Functional & Integrative Genomics

, Volume 4, Issue 2, pp 94–101

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

Authors

  • P. L. Kulwal
    • Molecular Biology Laboratory, Department of Genetics and Plant BreedingCh. Charan Singh University
  • R. Singh
    • Molecular Biology Laboratory, Department of Genetics and Plant BreedingCh. Charan Singh University
  • H. S. Balyan
    • Molecular Biology Laboratory, Department of Genetics and Plant BreedingCh. Charan Singh University
    • Molecular Biology Laboratory, Department of Genetics and Plant BreedingCh. Charan Singh University
Original Paper

DOI: 10.1007/s10142-004-0105-2

Cite this article as:
Kulwal, P.L., Singh, R., Balyan, H.S. et al. Funct Integr Genomics (2004) 4: 94. doi:10.1007/s10142-004-0105-2

Abstract

Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL × environment (QE) or QTL × QTL × environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.

Keywords

Pre-harvest sprouting toleranceBread wheatQTL analysisEpistasis

Introduction

In bread wheat, Triticum aestivum L., pre-harvest sprouting (PHS) leads to loss of grain weight and a reduction in the end use quality of kernels. Therefore, it is a major problem during wheat harvesting in different parts of the world including India (Sharma et al. 1994). In view of this, the trait is often the target for improvement of grain quality in this crop. PHS is believed to be due to the early break of dormancy, so that the term PHS tolerance (PHST) is sometimes also used interchangeably with dormancy. Factors derived from the seed coat, having maternal effects, are also known to contribute to grain dormancy and PHST (Gale 1989; Gale and Lenton 1987), and have been shown to be associated with several traits including red kernel colour (Groos et al. 2002; Himi et al. 2002; Kantety et al. 2000; Lawson et al. 1997; Nilsson-Ehle 1914), presence of germination inhibiting compounds in the bracts (Derera and Bhatt 1980) and slower uptake of water (King 1984). Similarly, susceptibility to PHS has also been shown to be dependent on many other traits (Zanetti et al. 2000) including growth habit, days to heading, days to maturity (delayed ripening of grain), etc. It is thus obvious that PHST is a complex trait and its genetics need to be dissected using the modern methods of QTL analysis.

In the past, several studies on PHST or dormancy in bread wheat have been conducted, treating it as a qualitative trait controlled by only one or two genes (Roy et al. 1999; Sharma et al. 1994). For instance, PHST has been shown to be controlled by the Phs locus on chromosome 4A and seems to be associated with triplicate homoeoloci (R) for red grain colour (Flintham 2000; Flintham and Gale 1996). Furthermore, the recently cloned wheat orthologue (TaVp1) of maize dormancy gene Vp1 has been mapped on the long arm of the group 3 chromosomes, midway between the centromere and the R loci (Bailey et al. 1999; Nakamura and Toyoma 2001). It has also been shown that missplicing of the TaVp1 transcripts is responsible for a high level of PHS and that transgenic wheat carrying Avena fatua Vp1 were less susceptible to PHS (McKibbin et al. 2002). However, PHST is now known to be a quantitative trait with several components that are controlled by many genes (Anderson et al. 1993; Flintham et al. 2002; Groos et al. 2002; Kato et al. 2001; Mares and Mrva 2001; Osa et al. 2003; Zanetti et al. 2000). In several studies recently, therefore, PHST data have been recorded on a continuous scale of 1–9 (Anderson et al. 1993; Humphreys and Noll 2002), thus facilitating QTL interval mapping for this trait.

In QTL analysis of a trait like PHST, generally a mapping population is prepared or chosen so that the parents of this population exhibit two extremes of the phenotype. In contrast to this, during the present study on PHST, while there was a narrow range of variability between the parents of the International Triticeae Mapping Initiative population (ITMIpop) that was used, a wide range of variability was available among the recombinant inbred lines (RILs). This suggested that the parents differed for a number of QTLs, each having either a positive or a negative effect on pre-harvest sprouting tolerance. Some of these QTLs presumably neutralized the effects among themselves, thus reducing the range of PHST in the two parents, despite large genotypic differences. This particular feature made ITMIpop a suitable population for genetic dissection of the trait PHST. Recently it has also been recognized that for genetic dissection of a quantitative trait we also need to detect QTLs involved in different interactions [QTLs involved in digenic epistasis (E-QTLs) and QTLs involved in interactions with the environment (QTL × environment: QE; QTL × QTL × environment: QQE)]. This encouraged us to conduct QTL interval mapping for PHST following both the single-locus analysis using composite interval mapping (CIM), via QTL Cartographer, and the two-locus analysis, via QTLMapper, leading to identification of main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs), and the QTLs involved in QE and QQE interactions. Results of this study are presented in this paper.

Materials and methods

Plant material and data recording

Seed of the ITMIpop was procured from Dr. R.P. Singh of CIMMYT (Mexico) and the crop was evaluated at three different locations. There was no rain during the period of harvesting at any of the locations and the mean day/night temperatures during the month of April (the month of harvesting) were 38.9/21.2°C for environment I; 37.1/22.0°C for environment II; 35.0/18.6°C for environment III; and 36.6/18.9°C for environment IV. The details of the ITMIpop that was used in the present study for QTL mapping are described elsewhere (Van Deynze et al. 1995). The parents of this population included (1) W7984, a synthetic wheat, and (2) Opata85, a bread wheat variety. The ITMIpop and the two parental genotypes were evaluated in replicated trials with two replications for 2 years in Meerut and for 1 year each at Pantnagar and Ludhiana, which are the major wheat growing areas of northern India. Thus, the experiment utilized PHST data from four environments. At each location, five spikes from each of the two parents and from each of 110 RILs (per genotype per replication) were harvested as and when they reached physiological maturity, which was characterized by the loss of green colour from the spike (Trethowan 1995). Data on PHS were scored on the scale of 1–9 with a score of 1 for genotypes with no visible sprouting and a score of 9 for the genotypes with complete sprouting (modified after McMaster and Derera 1976). The laboratory test of Baier (1987) was used for this purpose. Within an hour of harvest, the spikes were immersed in water for 4–6 h; after immersion, the spikes were kept in the laboratory at room temperature on a 7.5-cm-thick layer of moist sand covered with a double layer of moist jute bags. The spikes were sprinkled with water every 3–4 h to prevent drying. Observations on sprouting were recorded 10 days after the spikes were harvested and immersed in water.

Rank correlation coefficient analysis

Spearman’s rank correlations for PHS scores of RILs in the four different environments were calculated using MS Excel.

Molecular markers and QTL analysis for PHST

Genotypic data of the ITMIpop on a set of 521 mapped molecular markers with the corresponding genetic distances were reported earlier (Röder et al. 1998; Sorrells and Bermudez 2000) and is available at GrainGenes (http://wheat.pw.usda.gov/ggpages/map_summary.html). Single locus analysis involving estimation of the main effect QTLs was conducted following composite interval mapping (CIM) using QTL Cartographer version 2.0 (Basten et al. 1994, 2002). An LOD score of 3.0 was used for declaring the presence of a putative QTL. The threshold LOD scores for detection of QTLs were also calculated based on 1,000 permutations (Churchill and Doerge 1994; Doerge and Churchill 1996). QTLs detected with an LOD score ≥3.0 but below the threshold LOD score were considered as suggestive. Confidence intervals (CI) were obtained by marking positions ±1 LOD from the peak. More than one QTL with overlapping CI was treated as one QTL. Two-locus QTL analysis was performed using the software QTLMapper version 1.0 (Wang et al. 1999a, 1999b), which allowed estimation of positions and effects of M-QTLs, E-QTLs and those involved in QE or QQE interactions. The QTLs for PHST detected in the present study were designated according to the standard nomenclature for QTLs as recommended for wheat. The QTLs detected in the same or adjacent intervals by the two approaches were given the same designation.

Results

Distribution of mean PHS scores in RILs and correlation between PHS ranks of RILs in different environments

The mean PHS scores for RILs ranged from 3.02 (environment I) to 3.6 (environment II) and followed a normal distribution in only one of the four environments; in the other three environments, the data was skewed towards PHST (Fig. 1). The six possible correlations between the ranks of RILs in the four environments, arranged in pairs, were however positive and significant (Table 1).
Fig. 1

Frequency distribution of the mean pre-harvest sprouting (PHS) scores of 110 recombinant inbred lines (RILs) in four different environments. The mean PHS score of W7984 (P1), Opata85 (P2) and of the RIL population (M), are indicated by arrows

Table 1

Rank correlation coefficients between pre-harvest sprouting scores in four environments

Environmentsa

I

II

III

II

0.717**

III

0.804**

0.713**

IV

0.793**

0.734**

0.815**

** P <0.01

a Environment I = Meerut 2002; II = Meerut 2003; III = Pantnagar 2002; IV = Ludhiana 2002

QTLs resolved by single-locus analysis

Five QTLs with LOD score ranging from 3.06 to 5.60 were detected and mapped; these five QTLs were spread over four chromosome arms (2BL, 2DS, 3BL and 3DL; Table 2, Fig. 2). Three of these QTLs (QPhs.ccsu-2D.1, QPhs.ccsu-3B.4 and QPhs.ccsu-3D.3) were each identified in more than one environment, but none of them could be detected in all four. The total number of QTLs detected in individual environments ranged from 1 (environment III) to 4 (environment I). Three of the five QTLs had negative additive effects, suggesting that the desirable alleles of these QTLs were present in the inferior parent. The threshold LOD scores were 6.40, 3.63, 5.48 and 4.70 for environments I, II, III and IV, respectively. Two of the above five QTLs had LOD scores above the threshold values. Of these two QTLs, one (QPhs.ccsu-3D.3) was detected in environments II and III and the other (QPhs.ccsu-3B.4) was detected only in environment IV. At an LOD score ≥3.0, these two QTLs were also detected in environment I. The phenotypic variation explained (PVE) by individual QTLs (R2) ranged from 8.12% (QPhs.ccsu-3D.3) to 17.39% (QPhs.ccsu-3D.3).
Table 2

Results of composite interval mapping for pre-harvest sprouting tolerance in ITMIpop (environment I Meerut 2002, II Meerut 2003, III Pantnagar 2002, IV Ludhiana 2002; QTL quantitative trait locus, CI confidence interval, R2 phenotypic variation explained by individual QTLs, a additive effect)

Chromosome arm

QTL

Marker intervala

Position

LOD

CIb

R2

a

(cM)

(cM)

(%)

Environment I

2BL

QPhs.ccsu-2B.1

Xbcd1119-Xfbb335 (13)

59.21

4.89

55.0–64.3

16.22

–1.051

2DS

QPhs.ccsu-2D.1

Xgwm261-Xcdo1379 (6)

48.11

4.42

40.4–53.9

12.50

–0.842

3BL

QPhs.ccsu-3B.4

Xcdo1283.2-Xfbb283 (53)

225.31

3.45

222.5–234.8

9.32

0.741

3DL

QPhs.ccsu-3D.3

Xgbx3864-Xwg110 (39)

177.51

3.06

173.9–181.1

8.12

–0.663

Environment II

3BL

QPhs.ccsu-3B.1

XgbxG469-Xfba242 (32)

145.51

3.57

138.4–152.2

12.48

0.674

3DL

QPhs.ccsu-3D.3

Xgbx3864-Xwg110 (39)

177.51

4.16c

174.5–181.3

12.81

–0.843

Environment III

3DL

QPhs.ccsu-3D.3

Xgbx3864-Xwg110 (39)

177.51

5.60c

174.9–178.1

17.39

–1.091

Environment IV

2DS

QPhs.ccsu-2D.1

Xgwm261-Xcdo1379 (6)

44.11

3.80

35.0–55.0

14.96

–0.896

3BL

QPhs.ccsu-3B.4

Xcdo1283.2-Xfbb283 (53)

225.31

4.72c

222.8–227.8

14.81

1.288

a Figures in parentheses indicate the serial number of the interval on the corresponding chromosome

b Confidence intervals were obtained by marking positions ±1 LOD from the peak

c QTLs detected above the threshold LOD score

Fig. 2

Eight wheat chromosomes showing locations of different types of quantitative trait loci (QTLs) and interactions resolved through single-locus and two-locus analyses for pre-harvest sprouting tolerance. QTLs resolved through single-locus analysis following composite interval mapping (CIM) are on the left side, and those resolved through two-locus analysis are on the right side; different types of interactions involving QTLs and the environment are indicated by a symbol and different types of lines as shown in the index in the centre of the figure (C centromere)

QTLs resolved by two-locus analysis

The results of two-locus QTL analysis for PHST are summarized in Tables 3 and 4 and Fig. 2. As can be seen, QTLMapper resolved a total of 14 QTLs including M-QTLs, E-QTLs and QTLs involved in QE and QQE interactions, which were available with LOD scores ranging from 6.74 to 16.35.

Eight M-QTLs on four different chromosomes (2B, 3B, 3D and 6A) together accounted for 47.95% of the total phenotypic variation (Table 3). In three (QPhs.ccsu-2B.1, QPhs.ccsu-3D.3, QPhs.ccsu-6A.1) of these eight QTLs, additive effects were negative. Five of these eight QTLs (QPhs.ccsu-2B.1, QPhs.ccsu-3B.1, QPhs.ccsu-3B.4, QPhs.ccsu-3B.5 and QPhs.ccsu-3D.1) were also involved in digenic epistatic interactions, and two QTLs (QPhs.ccsu-3B.1 and QPhs.ccsu-6A.1) were involved in QQE interactions; none of the eight M-QTLs showed significant QE interaction with any of the four environments. In contrast, one QTL, located on chromosome 5D (QPhs.ccsu-5D.1) had neither any main effect nor any epistatic effect, but was involved in QE interaction (Table 3).
Table 3

Quantitative trait loci (QTLs) with main effects and those involved in interaction with the environment for pre-harvest sprouting tolerance in bread wheat (R2 phenotypic variation explained; a additive effect; ae1, ae2, ae3, ae4 QTL × environment interaction effects for environments 1, 2, 3 and 4, respectively)

QTL

Marker intervala

Position

LOD

a

ae1

ae2

ae3

ae4

R2

(cM)

(%)

QPhs.ccsu-2B.1

Xgwm55.1-Xbcd1119 (12)

56.30

15.28

–1.20

4.02

QPhs.ccsu-3B.1

XgbxG469-Xfba242 (32)

143.50

15.28

1.05

3.06

QPhs.ccsu-3B.2

Xgwm131-Xcdo583 (35)

157.70

9.09

1.10

QPhs.ccsu-3B.4

Xfbb283-XATPase.2 (54)

226.70

7.91

2.67

19.84

QPhs.ccsu-3B.5

Xgwm108-Xfbb378 (64)

275.70

16.35

1.96

10.69

QPhs.ccsu-3D.1

Xfba091-Xfba241 (10)

56.20

16.35

1.60

7.10

QPhs.ccsu-3D.3

Xgbx3864-Xwg110 (39)

177.50

10.56

–1.01

3.24

QPhs.ccsu-6A.1

Xcdo270-Xcdo1315 (4)

52.70

6.74

–0.65

QPhs.ccsu-5D.1

Xbcd1421-Xbcd197 (17)

178.50

10.51

–0.365

a Figures in parentheses indicate the serial number of the interval on the corresponding chromosome

Eight E-QTLs (including five of the above M-QTLs) distributed on four different chromosomes (2B, 3B, 3D, and 5B) were involved in four digenic epistatic interactions (QQ) and accounted for 28.73% of the phenotypic variation (Table 4). In two cases (QPhs.ccsu-3B.3 × QPhs.ccsu-3B.4 and QPhs.ccsu-3B.5 × QPhs.ccsu-3D.1) the additive × additive effect was positive, while in the remaining two cases (QPhs.ccsu-2B.1 × QPhs.ccsu-3B.1, QPhs.ccsu-3D.2 × QPhs.ccsu-5B.1) it was negative. None of these four epistatic combinations exhibited QQE interaction. Four QTLs, each located on a different chromosome (2B, 3B, 6A, and 7B), were involved in two QQE interactions (QPhs.ccsu-2B.2 × QPhs.ccsu-6A.2; QPhs.ccsu-3B.1 × QPhs.ccsu-7B.1; Table 4). These two pairs of QTLs showing QQE interactions accounted for 3.24% of the phenotypic variation; one pair of QTLs exhibited QQE in three environments and the other in only one environment (Table 4).
Table 4

Quantitative trait loci (QTLs) involved in epistasis, and epistasis associated with environmental interaction for pre-harvest sprouting tolerance in bread wheat (R2 phenotypic variation explained by interactions; aaij additive effect; aae1, aae2, aae3, aae4 epistasis associated with environments 1, 2, 3 and 4, respectively)

QTLi

Marker intervala

QTLj

Marker intervala

LOD

aaij

aae1

aae2

aae3

aae4

R2 (%)

QPhs.ccsu-2B.1

Xgwm55.1-Xbcd1119 (12)

QPhs.ccsu-3B.1

XgbxG469-Xfba242 (32)

15.28

–1.32

4.87

QPhs.ccsu-3B.3

Xbcd1418-Xmwg818 (37)

QPhs.ccsu-3B.4

Xfbb283-XATPase.2 (54)

7.91

2.14

12.81

QPhs.ccsu-3B.5

Xgwm108-Xfbb378 (64)

QPhs.ccsu-3D.1

Xfba091-Xfba241 (10)

16.35

1.77

8.81

QPhs.ccsu-3D.2

Xcdo1406-Xbcd288 (27)

QPhs.ccsu-5B.1

Xgwm443-Xgwm544 (2)

8.15

–0.90

2.24

QPhs.ccsu-2B.2

Xmwg546-Xgwm526 (20)

QPhs.ccsu-6A.1

Xcdo270-Xcdo1315 (4)

6.74

0.98

–1.62

0.32

3.24

QPhs.ccsu-3B.1

XgbxG469-Xfba242 (32)

QPhs.ccsu-7B.1

XksuE18-Xgwm68 (16)

10.72

0.87

a Figures in parentheses indicate the serial number of the interval on the corresponding chromosome

Discussion

In recent years, QTL mapping in bread wheat has been conducted for a variety of traits (for review see Gupta et al. 1999; Langridge et al. 2001). In order to reduce the quantum of work, in some of the QTL studies, the available ITMIpop, ITMImap and the associated molecular genotypic data were also utilized (Börner et al. 2002; Faris et al. 1999; Kulwal et al. 2003; Nelson et al. 1998; Sourdille et al. 1996). QTL studies in bread wheat have also been conducted for PHST/dormancy (Anderson et al. 1993; Flintham et al. 2002; Groos et al. 2002; Kato et al. 2001; Mares and Mrva 2001; Osa et al. 2003; Roy et al. 1999; Zanetti et al. 2000), but none of them made use of the ITMIpop. Furthermore, only one study (using wheat × spelt cross) involved interval mapping (CIM) as well as estimation of epistatic effects and QE interactions, although, instead of PHS data, they used data on estimates of falling number and α-amylase activity (Zanetti et al. 2000). Therefore, the present study is the first which involves QTL interval mapping for PHST per se, and makes use of the ITMImap and ITMIpop for detecting not only the M-QTLs, but also the E-QTLs and the QTLs interacting with the environment (QE/QQE).

Parameters for PHST

In the present study, as in several earlier studies, PHS was evaluated using the conventional test simulating field conditions, which involved immersing the spikes in water and then keeping them wet for a duration that would allow sprouting in susceptible material. However, there are other parameters available for evaluating susceptibility to pre-harvest sprouting, each having its own limitations. Two popular alternative parameters commonly used for evaluating PHS are falling number (FN) and α-amylase activity (Zanetti et al. 2000), which have been shown to have high correlation with PHS and therefore can be effectively used for evaluation of PHS. However, these parameters may have no causal relationship with PHS, since FN and α-amylase actually measure the quality of endosperm after sprouting, giving the estimates of damage done to the endosperm due to sprouting rather than measuring PHS per se. In view of several parameters available for measuring PHS, choice of a parameter also depends on the objective of the study. For instance, when evaluation of sprouting susceptibility is the sole objective, a germination test is believed to be better, since it involves testing intact spikes which gives an estimate of sprouting susceptibility in natural conditions (Hagemann and Ciha 1984). The germination test using intact spikes measures the level of seed dormancy and the potential to resist sprouting, and does not measure the existing condition of the endosperm. Therefore, from a breeders’ point of view, the germination test used in the present study proves to be a good criterion for testing sprouting susceptibility.

Single locus QTL analysis

Although in studies like the present one normality of the distribution of phenotype is desirable, the deviation from normality in three of the four environments can be explained by the narrow range of phenotypes available in the parents of the ITMIpop (Fig. 1). Phenotypic variation explained by QTLs identified following CIM during the present study was as much as 46.16% for environment I and 17.39% for environment II. This wide range of variation can be attributed to a failure to detect different types of interactions following CIM. However, these results seem to be in agreement with an earlier study, where a wide range (47–76%) of phenotypic variation (falling number and α-amylase activity as parameters for PHST) was explained by M-QTLs detected over different environments (Zanetti et al. 2000).

Two-locus QTL analysis

Following two-locus analysis in the present study, phenotypic variation for PHST due to M-QTLs was 47.95% and that due to E-QTLs was 28.73%, suggesting that more than three-quarters (76.68%) of the variation for PHST is fixable. Although in earlier studies environment has also been shown to have a major influence on PHS, during the present investigation variation due to QE and QQE is rather low (3.24%), suggesting that the parents did not differ for the QTL that interact with the environment, although such QTLs should be available in the wheat genome. Although these results do not estimate the higher order interaction(s) present, if any, they provide evidence for the importance of E-QTLs, if not of QE and QQE. As much as 20.08% of the variation for PHST still remains unexplained and can be attributed either to higher order interactions or environmental variation (Jannik and Jansen 2001). The presence of these higher order interactions has been documented in the past (Allard 1988, 1999; Alonso-Blanco et al. 1998; Doebley et al. 1995). As mentioned above, it is also possible that some of the QTLs for PHST escaped detection because the alleles for these QTLs did not differ in the two parents of the ITMIpop.

In the present study, following CIM, it may be noticed from Table 2 that QTLs detected above the threshold LOD score had a narrow confidence interval (<10 cM) and a positive relationship with the R2 (%); these QTLs may represent the true positive QTLs [e.g. QTLs QPhs.ccsu-3B.4 (environment IV) and QPhs.ccsu-3D.3 (environments II and III)]. When the results of M-QTLs obtained following both the analyses were compared, four of the five QTLs detected by CIM (QPhs.ccsu-2B.1, QPhs.ccsu-3B.1, QPhs.ccsu-3B.4, QPhs.ccsu-3D.3) had also been detected by two-locus analysis. The minor differences associated with the position and the marker interval of the QTLs may be attributed to the different approaches and software used.

Chromosomes and the markers associated with PHST

In earlier studies carried out in wheat, QTLs for PHST/dormancy were shown to be present on as many as 20 different chromosomes, the only exception being chromosome 1D (Anderson et al. 1993; Flintham et al. 2002; Groos et al. 2002; Kato et al. 2001; Mares and Mrva 2001; Miura et al. 2002; Osa et al. 2003; Roy et al. 1999; Zanetti et al. 2000). The role of chromosomes of group 3 and that of chromosome 4A in PHST has been particularly emphasized in some recent studies (Appels et al. 2003; Bailey et al. 1999; Flintham et al. 2002). In the present study, as many as three QTLs were detected on the long arms of chromosomes 3B and 3D following CIM, which also figured in the eight QTLs detected by two-locus analysis on the above two chromosomes: seven being on 3BL and 3DL, and the remaining one on 3DS (QPhs.ccsu-3D.1). The presence of QTLs on 3BL and 3DL had also been reported earlier (Anderson et al. 1993; Groos et al. 2002; Zanetti et al. 2000). For instance, TaVp1 has been shown to be located ~30 cM away from the centromere on 3L arms (Bailey et al. 1999). However, the maps of group 3 chromosomes used in the present study were dense, each representing a genetic length of ~283 cM and ~209 cM as against a length of ~97 cM each for 3BL and 3DL, respectively, used in Bailey’s study (Bailey et al. 1999). When these two maps were compared and relative positions of the centromere were calculated, it was found that 30 cM distance in the earlier map corresponded to ~88 cM for 3BL and ~65 cM for 3DL used in the present study. When these genetic distances were used, two QTLs were found to be located in the vicinity of TaVp1: QPhs.ccsu-3B.4 and QPhs.ccsu-3D.3. In particular, the marker Xwg110 associated with QPhs.ccsu-3D.3 seems to be associated with TaVp1 (Bailey et al. 1999). However, in the present study no effort was made to study the polymorphism for TaVp1 between the parental genotypes and within the mapping population. However, there are contrasting reports with regard to the role of TaVp1 in imparting dormancy in wheat. For instance, while Wilkinson et al. (2002) considered TaVp1 to be a candidate gene for a QTL for PHST, there are also reports where TaVp1 has been shown not to affect grain dormancy directly (Osa et al. 2003). The QTL QPhs.ccsu-3B.5 (flanked by the markers Xgwm108 and Xfbb378, the latter being adjacent to the marker Xabc174) is located in close proximity to the R gene (Bailey et al. 1999). The markers Xfbb378 and Xabc174 were also found to be associated with grain colour in an earlier study conducted using the same mapping population (Nelson et al. 1995). However, the relationship between PHS and red grain colour could be either due to pleiotropy or to linkage between the R gene and other genes affecting PHS (Groos et al. 2002). Looking into the commercial importance of the white-grained wheats, there is a need to provide white-grain wheats with increased tolerance to PHS. One approach for this is to look for the sprouting tolerant amber-grained recombinants (Sharma et al. 1994). Another important approach should be to target those dormancy genes (e.g. Phs) and/or QTLs which are not associated with the R genes (Flintham 2000). Success in this direction has been reported in the past (Mares 1987; Mares et al. 2002; McCaig and De Pauw 1992; Xiao et al. 2002).

Marker-assisted selection

It has been suggested in the past that QTL(s) which are detected in more than one environment and/or by more than one method of analysis are useful for marker-assisted selection (MAS; Moncada et al. 2001). Therefore, besides other QTLs, the two QTLs QPhs.ccsu-3B.4 and QPhs.ccsu-3D.3 seem to be important since they were detected above the threshold LOD score by both the methods of analysis in more than one environment. The present study also reports the presence of a QTL (QPhs.ccsu-3D.1) on 3DS for the first time. However, QTLs previously mapped on chromosomes 3A and 4A (Kato et al. 2001; Mares and Mrva 2001; Mares et al. 2002; Osa et al. 2003) escaped detection presumably due to the presence of the same QTL alleles in both parents. It is logical to assume that different materials used in different studies would allow detection of different QTLs (Flintham et al. 2002), so the desirable alleles from diverse materials need to be brought together through strategic breeding involving MAS. The negative M-QTL effects and the negative QQ interactions detected in the present study also suggest that a superior allele of a QTL for PHST present in the inferior parent can be transferred to the superior parent through MAS, thus enhancing the level of PHST. Such a proposal also receives support from the belief that PHST is a complex trait controlled by a number of QTLs, so that it may be necessary to combine these QTLs to achieve a high level of PHST (Flintham et al. 2002).

Conclusion

The present study is the first attempt to conduct QTL interval mapping for PHST using ITMIpop elucidating the quantitative genetic nature of the trait following single-locus (5 QTLs) and two-locus (14 QTLs) analyses. Since four of the QTLs were detected by both methods, the number of QTLs detected in the present study was 15. The molecular markers associated with PHST can be used for marker-aided selection along with other markers reported earlier. In bread wheat, crosses can now be planned, and after suitable validation studies, selected molecular markers can be used for MAS for the improvement of PHST along with improvement of other traits of economic value.

Acknowledgements

Financial support from the NATP-ICAR, New Delhi for conducting this study is gratefully acknowledged. The award of Senior Research Fellowship by the Council of Scientific and Industrial Research (CSIR) to P.L.K. and that of a Senior Scientist position by the Indian National Science Academy (INSA) to P.K.G. facilitated this study. Thanks are due to Dr. R.P. Singh of CIMMYT, Mexico for the supply of seeds of ITMIpop, and to Professor Jun Zhu, Zhejiang University, Hangzhou, China for conducting two-locus QTL analysis. We are also grateful to G.B.P.U.A. & T., Pantnagar and P.A.U., Ludhiana, India for providing field facilities and for their help in conducting field trials. Critical reading and useful comments from three anonymous reviewers helped in the improvement of the manuscript.

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© Springer-Verlag 2004