Introduction
Microdochium nivale (Fr.) Samuels & Hallett is the most widespread snow mould fungus. It is a pathogen-caused disease which develops under high humidity and low temperature, and results in major damage to economically important winter cereals and grasses. It is the source of seedling blight, stem rot, leaf blotch and a part of the disease complex called ‘fusarium head blight’. Genetic resistance is the most economical and environmental friendly way to control M. nivale infection. Triticale has been suggested as a source of disease resistance genes since it is a bridging species for the transfer of such genes to wheat and rye (Kuleung et al., Reference Kuleung, Baenziger and Dweikat2004). However, even resistance of the best triticale cultivars is far from satisfactory. According to Nakajima and Abe (Reference Nakajima and Abe1996), Ergon et al. (Reference Ergon, Klemsdal and Tronsmo1998), Tronsmo et al. (Reference Tronsmo, Hsiang, Okuyama, Nakajima, Iriki, Gaudet, Tronsmo, Matsumoto, Yoshida and Nishimune2001), and from our own experiments (Gołębiowska and Wędzony, Reference Gołębiowska and Wędzony2009), cold-hardening is the factor switching on cereal defence responses to M. nivale infection. The mechanisms of plant resistance induced by cold have not yet been recognised and the genotypes differ in their ability to obtain resistance expression (Hömmo, Reference Hömmo1996; Tronsmo, Reference Tronsmo, Doerffling, Brettschneider, Tantau and Pithan1994; Pulli et al., Reference Pulli, Hjortesholm, Larsen, Gudleifsson, Larsson, Kristiansson, Hömmo, Tronsmo, Ruuth and Kristensson1996). Thus, it is important to identify the genetic background and the markers that can be exploited to increase natural resistance via marker-assisted selection. Moreover, the most agronomically important characters in cereals, including resistance, are classified as polygenic or quantitative (Reszka et al., Reference Reszka, Song, Arseniuk, Cregan and Ueng2007; Carter et al., Reference Carter, Chen, Garland-Campbell and Kidwell2009). Therefore, we present the results of studies in which we tried to identify QTLs related to seedling survival and its vitality while recovering from snow mould infection in triticale. The studies exploit the first triticale marker map (paper in preparation) and the mapping doubled haploids (DH) population being developed at SPB Institute (Germany) by Dr. Eva Bauer.
Materials and methods
The mapping population consisting of 90 DH lines was derived by the maize method (Wędzony et al., Reference Wędzony, Marcińska, Ponitka, Ślusarkiewicz-Jarzina and Wozna1998; Wędzony, Reference Wędzony, Maluszynski, Kasha, Forster and Szarejko2003) from triticale ( × Triticosecale Wittmack) F1 generation of ‘Modus’ (partially resistant) × ‘SaKa3006’ (sensitive) (Fig. 1).
Fig. 1 Survival of doubled haploids (DH) lines selected from a triticale mapping population after infection with M. nivale. Parents are indicated with the arrows.
Seedlings were planted in multipots in a randomised complete block design with three replicates. Plants grew in a chamber at 16/12°C, 10/12 h light/darkness for 2 weeks. Then they were subjected to a pre-hardening period (12°C for 7 d) and hardening (2°C for 28 d) in the same light regime. Hardened seedlings were inoculated with soil-borne mycelium of M. nivale (monosporal isolate no. 38z/5a/01). Then the plants were covered with moist paper and black plastic bags to imitate a snow cover and to keep high humidity in darkness at 2°C. The control plants were treated the same way, except for the infection. The covers were removed after 21 d of incubation and the seedlings were moved for 2 weeks to optimal conditions for recovery. Five traits describing seedling survival and vigour were selected for analysis (Table 1).
Table 1 Results of the CIM analysis – detected main QTLs for the studied traits
R 2 (%), % of phenotypic variance explained by the QTL; Add, additive effects of QTLs expressed in the trait unit.
a Position of QTL peak from the first marker in cMorgans.
b A positive value means that the allele from the Modus increases the value of the trait.
QTLs were identified using Windows QTLCartographer 2.5 and the results were analysed using composite interval mapping (CIM). Threshold logarithm of the odds (LOD) scores were calculated based on 1000 permutations. A QTL was accepted when the LOD score was greater than 3.
Results and discussion
Genetic maps are the fundamental tools that identify the features of phenotypes that are linked to specific genetic loci, including those that influence QTLs. Over 40 maps with at least 300 markers have been published for different Triticeae populations; however, to date, only a few rye and one partial triticale map with more than 300 markers have been produced (Gonzalez et al., Reference Gonzalez, Muniz and Jouve2005; Lehmensiek et al., Reference Lehmensiek, Bovill, Wenzl, Langridge and Appels2009). Therefore, reports identifying and mapping QTLs in triticale are limited (Reszka et al., Reference Reszka, Song, Arseniuk, Cregan and Ueng2007; Hura et al., Reference Hura, Hura and Grzesiak2009). In our research, the population of 90 DH lines derived from the F1 hybrid of triticale ‘Modus’ and ‘SaKa3006’ was mapped with 1518 markers selected from a total of 1670 wheat and rye candidate markers: simple sequence repeat, amplified fragment length polymorphism and diversity arrays technology markers. The map spans a distance covering a total of 2197 cM genomic regions with an average interval of 1.44 cM between adjacent marker loci. The highest marker density was established in rye chromosomes 4R, 5R, 6R and 7R.
Resistance to M. nivale showed a wide variation range within the mapping population, 23% of lines had lower survival capability than resistant Magnat, 43% lines did not differ from Magnat and 20% lines fully regenerated after infection. However, CIM approaches, performed for 14 wheat and 7 rye chromosomes, revealed that reaction to snow mould in this population is not determined by ‘plant survival after infection’ because the QTLs were not identified. In the case of plants regenerated after infection, two QTLs were detected on chromosomes 2A and 3B (around the Xbarc212 and wPt-6802 regions, respectively) at a similar position for both traits concerning the number of regenerated leaves (traits 1 and 4, Table 1), with LOD from 3.03 to 6.27. Other QTLs for trait 1 were discovered on chromosome 3B (near wPt-4625) and 5B (near wPt-9872), which explained 9.61 and 10.93% of the phenotypic variation. The QTLs on chromosomes 3B and 5B were consistent with reports of resistance to Stagonospora nodorum blotch disease in triticale and wheat (Reszka et al., Reference Reszka, Song, Arseniuk, Cregan and Ueng2007). The nearby region between the loci Xgwm533.1 and Xgwm493 on chromosome 3B was shown to harbour a QTL related to Fusarium head blight resistance in wheat cultivars Ning894037 and Wangshuibai (Jia et al., Reference Jia, Chen, Qin, Bai, Wang, Wang, Zhou, Zhang and Liu2005; Shen et al., Reference Shen, Francki and Ohm2006). Carter et al. (Reference Carter, Chen, Garland-Campbell and Kidwell2009) mapped the putative QTLs on 3B wheat chromosome, which is significantly associated with resistance to stripe rust. These coincidences suggest the possibility that resistance to certain fungal diseases may be controlled by the same resistance loci in wheat and triticale.
QTL distribution differed for the control plants. One QTL for traits that related the number of leaves was mapped on chromosome 1B with R 2 values of 27.5 (trait 2, Table 1) and 25.8% (trait 5, Table 1). The presence of one QTL on chromosome 7R (near rPt-399750) for trait 2 and two QTLs on chromosomes 5A and 7B for trait 5 were also identified.
QTLs controlling variation in ‘height of regenerated plant’ (traits 6 and 7), each showing significant LOD values (about 3 for control and 6.6 for infected plants), were mapped on wheat chromosome 5A. Another QTL for the trait ‘height of regenerated plant-control’, related rather to seedling morphology than disease resistance, was located on chromosome 7B. Unlike what was reported in rye (Börner et al., Reference Börner, Korzun, Voylokov, Worland and Weber2000), we detected one QTL for height on chromosome 5R, which confirmed that the results obtained depend on the population. Zhang et al. (Reference Zhang, Tian, Zhao and Wang2008) constructed a linkage map for the DH lines of the Huapei3/Yumai57 population and they detected four additive QTLs and five pairs of epistatic effects for plant height on chromosomes 3A, 4B, 4D, 5A, 6A, 7B and 7D. However, within the population of Hanxuan10/Lumai14, three additive QTLs significantly affecting plant height were found on chromosomes 1B, 4D and 5B, and three pairs of epistatic QTLs were found on chromosomes 1B–1B, 2A–2D, 2D–5B (Wang et al., Reference Wang, Wu, Ren, Chang, Li and Jing2010). Candidate QTLs for other assessed components that could be related to resistance in this study were detected on chromosomes 1B, 2A, 2B, 3A, 6A, 6B and 7B (Table 1).
In conclusion, the results were consistent with the observation that plant resistance to M. nivale is polygenic in nature. Principal component analysis showed that 95.7% of the variation can be explained by the interaction of five independent factors. Finally, although the triticale are hexaploids composed of genomes from durum wheat (AABB) and rye (RR), it seems that seedling vitality is mainly controlled by the wheat genome.
Acknowledgements
This work was financed by project COST/254/2006. M. S.-H. is grateful for financial support from projects MERG-CT-2007-207350 and 595/N-COST/2009/0.
