.4

(PE/Applied Biosystems) and Genotyper� analysis

software version 2.5 as per the instructions given in

the user’s manual. The AFLP/SAMPL markers were

designated according to the prescribed nomenclature

available at the GrainGenes (http://wheat.pw.usda.

gov/ggpages/keygeneAFLPs.html).

Preparation of framework linkage map

The genotyping data on 90 RILs for polymorphic

markers were used for constructing a framework

linkage map of all the 21 chromosomes using

MAPMAKER/EXP v 3.0b (Lander et al. 1987).

Recombination frequencies were converted into cen-

tiMorgans (cM) using the Kosambi function (Kos-

ambi 1944) and using a minimum LOD (logarithm of

odds) score of 2.0 and maximum recombination

frequency of 50%.

QTL analysis

Single-locus QTL analysis using composite interval

mapping (CIM) for detection of main-effect QTL was

conducted using QTL Cartographer V2.5 (Wang et al.

2007) involving multiple linear regression with a

probability in and out of 0.05 and window size of

10 cM. LOD score of 2.5 was used for suggesting the

presence of a putative QTL. Threshold LOD scores,

calculated using 1,000 permutations, were used for

declaring definitive QTL. Two-locus analysis that

identifies main-effect QTL (M-QTL), epistatic QTL

(E-QTL), QTL 9 environment (QE) and QTL 9

QTL 9 environment (QQE) interactions was con-

ducted using QTLNetwork Version 2.0 (Yang et al.

Euphytica (2009) 168:319–329 321

123

2007). The relative contribution of a genetic compo-

nent was calculated as the proportion of the pheno-

typic variation explained (PVE) by that component.

The QTL were designated according to the standard

nomenclature for QTL as recommended for wheat

and used in our earlier study on PHS in wheat

(Kulwal et al. 2004).

Results

Mean PHS values, ANOVA and rank correlations

Mean PHS score for RILs in the six different

environments ranged from 3.62 (Environment V) to

5.34 (Environment IV); the distributions were skewed

towards tolerance to PHS in each of the six environ-

ments, often showing negative kurtosis. The results of

ANOVA of PHS in the RIL population are given in

Table 1. The mean squares due to RILs and environ-

ments for PHS were significant. The 15 possible rank

correlations between the PHS scores of RILs in six

environments were also positive and highly signifi-

cant (Table 2).

Whole genome framework map

A total of 778 pairs of SSR primers were tested on the

two parental genotypes (SPR8198 and HD2329) of the

RIL mapping population. As many as 233 (*30%) of

these SSRs covering all the 21 chromosomes of bread

wheat were polymorphic, and were therefore used to

screen a set of 90 RILs of the mapping populations.

Further, 16 AFLP and 9 SAMPL primer combinations

were tried for detection of polymorphism between the

two parental genotypes; 23 AFLP and 91 SAMPL

polymorphic markers were identified. The framework

genetic map that was prepared consisted of 214 loci

(198 SSR, 5 AFLP and 11 SAMPL loci), which were

distributed on all the 21 different wheat chromosomes

with an average of 10.2 loci/chromosome (Fig. 1). The

map spanned a genetic distance of 3,972 cM. Of the

total mapped loci, a maximum of 77 loci were mapped

on sub-genome A (11 loci/chromosome), followed by

73 loci on sub-genome B (10.4 loci/chromosome) and

64 loci on sub-genome D (9.1 loci/chromosome). On

an individual chromosome, a maximum of 16 loci were

located on chromosome 1A and a minimum of five loci

each were located on chromosomes 4D, 5A and 6A.

QTL analysis

Single-locus QTL analysis

As many as seven QTL were detected using single-

locus analysis (Fig. 1); three QTL (QPhs.ccsu-2A.5

QPhs.ccsu-3A.1 and QPhs.ccsu-3B.6) were each

detected in more than three environments, and were

therefore considered stable (Table 3). The LOD score

for these QTL ranged from 2.80 to 6.41. The PVE by

individual QTL (R2) ranged from 15.2 to 45.11%.

Positive effect of QPhs.ccsu-3A.1 suggested that an

allele for PHS tolerance is available in the PHS tolerant

parental genotype SPR8198 and negative effect of

QPhs.ccsu-2A.5 and QPhs.ccsu-3B.6 suggested that

corresponding alleles imparting tolerance are available

in the PHS susceptible genotype HD2329.

Two-locus QTL analysis

A total of four main-effect QTL (M-QTL) were

identified through QTLNetwork, two on chromosome

Table 1 Analysis of variance for pre-harvest sprouting in

bread wheat

Source of variation Degree of freedom Mean square

Environment 5 128.81***

RIL 89 29.53***

RIL 9 environment 445 2.89

Pooled error 539 2.12

*** P \ 0.001

Table 2 Rank correlation coefficients between the pre-harvest

sprouting scores of RILs of bread wheat in six different

environments

Environment II III IV V VI

I 0.75*** 0.61*** 0.68*** 0.66*** 0.65***

II 0.65*** 0.58*** 0.52*** 0.56***

III 0.77*** 0.66*** 0.87***

IV 0.70*** 0.80***

V 0.65***

Environment I Meerut 2001, II Pantnagar 2001, III Ludhiana

2001, IV Meerut 2002, V Pantnagar 2002; VI Ludhiana 2002

*** P \ 0.001

322 Euphytica (2009) 168:319–329

123

Fig. 1 Whole genome

framework linkage map of

bread wheat showing QTL

(M-QTL and E-QTL) on the

right. j, M-QTL detected

through CIM; d, M-QTL

detected through

QTLNetwork; m, E-QTL

detected through

QTLNetwork

Euphytica (2009) 168:319–329 323

123

2A, and one each on 2B and 3A (Fig. 1). These M-QTL

on three different chromosomes together accounted for

37.28% (3.3–15.99%) of the total PV (phenotypic

variation). Out of 4 M-QTL, three QTL (QPhs.ccsu-

2A.4, QPhs.ccsu-2A.5 and QPhs.ccsu-2B.4) showed

additive effect in negative direction and the remaining

one QTL had positive additive effect (Table 4). As

many as 12 epistatic QTL (E-QTL) were identified,

which were involved in seven digenic epistatic inter-

actions (QQ) and accounted for 27.03% of the PV

Table 3 Single-locus analysis using composite interval mapping for pre-harvest sprouting in bread wheat

Chromosome arm QTL Marker interval Position (cM) LOD R2 (%) a

Environment I

2AL QPhs.ccsu-2A.3 Xgwm95–Xwmc170d 140.11 3.21 22.78 -1.21

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 277.81 5.66 25.11 1.25

3BL QPhs.ccsu-3B.6 Xgwm1005–Xgwm980 92.71 3.53 25.73 -1.28

Environment II

1AS QPhs.ccsu-1A.1 Xwmc24–Xbarc119 1.00 2.89 15.41 1.09

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 275.81 6.41 32.81 1.59

Environment III

2AL QPhs.ccsu-2A.5 Xwmc1045–Xgwm296 253.71 2.83 19.14 -1.08

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 273.81 3.39 19.23 1.09

3BL QPhs.ccsu-3B.6 Xgwm1005–Xgwm980 100.71 3.34 21.83 -1.13

Environment IV

2AL QPhs.ccsu-2A.5 Xwmc1045–Xgwm296 253.71 3.70 31.52 -1.63

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 275.81 3.80 19.91 1.29

Environment V

2AL QPhs.ccsu-2A.5 Xwmc1045–Xgwm296 269.51 3.78 20.56 -1.21

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 277.81 3.97 15.22 1.04

2DL QPhs.ccsu-2D.2 Xcfd168–Xcfd168a 155.01 5.55 37.19 -0.99

2DL QPhs.ccsu-2D.4 Xcfd44–Xgwm539 172.01 5.09 25.72 -1.59

Environment VI

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 277.81 5.79 23.09 1.12

Pooled Data

2AL QPhs.ccsu-2A.5 Xwmc1045–Xgwm296 255.71 4.15 45.11 -1.70

3AL QPhs.ccsu-3A.1 Xwmc153–Xgwm155 277.81 5.89 23.49 1.08

3BL QPhs.ccsu-3B.6 Xgwm1005–Xgwm980 96.71 2.80 17.45 -0.91

Environment I Meerut 2001, II Pantnagar 2001, III Ludhiana 2001, IV Meerut 2002, V Pantnagar 2002,VI Ludhiana 2002

QTL quantitative trait locus

R2 phenotypic variation explained by individual QTL

a additive effect

Table 4 QTL with main-effect (M-QTL) for pre-harvest sprouting detected following two-locus analysis in bread wheat

QTL Interval Position (cM) Range (cM) a P-value R2 (%)

QPhs.ccsu-2A.4 Xwmc170d–Xcfd168 170.2 161.2–179.2 -0.51 0.000001 3.30

QPhs.ccsu-2A.5 Xgwm1045–Xgdm296 256.7 250.7–261.7 -1.06 0 13.90

QPhs.ccsu-2B.4 XE36M605–XE36M607 276.7 253.8–282.7 -0.57 0 4.09

QPhs.ccsu-3A.1 Xwmc153–Xgwm155 278.1 276.1–278.1 1.14 0 15.99

R2 phenotypic variation explained by individual QTL

a additive effect

324 Euphytica (2009) 168:319–329

123

(Table 5). Of the above 7 Q 9 Q interactions (Fig. 2),

five (QPhs.ccsu-2A.1 9 QPhs.ccsu-4D.2, QPhs.ccsu-2A.

2 9 QPhs.ccsu-4D.2, QPhs.ccsu-2B.3 9 QPhs.ccsu-

4A.1, QPhs.ccsu-7A.1 9 QPhs.ccsu-7B.1 and QPhs.

ccsu-7A.1 9 QPhs.ccsu-7B.2) showed positive addi-

tive 9 additive effect and in the remaining two

(QPhs.ccsu-2D.3 9 QPhs.ccsu-4D.1 and QPhs.ccsu-

4B.1 9 QPhs.ccsu-6D.1) the effect was negative.

None of these M-QTL and E-QTL was involved in

QE and QQE interactions across the environments.

Discussion

Means and variation for PHS

The skewed distribution of RILs towards PHS

tolerance suggested non-uniform distribution and

abundance of loci controlling PHS. The positive

and significant rank correlations of PHS of RILs in

paired environments also provided evidence for

predictable response of RILs to g 9 e interactions

as reported in earlier studies (Kulwal et al. 2005;

Kumar et al. 2006).

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Fig. 2 Twelve bread wheat chromosomes showing locations

of M-QTL and E-QTL and the Q 9 Q interactions; QTL

showing epistatic interactions are connected through dottedlines. j, M-QTL detected through CIM; d, M-QTL detected

through QTLNetwork; m, E-QTL detected through

QTLNetwork

Euphytica (2009) 168:319–329 325

123

Genetic map

The framework linkage map constructed during the

present study is a new intervarietal map based on a

cross between SPR8198 and HD2329. The map had a

total length of 3,972 cM, which is not very different

from the size of the earlier known maps including

ITMI population map (*3,700 cM; Roder et al.

1998; see Gupta et al. 1999 for review), Cour-

tot 9 Chinese Spring map (3,685 cM; Sourdille et al.

2003), Chinese Spring 9 SQ1 map (3,522 cM, Quar-

rie et al. 2005), and Fukuho-komugi 9 Oligoculm

map (3,948 cM, Suenaga et al. 2005). The excess

map length in the present study was attributed to the

presence of gaps ([45 cM) in the map. The order of

common markers along our maps is consistent with

the SSR map reported by Somers et al. (2004) with

only few exceptions. The genetic map constructed as

above was used for interval mapping of QTL

involved in PHS.

QTL analysis for PHS

For genetic studies on PHS, this trait has been treated

either as a qualitative trait controlled by only one

gene or two genes, as was initially done by us (Roy

et al. 1999), or as a quantitative trait controlled by

many genes, as done by us in subsequent studies (for

details see Kulwal et al. 2004). It is however certain

now that it is a quantitative trait, so that PHS data

have been recorded on a continuous scale of 1–9

(Anderson et al. 1993; Humphreys and Noll 2002;

Kulwal et al. 2004, 2005) to facilitate QTL interval

mapping for this trait. However, there are also two

alternative parameters [falling number (FN) and a-

amylase activity (Zanetti et al. 2000)] commonly

used for evaluating susceptibility to pre-harvest

sprouting, each having its own limitations. Germina-

tion test used in the present study is often preferred,

since it involves testing intact spikes giving an

estimate of sprouting susceptibility in natural condi-

tions. Seed dormancy measured as germination index

(GI), sprouting index (SI), and visibly sprouted seeds

(VI) (Imtiaz et al. 2008; Ogbonnaya et al. 2008) has

also been used to estimate pre-harvest sprouting

tolerance. However we believe that pre-harvest

sprouting and seed dormancy may not be the same,

and seed dormancy tests should not be used as a

measure for pre-harvest sprouting tolerance.

In the past, several studies have been conducted on

QTL analysis for PHS tolerance/dormancy in wheat

(Roy et al. 1999; Langridge et al. 2001; Jahoor et al.

2004; Li and Gill 2004; Tuberosa and Salvi 2004;

Kulwal et al. 2004, 2005; Mori et al. 2005; Mares

et al. 2005; Tan et al. 2006; Xia et al. 2008; Imtiaz

et al. 2008; Ogbonnaya et al. 2008). QTL analysis of

PHS in the present study is an extension of our earlier

study involving detection of a major QTL for PHS on

chromosome arm 3AL of bread wheat (Kulwal et al.

2005). A whole-genome molecular map constructed

during the present study allowed detection of seven

putative QTL for PHS on chromosome arms 1AS,

2AL, 2DL, 3AL and 3BL (Table 3). Only three of the

seven QTL located on chromosome arms 2AL, 3AL

and 3BL were stable (available in 3–6 environments),

the remaining four QTL on 1AS, 2AL 2DL were

detected in only one of the six different environments

suggesting that these QTL were unstable. Neverthe-

less, identification of several QTL for PHS during the

present study underlined the importance of genome-

wide QTL analysis, since only a solitary QTL

(QPhs.ccsu-3A.1) was earlier reported by us for

PHS while working with individual chromosome 3A

of the mapping population utilized during the present

study (Kulwal et al. 2005). Each of the seven QTL

explained varying proportion of PV (15.2–45.1%).

In earlier studies, QTL for PHS in wheat were

reported on all the 21 different chromosomes (Og-

bonnaya et al. 2008; Chen et al. 2008; Imtiaz et al.

2008, Kulwal et al. 2005, Anderson et al. 1993;

Flintham et al. 2002, Sorrells and Anderson 1996,

Zanetti et al. 2000, Roy et al. 1999, Groos et al. 2002)

and the major QTL for seed dormancy were mapped

to chromosomes 3AS (Osa et al. 2003; Mori et al.

2005) and 4AL (Kato et al. 2001; Noda et al. 2002;

Mares et al. 2005; Torada et al. 2005; Chen et al.

2008). QTL identified on 2AL and 2DL during the

present study are being reported for the first time, and

do not appear to be homoeo-QTL or paralogues.

Each of the three stable QTL for PHS, namely

QPhs.ccsu-2A.5, QPhs.ccsu-3A.1 and QPhs.ccsu-

3B.6 explained significant proportions of the PV

(range 15.22–45.11%) for PHS in individual envi-

ronments, suggesting that these were not minor QTL.

The presence of QTL on short arm of chromosome

1A (QPhs.ccsu-1A.1) was also reported in earlier

studies (Anderson et al.1993, Zanetti et al. 2000,

Knox et al. 2005, Kulwal et al. 2004). Mares et al.

326 Euphytica (2009) 168:319–329

123

(2007) also detected a QTL on chromosome 3BL

close to locus R-B1a, which seems to be associated

with higher expression of genes controlling key

enzymes in the flavonoid pathway. In rice, recently it

was reported that mutation in the genes encoding

major enzymes for the biosynthesis of the carotenoid

precursors of ABA resulted in impairment of carot-

enoid biosynthesis and ABA deficient phenotype,

thus leading to PHS (Fang et al. 2008).

Following two-locus QTL analysis, a total of

4 M-QTL on chromosome arms 2AL, 2BL and 3AL

were identified. Among these four QTL, two QTL on

chromosome arms 2AL and 3AL were also detected

in the same marker intervals using CIM. These two

M-QTL explained high PV, and were therefore

definitive.

Besides, the main-effect QTL, the role of epistasis

(QTL 9 QTL) in controlling quantitative genetic

variation in PHS in wheat and dormancy in rice has

also been demonstrated (Gu et al. 2004; Kulwal et al.

2004; Imtiaz et al. 2008). The phenotypic variation

for PHS due to M-QTL was found to be 37.28% and

that due to E-QTL was 27.03%, suggesting that more

than 50% of the variation for PHST is fixable.

Although, in earlier studies influence of environment

on PHS was suggested, during the present study, no

significant variation in PHS due to QE and QQE was

detected. The present results are thus in agreement

with our earlier results (Kulwal et al. 2004). This

may be due to the fact that the parents did not differ

for the QTL that interact with the environment,

although, the presence of such QTL in wheat genome

should not be ruled out (Gupta et al. 2007). In

another recent study, however, Imtiaz et al. (2008),

while using three environments reported that two out

of three parameters used for evaluating PHS (sprout-

ing index and visibly sprouted seeds) showed no

significant Q 9 E interaction while for the third

parameter (germination index), the Q 9 E interaction

was significant.

From the above results and discussion, we

concluded that the polygenic control of PHST in

bread wheat, involving only a few major QTL and

many QTL with minor effects, limits the chances of

success for improvement of PHST in bread wheat

through classical methods of plant breeding. Hence

molecular markers linked with PHST may be used

in marker-assisted selection (MAS) to accelerate

development of cultivars with high PHST. Some of

the major QTL for PHST identified in the present

study and earlier studies can be used for this

purpose. In our laboratory, using marker assisted

backcrossing programme, efforts are underway to

pyramid the two major PHST QTL, located on 2AL

and 3AL, in the genetic background of HD2329, an

important but PHS susceptible Indian bread wheat

cultivar.

Acknowledgments This work was supported by Department

of Biotechnology (DBT), Government of India, New Delhi and

the Indian National Science Academy (INSA), New Delhi. The

support by the DST through FIST-programme and by the

University Grants Commission (UGC), New Delhi through

SAP-DRS programme is also gratefully acknowledged. Thanks

are also due to Dr. Rajendra Prasad, Scientist, Indian

Agricultural Statistical Research Institute, New Delhi for

statistical analyses and to the Department of Plant Molecular

Biology, University of Delhi South Campus for their help with

ABI 377 DNA sequencer for AFLP/SAMPL genotyping.

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