Background

Pre-harvest sprouting (PHS) in common wheat (Triticum astivum) is characterized by germination of grains in physiologically mature spikes, if subjected to prolonged wet weather conditions before harvest (Sharma et al., 1994; Groos et al., 2002; Kulwal et al., 2011). It is widely known that PHS leads to yield losses and downgrading of grain and seed quality, thus limiting their end use. It is also known that PHS is a complex trait that is affected by several genetic and environmental cues. In many breeding programs, introgression of grain dormancy is the primary target to improve pre-harvest sprouting tolerance (PHST), since seed dormancy is one of the important traits, which may contribute to PHST in wheat. The parameters that have been used as estimates of PHST and dormancy, include germination index (GI), sprouting index (SI), visually sprouted seed (VI), and Hegberg falling number (FN) (Fofana et al., 2009; Imtiaz et al., 2008; Munkvold et al., 2009; Rasul et al., 2009). The first three of these parameters are negatively correlated and the last one (FN) is positively correlated with PHST/dormancy. It is also known that PHST is associated with red grain color (GC) (Nilsson-Ehle, 1914; DePauw and McCaig, 1983; Groos et al., 2002; Fofana et al., 2009), so that GC has also been used as a genetic marker for resistance to PHS (Flintham, 2000), although rare PHS tolerant white-grained wheat genotypes have been obtained both in nature and in the progenies of experimental crosses.

In the past decade, with the availability of high-density linkage maps and with the development of powerful statistical tools for QTL analysis, a number of studies have been conducted to study the genetics of PHST and dormancy in bread wheat, so that these traits are now known to be controlled by a large number of QTLs. Altogether, 30 Studies on QTL analysis for PHST have so far been conducted and ~165 QTLs, spread over all the 21 chromosomes, have been reported (see Mohan et al., 2009 for details). These QTLs include both major and minor QTLs.  However, the QTLs located on chromosomes of homoeologous group 3 (3A, 3B and 3D) and chromosome 4A are considered to be relatively more important (Flintham and Gale, 1996; Bailey et al., 1999; Zanetti et al., 2000; Warner et al., 2000; Watanabe and Ikebata, 2000; Kato et al 2001; Flintham et al., 2002; Himi et al., 2002; Gale et al., 2002; Osa et al., 2003; Kulwal et al., 2004; 2011; Mori et al., 2005; Mohan et al., 2009).

The earlier QTL studies on PHST largely involved the use of a number of bi-parental mapping populations, some of them grown each in more than one environments (for a review, see Mohan et al., 2009; Kulwal et al., 2011). It is also known that the identification of a QTL depends on the genetic background, so that often some of the minor QTLs identified in one genetic background may escape detection in another genetic background, even when the mapping populations are grown under similar experimental conditions (Mares et al., 2005). Further, the use of different parental combinations and/or different environments often resulted in identification of partly or wholly non-overlapping sets of QTLs on the same individual chromosomes (Rong et al., 2007). It may, therefore, be necessary to know whether the QTL for PHST and associated traits (e.g. grain colour) identified in a specific genomic region in one study correspond to those detected in the same genomic region in other studies. This issue can be resolved through meta-QTL analysis (Goffinet and Gerber, 2000), which combines results from several independent studies and allows us to estimate the number of real and stable QTLs for each linkage group separately.

Meta-QTL analysis in wheat has been successfully utilized to detect real QTLs for several specific individual traits including ear emergence (Hanocq et al., 2007; Griffiths et al., 2009), resistance against Fusarium head blight (Haberle et al., 2009; Loffler et al., 2009), plant height (Griffith et al., 2012), grain dietary fiber content (Quraishi et al., 2010), seed size and seed shape (Gegas et al., 2010) and for yield contributing traits (Zhang et al., 2010). Meta-QTL analysis has also been successfully conducted in several other crops like maize (Chardon et al., 2004; Truntzler et al., 2010; Hao et al., 2010; Li et al., 2011; Wang et al., 2006; Coque et al., 2008), cotton (Rong et al., 2007), rice (Ballini et al., 2008; Norton et al., 2008; Khowaja et al., 2009), rapeseed (Shi et al., 2009), potato (Danan et al., 2011), cocoa (Lanaud et al., 2009), soybean (Guo et al., 2006; Sun et al., 2012) and apricot (Marandel et al., 2009).

In the present study, meta-QTL analysis was carried out for PHST and dormancy in common wheat for the first time. For this purpose as many as 30 studies reporting as many as ~165 QTLs for PHST spread over all the 21 wheat chromosomes were available. While selecting the chromosomes and reported QTLs to be used for meta-QTL analysis, only those chromosomes were selected which had at least 10 and up to an optimum of 40 QTLs (Arcade et al., 2004). Using this criterion, only 24 of 30 studies reporting 64 QTLs spread over only four chromosomes (3A, 3B, 3D and 4A) were found suitable to be included in the present study. The results of the study, where the above 64 QTLs for PHST/dormancy could be assigned to 8 meta-QTLs are reported in this communication.

1 Results

1.1 Bibliography search involving QTL analysis for PHST

A bibliography search for QTL studies on PHST and dormancy in common wheat was first conducted. In 30 studies involving QTL analysis for PHST in bread wheat, 35 mapping populations were used, leading to detection of as many as ~ 165 QTLs, which included QTLs for the two related traits PHST and dormancy that were estimated using a variety of different parameters. When ~165 QTLs (spread over 21 chromosomes) were arranged in 7 homoeologous groups and 3 genomes, significant differences were observed not only among different homoeologous groups, but also among chromosomes within a group (Figure 1). Group 3 carried the largest number of QTLs (32.31%), followed in order by group 4 (26.21%), group 2 (14.00 %), group 5 (8.53%), group 1 (7.93%), group 7 (6.75%) and group 6 (4.26%).  Approximately 43% of the QTLs were mapped on A genome, 34% on B genome and 23% on D genome.

1.2 Selection of QTLs for meta-QTL analysis

As many as 30 studies reporting as many as ~165 QTLs for PHST spread over all the 21 wheat chromosomes were available, but only 24 of these studies reporting 64 QTLs spread over only four chromosomes (3A, 3B, 3D and 4A) were found suitable to be included in the present study on meta-QTL analysis. The remaining 17 chromosomes did not have adequate number of reported QTLs for meta-QTL analysis. The 64 QTLs distributed on these four chromosomes belonged to two groups, including those detected using level of PHST as a trait, and those detected on the basis of level of dormancy. Even within these two groups, different parameters were used for the detection of QTLs. For instance, PHST was measured using one of the following two parameters, (i) sprouting index (SI), which was based on counting sprouted seeds per spike using either a specific formula as done in two studies (McMaster and Derera, 1976; Towenley-Smith and Czarnecki, 2008) or using a 0-9 scale visually as done in our own laboratory (Kulwal et al., 2004); (ii) visual sprouting seeds (VI), which was based on the number of germinated seeds per 200 seeds. Dormancy was estimated in most studies using germination index (GI), using the formula used in earlier studies (Walker-Simmons and Ried, 1988; Reddy et al., 1985).

A summary of QTL studies that were used for meta-QTL analysis in the present study is presented in Table 1. Out of 64 QTLs that were initially selected for meta-QTL analysis, the available information for meta-QTL analysis for five QTLs was inadequate thus reducing the number of QTLs to 59. Nine other QTLs, each showing association with only one common marker were also excluded, thus reducing further the number to only 50 QTLs that were projected on consensus maps (see next section). Each QTL is characterized by its map position [most likely position and confidence interval (C.I.) around this position], LOD value and the proportion of phenotypic variance explained (PVE, estimated through value of R²). Whenever the required information about position and R² value for the QTLs was not available from a particular study, the most likely position of QTLs was determined as the middle point of the distance between the two flanking markers, and the R² value of closest flanking marker was taken as the R2 value of the QTL.

1.3 Development of a consensus map for QTL projection

In bread wheat, a number of framework genetic maps are available, one each for an individual mapping population that was used for QTL interval mapping in a particular study. However, the number of markers common among different individual maps that were used in the present study were not adequate for construction of a consensus map and reliable projection of QTL positions. Therefore, for developing a consensus map, a pre-consensus map was first created by integrating two available saturated genetic maps including the most recent Somers’ consensus SSR map (Somers et al., 2004) consisting of 1,235 markers and another composite map consisting of 4,506 markers, which was also produced in 2004 (http://wheat.pw.usda.gov).

The pre-consensus map developed as above was used for developing a consensus map using 15 of the 24 studies involving QTL analysis for PHST/dormancy, since only these carried sufficient information for construction of consensus map and for meta-QTL analysis. Out of these 15 studies, three individual studies (Fofana et al., 2009; Imtiaz et al., 2008; Groos et al., 2002) also reported QTLs for grain colour (GC), closely associated with QTLs for PHST. Following criteria were used for construction of consensus map using framework maps from 15 studies: (1) A chromosome in a framework map having no more than one common marker in the corresponding chromosome of the pre-consensus map was excluded. (2) Inversion of marker order was filtered out by discarding inconsistent loci with the exception of very closely linked markers. If two or more markers in a map are available in inverted orientation relative to pre-consensus map, then one of the two closest markers available in inverted order and separated by a distance of less than 1 cM, was dropped to retain a maximum number of common markers. (3) When all the common markers were in reverse order with respect to the pre-consensus map, we used inverted genetic map for projection. In all other situations, QTLs were not projected.

1.4 Overview of QTLs for PHST and related traits

The overview statistics of QTL repartitioning along the four chromosomes were also obtained (Figure 2). Density curves had 24 peaks that exceeded the average value U(x), suggesting that several hot-spots are present on these four chromosomes which may be involved in PHST. On chromosome 3D and 4A some of the adjoining peaks were very close to each other. At nine regions, the curve shows sharp peaks particularly with high values of P(x) [exceeding the H(x) value; 5 times of the average value]. These nine sharp and high peaks include one on chromosome 3B, two each on 3A and 3D and four on 4A. The results thus confirmed the MQTLs detected through meta-QTL analysis. A representative example of the results of overview of QTL repartitioning involving chromosome 3A, is presented in Figure 3, where two MQTLs (MQTL 1 and 2) positioned at 18.46 cM and 96.48 cM  (Figure 3) were confirmed by two separate peaks at the corresponding positions. These peaks exceed the average U(x) value, thus confirming the two MQTLs detected through meta-analysis.

1.5 Meta-QTL analysis

Genomic regions identified through overview were than analyzed for the presence of true QTLs for PHST/dormancy using another approach, meta-QTL analysis. Using this approach, out of 24 regions with peaks, in 8 genomic regions meta-QTLs were identified, either at same or nearby location (Figure 2). In this process 50 QTLs that were found suitable for meta-QTL analysis were projected on the consensus map for four chromosomes selected for the present study. A total of 8 MQTLs (designated as MQTL1 to MQTL8) were identified using only 36 of the 50 QTLs that were projected (Table 2), thus leaving out 14 QTLs, which could not be condensed further using meta-QTL analysis. These 36 QTLs were integrated into 8 MQTLs, an individual MQTL resulting due to integration of 2 to 11 QTLs. Out of 8 MQTLs, 7 MQTLs were located on chromosomes of homoeologous group 3 including 3A (2 MQTL), 3B (3 MQTL) and 3D (2 MQTL) and 1 MQTL was located on chromosome 4A. The integrated QTL maps of four individual chromosomes are presented in Figure 4. It may be noticed that while some individual QTL clusters (condensed into MQTL) had original QTLs either only for PHST or only for dormancy, there were others, which included original QTLs for PHST as well as dormancy, and still others QTL clusters had original QTLs for all three traits (PHST, dormancy and GC). For example, on chromosome 3B, 3 QTLs for PHST clustered at 85.27 cM (MQTL 4) and 2 QTLs for dormancy clustered at 68.93 cM (MQTL 3). For MQTL 8 on chromosome 4 A at 75.75 cM, the 11 original QTLs that clustered together included QTLs for both PHST and dormancy. In contrast, MQTL 5 at 96.41 cM was based on three original QTLs for three original traits (PHST, dormancy and GC). The results involving MQTLs mapped on different individual chromosomes are described below in greater details.

1.5.1 Seven MQTLs on chromosomes 3A, 3B and 3D

On chromosome 3A, 11 original QTLs were projected, but only 8 of these 11 QTLs clustered as 2 MQTLs (MQTL 1 and 2), positioned at 18.46 cM and 96.48 cM. None of the remaining three original QTLs (58.1 cM, 81.03 cM and 119.49 cM) clustered with any other QTL; these were, therefore, excluded from meta-QTL analysis. Each of the two MQTL was based on four original QTLs from 6 different studies, involving PHST, GC and dormancy QTLs. In case of MQTL 1, three of the four original QTLs belonged to seed dormancy, suggesting that this locus may have an important effect on seed dormancy in bread wheat. The other MQTL (MQTL2) positioned at 96.48 cM may control two associated traits (PHST and GC), since the original QTLs for these two associated traits clustered together to generate this MQTL.

On chromosome 3B, 8 of the 10 original QTLs were found to represent 3 MQTLs, which were designated as MQTL 3, 4 and 5 and positioned at 68.93 cM, 85.27 cM and 96.41 cM. MQTL 3 included two original seed dormancy QTLs, while MQTL 4 included three original QTLs for PHST. As mentioned above, MQTL 5 was based on three original QTLs for three traits (PHST, dormancy and GC) from two separate studies.

On chromosome 3D, 9 of the 14 original QTLs represented only 2 MQTLs (MQTL 6 and 7) that were mapped at 29.36 cM and 43.71 cM. MQTL 6 was based on 3 original QTLs from two individual studies, while MQTL 7 with a narrow window of 0.49 cM involved clustering of 6 original QTLs (4 for PHST and 2 for dormancy) from three different studies.

1.5.2 A solitary MQTL on chromosome 4A

 A solitary MQTL designated as MQTL 8 was mapped at 75.75 cM with a narrow CI of 0.40 cM on chromosome 4A, and involved 11 original QTLs (7 QTL for PHST and 4 QTL for dormancy).

1.6 Confidence intervals (CIs) in meta-QTLs and original QTLs

The confidence intervals for MQTLs were narrow (mean: 2.67 cM; range 0.40 to 6.55 cM) than those for the original QTLs (mean: 23.56 cM; range- 10.0 to 53.6 cM; for details see Table 2). MQTLs with the most precise and narrow CI of 0.40, 0.49, 2.32, 2.36 and 2.61 were located on chromosomes 3B, 3D and 4A. With respect to the reduction in size from mean initial CI to MQTL CI, the gain in accuracy in terms of coefficient of reduction (Mean initial CI/ MQTL CI) ranged from 2.7 (3A; MQTL 1) to 55.3 (3D; MQTL 7). An average decrease in CI ranged from 19.6 cM to 5.0 cM on chromosome 3A, from 32.5 cM to 2.43 cM on chromosome 3B, from 18.5 to 2.0 cM for chromosome 3D and from 14.88 cM to 0.40 cM for chromosome 4A. On an average, meta-QTL analysis led to reduction in the size of CI by a factor of 8.8 and thus increased the precision of QTL mapping.

2 Discussion

2.1 Markers for MAS based on MQTLs

It may be recalled that during the present study 36 QTLs were condensed into 8 MQTLs. Each of these 8 MQTL had a relatively narrow confidence interval (CI), thus providing markers that are more closely associated with the corresponding MQTL. Since these MQTLs are based on different original QTLs reported in different environments, it is also suggested that MQTLs may be more stable across different environments and are therefore more suitable not only for MAS, but also for searching candidate genes or for map-based cloning. It should be noted that at least one of the flanking SSR markers for each of the eight meta-QTLs was also reported as the closest marker to QTLs reported in one or more of the initial studies. For instance, MQTL 1 (chromosome 3A) was located within the same marker interval (see Table 2), to which one of the 3A QTL reported earlier belonged (Liu et al., 2008, 2010). Similarly, MQTL 3 (chromosome 3B) was located in the same interval (see Table 2), in which one of the 3B QTL for dormancy was reported earlier (Mares et al., 2009).

2.2 Homoeology between MQTL on 3A, 3B and 3D

Since three of the four chromosomes used for meta-QTL analysis belonged to the same homoeologous group, one would expect that there may be homoeologous relationship between MQTL mapped on these three homoeologous chromosomes. In this connection, it is interesting to note that at least one MQTL on chromosome 3A (MQTL2) is homoeologous to one MQTL each on chromosome 3B (MQTL5) and chromosome 3D (MQTL7). The proportional comparative positions of these three MQTLs (MQTL2, MQTL5 and MQTL7) on the  consensus map (of genetic length: 3A; 173 cM, 3B; 164 cM and 3D; 79 cM) are so close (MQTL 2 located at 55.76% of chromosome length, MQTL 5 at 58.78% of chromosome length and MQTL 7 at 55.32% of chromosome length) that these may certainly be homoeo-triplicate MQTLs, if the differences in the genetic length of these chromosomes is due to random insertions or deletions. This is further substantiated by the fact that MQTL2 resulted from condensation of QTLs for PHST and grain colour, MQTL5 resulted from condensation of three QTLs, one each for PHST, dormancy and grain colour, while MQTL 7 resulted from condensation of QTLs for PHST and dormancy. These triplicate QTLs may therefore, represent a complex locus controlling all the three traits (PHST, dormancy and grain colour). Further studies may be conducted to verify this observation. The remaining four MQTLs belonging to the homoeologous group 3 do not show any similarity in their position on homoeologous chromosomes on the consensus genetic map. It is possible that the homoeologues for these four MQTLs do occur, but have not been identified so far. Similar homoeologous MQTL may also be detected in other homoeologous groups in future studies.

2.3 MQTLs for PHST versus QTLs for PHST, dormancy and grain colour

It is known that QTL that were condensed into meta-QTL for PHST and dormancy were detected using different parameters including sprouting index (SI), visually sprouted seeds (VI) and falling number (FN) for PHST, and germination index (GI) for dormancy. Another associated trait was grain colour, for which no specific parameter was required. A critical analysis would suggest that, some individual MQTLs resulted from condensation of QTLs for the same trait/parameter, while others resulted from condensation of QTLs for more than one traits/parameters. For instance, MQTL3 resulted from two original QTLs, both for seed dormancy (Mares et al.,2009; Kottearachchi et al., 2008), and MQTL4 resulted from three original QTLs, each for PHST (Kulwal et al., 2004; Mohan et al., 2009; Groos et al., 2002). This suggests that perhaps PHST and dormancy may be controlled by at least some independent genes, although there may be other loci carrying pleiotropic or closely linked genes, thus influencing both these traits (PHST and dormancy). Such a conclusion is further substantiated by the observation that the remaining six MQTLs (MQTL1, 2, 5, 6, 7, 8) resulted due to condensation of QTLs for PHST as well as those for dormancy and/or grain colour. MQTL1 resulted from condensation of four QTLs including one for PHST and three for dormancy, MQTL 2 resulted from condensation of QTLs for both PHST and grain colour (GC), MQTL5 resulted from condensation of QTLs for three traits, namely PHST, dormancy and grain colour, MQTL6 resulted from 3 QTLs including one QTL for PHST and 2 QTLs for dormancy, MQTL7 was based on 5 QTL for PHST and one for dormancy and finally MQTL8 resulted from condensation of 7 QTLs for PHST and 4 QTLs for dormancy. Therefore, it is obvious that in majority of cases either QTLs for PHST and dormancy (e.g. MQTL1, 6, 7 and 8), or those for PHST and grain colour were co-localized (e.g., MQTL2), although rarely, QTL for all the three traits were also co- localized (e.g., MQTL5). However, since PHS tolerant genotypes with amber colour have been obtained (CN19055: Hucl and Matus-Cadiz, 2002; AUS1408: Mares, 1987; AUS26906: Zou, 1999, Clark’s Cream: Bhatt et al., 1981), it is obvious that QTLs for PHST and GC may be closely linked rather than pleiotropic in nature.

2.4 An important MQTL on 4A

Since there is a solitary MQTL (MQTL8) on chromosome 4A, which is based on condensation of 11 original QTLs (maximum number for any MQTL), and since it is known that no QTL/gene for red grain colour is located on chromosome 4A, it is obvious that this QTL is important for developing white-grained PHS tolerant wheat genotypes. This important QTL has already been shown to be responsible for some white-grained PHS tolerant genotypes in common wheat (CN19055: Ogbonnaya et al., 2008; AUS1408: Mares et al., 2005). We also know that MQTL8 involved 7 original QTL for PHST and 4 original QTL for dormancy. Therefore, it is apparent that dormancy may affect PHS tolerance and vice versa. From information available in Table 2, it is also obvious that MQTL8 has the highest R² value explaining more than 25% of the phenotypic variation (with a range of 8.0-45.10), suggesting that this is a major QTL and can be exploited for marker-assisted selection (MAS).

2.5 Important genomic region/QTL for PHST/dormancy on other chromosomes

During last two decades, QTL (including both minor and major QTL) for PHST have been mapped on all wheat chromosomes, although most of these are minor QTL with low PV. For instance, chromosome of homeologous group 3 (3A, 3B and 3D) and chromosome 4A have been shown in several studies to carry major QTL, although some major QTL have also been identified on other wheat chromosomes including the following: 1A (Singh et al., 2010); 2B (Munkvold et al., 2009); 4B, 7D (Rasul et al., 2009); 5D (Fofana et al., 2009) and 7A (Singh et al., 2010). These QTLs are also notable because these are independent of seed coat colour and can be utilized in breeding program with a purpose to develop PHS tolerant genotype with white seed coat colour. Despite this, these chromosomes could not be included during present study, since adequate number of QTLs on each of these chromosomes was not available. In future the availability of more QTLs for PHST/dormancy on these chromosomes may facilitate meta-analysis to identify important genomic regions for PHST/dormancy on these chromosomes.

2.6 Relationship between PHST and dormancy

PHST and seed dormancy are two related, but perhaps independent economic traits, Physiology and biochemistry of these traits has been studied in the past, but the biochemical pathways involved are not fully understood (Kulwal et al., 2011). In earlier studies, grain dormancy has been shown to contribute to enhanced resistance to pre-harvest sprouting (Mares et al., 2009; Osa et al., 2003; Nakamura et al., 2007; Lohwasser et al., 2005; Torada et al., 2005; Ogbonnaya et al., 2008; Singh et al., 2010; Chen et al., 2008; Mares et al., 2005; Kottearachchi et al., 2008; Imtiaz et al., 2008; Munkvold et al., 2009; Rasul et al., 2009; Fofana et al., 2009). This observation is confirmed by the results of the present study showing clustering of individual original QTLs for PHST and grain dormancy into a single cluster during meta-QTL analysis. QTLs for PHST measured as sprouting index (Rasul et al., 2009; Fofana et al., 2009; Imtiaz et al., 2008), visually sprouted seed (Imtiaz et al., 2008), GI (Imtiaz et al., 2008; Munkvold et al., 2009; Rasul et al., 2009; Fofana et al., 2009) and the associated trait grain colour (Groos et al., 2002; Fofana et al., 2009) were also clustered together and represented different MQTLs. As mentioned earlier, this is either due to a pleiotropic effect of individual QTL or due to occurrence of closely linked genes for different traits.

2.7 PHST and grain colour

The demand for white-grain wheat has increased in many domestic and international markets, particularly in Southeast Asia and the Middle East, Africa and North America (Ambalamaatil et al., 2006). However, red-grain wheat appeared to be more tolerant to PHS than white-grain wheat genotypes and/or is associated with high seed dormancy. However, rarely red-grain commercial cultivars have been found to be pre-harvest susceptible and some white-grain aestivum and durum wheat have been found to be PHS tolerant (Mares and Ellison, 1990; McCaig and DePauw, 1992; Clarke et al., 1994) suggesting no absolute association between PHST and red grain colour.

The genetic dissection of PHST through QTL analysis also led to successful use of markers linked with PHST in backcross breeding for improvement of PHST in common wheat (Kumar et al., 2010). However derived lines produced using MAS in these studies were red-grained. Therefore, efforts in our laboratory are currently underway for production PHS tolerant wheat genotypes with amber grain. 

2.8 Candidate genes for dormancy/PHST and MQTLs

The genomic regions containing MQTLs were also examined for the presence of candidate genes for PHST and dormancy. It is known that wheat group 3 chromosomes, which are the largest in physical size (Dvorak et al., 1984; Gill et al., 1991) are also the most conserved in gene content and order (Munkvold et al., 2004). This group also shared synteny with barley (Hordeum vulgare L.) chromosome 3H (Devos and Gale, 1993; Nelson et al., 1995), rye (Secale cereale L.) chromosome 3R (Devos et al., 1992), rice (Oryza sativa L.) chromosome 1 (Devos et al., 1992; Ahn and Tanksley,  1993; Kurata et al., 1994; Van Deynze et al., 1995b), maize (Zea mays L.) chromosomes 3 and 8 (Van Deynze et al., 1995b; Wilson et al., 1999), sorghum (Sorghum bicolor L.) chromosome 3 (Whitkus et al., 1992; Klein et al., 2003), and diploid oat (Avena spp.) chromosomes C and G (Van Deynze et al., 1995a). At least 38 genes affecting morphological and biochemical traits are located (Munkvold et al., 2004) on this group. Among the more important genes, Vp1 (VIVIPAROUS-1: dormancy-related) orthologues have been cloned and sequenced in maize as Vp1 (McCarty et al., 1991), in wheat as taVp1 (Bailey et al., 1999), in rice as osVp1 (Hattori et al., 1994), in wild oat Avena fatua, as afVp1 (Jones et al., 1997), in sorghum as Sbvp1 (Carrari et al., 2001), and in Arabidopsis thaliana as ABI3 (Koornneef et al., 1989).

In wheat taVp1 has been shown to be located on the long arms of group 3 chromosomes (genetic length ~ 97 cM) at a distance of ~ 30 cM from centromere (Bailey et al., 1999). In the present study, chromosomes 3A, 3B and 3D measured ~ 175 cM, ~ 165 cM and ~ 80 cM respectively as against the length of ~ 97 cM that was used for long arm of group 3 chromosomes in Bailey’s study (1999). Comparative study for the position of taVp1 between Bailey’s map and in the consensus map used in the present study indicate that ~ 30 cM distance in the earlier map of group 3 chromosomes corresponded to ~ 99 cM for 3A, ~ 97 cM for 3B  and ~ 44 cM for 3D map used in the present study. Interestingly, we noted that MQTL 2 (located at 96.48 cM), MQTL 5 (located at 96.41 cM) and MQTL 7 (located at 43.71) are located in the same regions, where gene taVp1 is located on chromosome 3AL, 3BL and 3DL respectively, making taVp1 gene as possible candidate gene for PHST/dormancy QTL.

In several earlier studies, QTL for PHST/dormancy have also been detected on wheat chromosome 4AL (Flintham et al., 2002; Kato et al., 2001; Mares and Mrva, 2001; Mares et al., 2005; Chen et al., 2008; Ogbonnaya et al., 2008; Rasul et al., 2009; Nakamura et al., 2007; Singh et al., 2010; Lohwasser et al., 2005; Munkvold et al., 2009; Imtiaz et al., 2008). In wheat the three homoeologues of the GA20-oxidase gene TaGA20-ox1 (involved in gibberellin biosynthesis, controlling PHST/seed dormancy) have also been mapped to chromosomes 5BL, 5DL and 4AL (Appleford et al., 2006). GA20-oxidase gene (encoding gibberellin 20 oxidase), mapped on the long arm (located in bin 4AL5-0.66-0.80) of barley chromosome 5H (Li et al., 2004) has been considered to be a candidate gene for dormancy and /or pre-harvest sprouting tolerance (PHST). This region showed good synteny with terminal end of long arm of rice chromosome 3 and with the telomeric region of wheat chromosome 4AL. However, this region was located outside the QTL reported for seed dormancy in wheat on 4AL (Li et al., 2004). On the basis of available markers, the most likely position of the QTL for PHST/seed dormancy was identified in bin 4AL12-0.43-0.59 on 4AL (Li et al., 2004). When we compare this region with the 4AL dense map of present study, it was found that MQTL 8 (located at 75.75 cM) was present in the same region, thus suggesting the possibility of GA20-oxidase gene (TaGA20-ox1) to be a candidate for PHST/seed dormancy on 4AL. However further work is needed to investigate the potential relationship of taVp1 with MQTLs 2, 3 and 7 on long arm of group 3 chromosomes and of TaGA20-ox1 with MQTL 8 on long arm of 4A chromosome.

2.9 Genetic architecture of PHST and utility of MQTLs for breeders

On the basis of meta-QTL analysis conducted during the present study, the genetic architecture of PHST in hexaploid wheat can be described as follows: (i) a large number of QTLs on all the 21 wheat chromosomes control the quantitative trait PHST, (ii) different PHS tolerant genotypes in wheat may carry different sets of QTLs, (iii) only a few QTLs have large effects; most other QTLs have small effects that are prone to genotype × environment interactions.

The present study also supports the view that PHST is affected by the cumulative effect of QTLs for PHST and its associated traits GC and dormancy. Therefore, a breeder will have to select appropriate MQTLs for MAS to improve PHST. For this purpose, a breeder may select one or more MQTLs (for PHST with or without one or more other associated trait), which resulted from a large number of original QTLs and has a narrow CI. Keeping this in view, all 8 MQTLs (excluding MQTL3 for seed dormancy) are interesting and markers associated with these MQTL may be used for marker-aided introgression of independent QTL and MQTLs in any adaptive genetic background for development of PHS tolerant wheat cultivars.

3 Material and methods

Following four steps were involved in meta-QTL analysis. First, bibliographic review was done to collect data related to the mapped QTLs; second, a consensus map of individual chromosomes based on all the markers available in the framework maps used in earlier studies was constructed and the reported QTLs were projected on to these consensus maps; third, an overview was computed to identify the genomic regions carrying all the QTLs in an individual experiment and fourth meta-QTL analysis was conducted to identify true and reliable QTLs based on QTLs reported earlier. Methods involved in these four individual steps are described.

3.1 Bibliographic review and selection of QTL studies for meta-QTL analysis

During bibliographic review, information on genetic maps and details about QTL (including QTL positions, CIs, R² and LOD values) were collected from 24 independent studies involving QTL analysis for PHST, dormancy and the associated trait grain colour. We know that CIs and R² values for all the QTLs are needed for meta-QTL analysis, but in some studies, CIs for individual QTLs are not available. For this purpose, and to have uniformity in data used for meta-QTL analysis, a 5% confidence interval (CI) was worked out for each reported QTL using the following formula (Darvasi and Soller, 1997):

Where 530 is a constant value obtained from simulations, N is the size of population and R² is the proportion of phenotypic variance explained (PVE) by the QTL.

3.2 Construction of consensus maps and QTL projection

In earlier studies involving QTL analysis for PHST/dormancy in bread wheat, a number of framework genetic maps (each with 4 to 25 markers) were utilized, one each for an individual mapping population that was used for QTL interval mapping in a particular study. However, the mapped markers on these different maps differ, and not many markers are common to all maps. Also raw data for marker genotyping is seldom available through literature or databases. Therefore, two relatively dense reference maps published earlier and the available framework maps were used for developing a consensus map, which carried maximum number of markers from each of the framework map. Both the reference maps used in the present study were developed in 2004, one belonging to Somers et al., (2004) and the other being the wheat composite map (2004), available at GrainGene 2.0. Together, the two maps had a much larger number of markers than an individual reference map. Therefore these two maps were first integrated to provide a pre-consensus map, which was then used for developing a consensus map, where it was possible to project all the QTLs reported in 15 earlier studies (for details see Table 1) to facilitate meta-QTL analysis.

Computation for developing a consensus map was performed using BioMercator 2.1 (www.genoplante.org) applying a weighted least square method (Arcade et al., 2004). This allowed arrangement of markers in a linear order, and positioning of these markers on the consensus map. BioMercator 2.1 facilitates projection of the mapped markers included in the framework maps and also the corresponding reported QTLs on to the pre-consensus map prepared as above, so that the loci which were present in a framework map and absent in the pre-consensus map were added, keeping in view the linear order of loci that were common between a framework map and the pre-consensus map. In this process, common loci that occurred in an inverted order with respect to each other are discarded. For each interval flanked by common markers, a specific distance ratio was computed (using interval lengths in the framework map and pre-consensus map) and used for determining the position of uncommon loci (present between the two common flanking loci) that were added from the framework map on to the consensus map. For placement of other uncommon loci that were located above the first interval of common markers and below the last interval of common markers, a global ratio was similarly computed (Arcade et al., 2004). Lastly, the remaining uncommon loci of a framework map and all the QTLs were positioned onto the consensus map by means of homothetic function (monotonic transformation of homogeneous function), using common markers between framework maps and the consensus map. The details of the procedure followed for projection are described elsewhere (Chardon et al., 2004). Fifty QTLs from 15 different studies were retained for map projection: 27 QTLs were PHST QTLs (detected using two parameters, SI and VI), 18 QTLs were dormancy QTLs (detected using the parameter, GI) and the remaining 5 QTLs were for associated trait grain colour (GC).

3.3 QTL refinement (overview)

QTL refinement was achieved for 50 QTLs through estimation of overview, which is a statistic, used for estimating the probability that a given genomic region comprises a QTL in an individual experiment (Chardon et al., 2004). For this purpose, QTL information (most likely position of the QTLs, variance and total number of QTLs identified in all earlier experiments) is utilized to estimate the average value of overview statistic P(x)  for  every 0.5 cM segment on the genetic map (Chardon et al., 2004). In order to achieve this, we assume that the true position of QTL is normally distributed around the most likely position pi of the QTL, with a variance  (). The value of P(x) was estimated using the following formula:

P =

Where nbqtl is the number of QTL and nbE is the total number of experiments. For identifying the high density genomic regions that have a key contribution to the variance of the trait of interest in the experiment [a notable peak with high P(x) value], we also calculated an average value U(x), which is the uniform probability so that a P(x)value above the U(x) value will suggest that the segment flanked by loci at ,  + 0.5 comprises a QTL in an experiment. The value of U(x) was estimated using the following formula:

A high value threshold H(x), which is fivefold the value of U(x) was also calculated to identify the genomic regions carrying a very high P(x) value as done by Chardon et al., (2004). A P(x) peak which exceeds H(x) suggests a much higher level of confidence for the presence of a QTL in the corresponding 0.5 cM genomic region. For each chromosome consensus map P(x), U(x) and H(x) were plotted simultaneously.

3.4 Meta-QTL analysis

Meta-QTL analysis was conducted using BioMercator software following Goffinet and Gerber (2000). This software allows us to find out the number of meta-QTL, which may be 1, 2, 3, 4 or n QTLs (n meta-QTL means, the number of meta-QTL is equal to number of projected QTLs). This is done by selecting one of the five models available in the software, one model for each possibility. A specific model is selected on the basis of minimum value of Akaike Information Criterion (AIC): the lower the AIC value, the more likely the model. The aim of AIC is to estimate the mean log-likelihood (MELL) for the real positions x_i of the n QTLs (Sakamoto et al., 1986). The AIC value is computed using the following formula (Goffinet and Gerber, 2000):

AIC = −2 × L(,  ) + 2×k.

Where L(,  ) denoted the log-likelihood of the observed vector (QTL position), k denoted the actual number of parameters (1,2,3,4…n), the actual value of parameters and X₀ the actual value of the n QTLs positions. k is an unbiased estimator of MELL, therefore it is recommended to choose a model with the minimum value of the Akaike Information Criterion (AIC).

4 Conclusion

The present study for the first time demonstrated the utility of meta-QTL analysis for PHST and dormancy in wheat to identify genuine QTLs and the associated markers for marker-assisted selection (MAS) and map based cloning. Several such studies for other traits in wheat and for a number of traits in other crops have been conducted in the past. Theses studies together with the present study suggest that whenever, hundreds of QTLs based on a large number of studies that involved a number of mapping populations are available in the published literature, meta-QTL analysis can be a useful reductionist approach to bring down the number of genuine and real QTLs to a reasonable number for further study and possible use. The approach also allows narrowing down the confidence interval for each QTL inferred from meta-QTL analysis and overcomes a part of the difficulty and confusion that exist due to redundancy in the number of QTLs in overlapping genomic regions. In particular for PHST and dormancy in wheat, the present study allowed identification of 8 meta-QTLs on four chromosomes (3A, 3B, 3D and 4A), which can be immediately used. Since candidate genes have also been identified, one would need to verify whether or not the candidate genes taVp1 (vivipary gene) and TaGA20-ox1 (gibberellin20- oxidase gene) are really responsible for PHST and dormancy in wheat. Further studies, however, need to be conducted to fully understand the genetic architecture of PHST and dormancy, since the present study could not utilize any QTLs reported on other 17 chromosomes of wheat. Also, no information on epistatic QTLs or on eQTLs and epigenetic modifications was available for the present study, although we know that these may also play an important role in determining the level of PHST in a wheat genotype.   

Authors’ contributions

ST participated in the design of the study, performed analysis and drafted the manuscript. PKG participated in the design and supervision of the study and preparation of the final manuscript. Both the authors have read and approved the final manuscript.

Acknowledgments  

Thanks are due to Professor HS Balyan (Department of Genetics and Plant Breeding, CCS University, Meerut, India) for reading the manuscript and giving useful comments, to Professor  B. Ramesh (Head of the Department of Genetics and Plant Breeding, CCS University, Meerut, India), for providing facilities, to Dr. Fabian Chardon (INRA Centre, Versailles, France) for his help in constructing  overview curve and to Dr. Akshay Pradhan (Department of Genetics, University of Delhi South Campus, India) and Dr. Satish Kumar Yadav (Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, India) for their help in meta-analysis.

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