Justus-Liebig-University Giessen Research Center for Biosystems, Land Resources and Nutrition Department of Plant Breeding Head: Prof. Dr. Dr. h.c. Wolfgang Friedt Analysis of Genetic Diversity among Current Spring Wheat Varieties and Breeding for Improved Yield Stability of Wheat (Triticum aestivum L.) Dissertation Submitted for the degree of Doctor of Agricultural Science Faculty of Agricultural and Nutritional Sciences, Home Economics and Environmental Management Justus-Liebig-University Giessen Submitted by Lin Hai from Beijing, P. R. China Giessen, December 2006 Mitglieder der Prüfungskommission: Vorsitzende: Prof. Dr. Dr. Annette Otte Gutachter: Prof. Dr. Dr. h.c. Wolfgang Friedt Gutachter: Prof. Dr. Wolfgang Köhler Prüfer: Prof. Dr. Bernd Honermeier Prüfer: Prof. Dr. Andreas Vilcinskas CONTENTS I 1 INTRODUCTION 1.1 Genetic diversity as a basis of crop improvement 1.2 Evaluation methods of genetic diversity 1.2.1 Coefficient of parentage (COP) 1.2.2 Molecular markers 1.2.2.1 Restriction fragment polymorphisms (RFLPs) 1.2.2.2 Random amplified polymorphic DNAs (RAPDs) 1.2.2.3 Amplified fragment length polymorphisms (AFLPs) 1.2.2.4 Simple sequence repeats (SSRs) 1.2.2.5 Single nucleus polymorphisms (SNPs) 1.3 Lodging, its occurrence and types 1.4 Effects of lodging on yield and quality of cereals 1.5 Factors affect lodging 1.5.1 Plant height 1.5.2 Stem characteristics 1.5.2.1 Morphological characters 1.5.2.2 Anatomical structure 1.5.2.3 Physiological and chemical ingredients 1.6 Evaluation methods and indexes for lodging 1.7 Inheritance mode and chromosomal location of genes related to lodging 1.8 Quantitative trait loci (QTL) mapping 1.8.1 Mapping population 1.8.1.1 F2 population 1.8.1.2 Backcross (BC) population 1.8.1.3 Doubled haploid (DH) population 1.8.1.4 Recombinant inbred (RI) population 1.8.2 Linkage map construction 1.8.3 Statistical methods for QTL mapping 1.8.3.1 Single marker method 1.8.3.2 Simple interval mapping (SIM) method 1.8.3.3 Composite interval mapping (CIM) method 1.8.4 QTL mapping of agronomic traits in wheat 1.8.5 QTL mapping of lodging resistance and related traits in wheat 1 1 2 2 3 4 4 5 6 6 8 9 10 10 11 11 13 14 15 16 16 17 17 17 18 18 19 19 20 21 22 23 23 CONTENTS II 1.9 References 2 OBJECTIVES 3 PUBLICATIONS 3.1 Quantitative structure analysis of genetic diversity among spring bread wheat (Triticum aestivum L.) from different geographical regions 3.1.1 Abstract 3.1.2 Introduction 3.1.3 Materials and methods 3.1.3.1 Plant materials 3.1.3.2 DNA extraction and SSR analysis 3.1.3.3 Statistical analysis 3.1.4 Results 3.1.4.1 SSR polymorphisms and genetic diversity 3.1.4.2 Genetic similarity and relatedness among accessions 3.1.4.3 Relevance of geographical origin for genetic variation 3.1.4.4 Relationship and diversity between six European geographical groups 3.1.5 Discussion 3.1.6 Acknowledgements 3.1.7 References 3.2 Quantitative trait loci (QTL) for stem strength and related traits in a doubled haploid population of wheat (Triticum aestivum L.) 3.2.1 Abstract 3.2.2 Introduction 3.2.3 Materials and methods 3.2.3.1 Plant materials 3.2.3.2 Measurement of stem strength and related basal internode traits 3.2.3.3 SSR analysis 3.2.3.4 Molecular map construction 3.2.3.5 Statistical analysis 25 37 38 38 38 39 40 40 41 43 46 46 48 51 52 54 58 59 63 63 64 66 66 67 68 68 68 CONTENTS III 3.2.4 Results 3.2.4.1 Variation in stem strength and correlation between stem strength and related basal internode traits 3.2.4.2 Molecular map 3.2.4.3 QTL detection 3.2.4.4 Pleiotropic effects 3.2.5 Discussion 3.2.6 Acknowledgements 3.2.7 References 4 DISCUSSION 4.1 Genetic variation in existing gene pools of spring wheat (T. aestivum) 4.2 Exploration of desirable alleles in genetic resources 4.3 Quantification of lodging resistance as a major stability trait of wheat 4.4 QTL mapping of stem strength and perspectives of marker-assisted selection for lodging resistance 4.5 References 5 SUMMARY 6 ZUSAMMENFASSUNG 7 LIST OF FIGURES 8 LIST OF TABLES 9 LIST OF ABBREVIATIONS 10 ACKNOWLEDGEMENTS 11 DECLARATION 69 69 70 71 72 72 75 75 79 79 80 81 82 84 86 89 94 96 97 99 100 INTRODUCTION 1 1 INTRODUCTION 1.1 Genetic diversity as a basis of crop improvement Wheat is one of the most important cereal crops in the world. Its global consumption is close behind rice and maize. With the steadily growth of the world population, the demand for the food production is continually expanding (Lee et al., 1998; Hoisington et al., 1999). Especially, the demand for wheat is expected to increase faster than any other major crop such as rice and maize. To keep pace with the anticipated growth of human population, the predicted demand for the year 2020 varies between 840 (Rosegrant et al., 1995) and 1050 million tons (Kronstad, 1998). Given the fact that much existing arable land is decreasing due to urban and industrial development or natural erosion such as expanding deserts (Reif, 2004), genetic improvement of crops is considered as the most viable and sustainable approach to increase agricultural productivity (Tanksley & McCouch, 1997). Effective crop improvement depends on the extent of genetic diversity in the gene pools. Over the past century, the achievements of plant breeding have contributed a lot to increase crop productivity and needs of societies by systemically genetic improvements with utilization efficiency of agricultural inputs (Warburton et al., 2002). However, these gains have often been accompanied by decreased genetic diversity within elite gene pools (Lee, 1998; Fernie et al., 2006). Although landraces have a diverse genetic base, they are therefore rarely integrated into the plant breeding programs due to their low productive performance. New varieties are usually derived from a set of genetically related modern high-yielding varieties. As a result, many landraces were continually replaced by modern wheat cultivars and crop improvement is still practiced in a narrow genetic base (Fernie et al., 2006). It has been presumed that modern breeding practices with intensive selection leads inevitably to a loss of the genetic diversity in crops (Cluies-Ross, 1995; Tanksley & McCouch, 1997). Such reduction may have serious consequences. The vulnerability of crops against pests and diseases and the ability to respond INTRODUCTION 2 to changes in environmental conditions can be drastically influenced and threaten the sustained genetic improvement (Harlan, 1987; Tripp, 1996; Smale, 1996; FAO, 1996; Donini et al., 2000). This risk was brought sharply into focus in 1970 with the outbreak of Southern corn leaf blight (National Research Council, 1972). This disease drastically reduced corn yields in the United States due to the extensive use of a single genetic male sterility cytoplasm, which was associated with disease susceptibility. Other several server evidences occurred in India also in 1970s like epidemics of shoot fly (Atherigona spp.) and karnal bunt (Tilletia indica) (Dalrymple, 1986). Reduction in diversity can be counterbalanced by introgression of novel germplasm. However, it should be noted that only a small proportion of the available genetic variation of the gene pools has been exploited for plant breeding so far (Frankel, 1977; Tanksley & McCouch, 1997; Fernie et al., 2006), but most of the exotic pools remain untapped, uncharacterized and underutilized (Alisdair et al., 2006). Therefore, the genetic variation provided by the current and expanded gene pools should be examined and harnessed for further crop improvement. 1.2 Evaluation methods of genetic diversity Effective management and utilization of resources depends to a large extent on appropriate estimation of the material represented in the collection. Diversity can be generally characterized either by apparent diversity reflecting the different performance of crops across environments and management or by latent diversity referring to the genealogical and molecular measurements which are not necessarily expressed in crop performance (Smale et al., 2002). Several methods including pedigree records, biochemical markers and DNA marker can be performed to measure the latent diversity to quantify genetic diversity among genotypes (Cox et al., 1985; Karp et al., 1996). 1.2.1 Coefficient of parentage (COP) The COP method is based on pedigree information and provides an indirect INTRODUCTION 3 measurement for the genetic diversity of cultivars by estimating the probability that alleles at a given locus are identical by descent. However, calculation of COP values has limitations because of the simplifying assumptions regarding relatedness of ancestors, parental contribution to the offspring, selection pressure, and genetic drift, which are generally not met (Cox et al., 1985; Cowen & Frey, 1987). Furthermore, pedigree records are not always available or detailed enough for such type of analysis, especially when large numbers of breeding lines or cultivars are being assessed (Parker et al., 2002). 1.2.2 Molecular markers Diversity on a molecular level has been studied in plants for about three decades. The most comprehensive early studies were performed with biochemical markers such as isozymes and protein subunits (Hamrick & Godt, 1990; Weeden et al., 1994; Eagles et al., 2001) and provided many insights into population structure and breeding systems. Although these markers allowed large numbers of samples to be analyzed, only a limited number of loci could be scored. Furthermore, the comparison of samples from different species and laboratories were problematic (Buckler & Thornsberry, 2002). In contrast, DNA markers offer quantitative views of genetic diversity among genotypes on the DNA level and have been widely accepted as potentially valuable tools to assess precisely genomic diversity in cereals, like wheat (Burkhamer et al., 1998; Eagles et al., 2001; Koebner, 2003), rice (Mackill et al., 1999), barley (Donini et al., 2001; Russell et al., 2000) and maize (Smith et al., 1997; Gauthier et al., 2002). In general, molecular markers can be classified into three categories based on their detection method: (1) hybridization-based such as restriction fragment length polymorphisms (RFLPs); (2) polymerase chain reaction (PCR)-based such as random amplified polymorphic DNAs (RAPDs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs), and (3) DNA chip and/or sequence-based such as single nucleotide polymorphisms (SNPs) (Gupta et al., 1999; Collard et al., 2005). INTRODUCTION 4 1.2.2.1 Restriction fragment length polymorphisms (RFLPs) Among the various molecular markers, RFLPs were developed first and initially used to human genome mapping (Bostein et al., 1980). Later, these DNA marker technique was used in plant genome analysis including genome mapping (Weber & Helentjaris, 1989; Tanksley et al., 1989), variety identification (Vaccino et al., 1993) and assessing the level of genetic diversity and relationships within germplasm (Kim & Ward, 1997; Paull et al., 1989). RFLPs refer to variation between genotypes in lengths of DNA fragments produced by restriction enzymes that cut genomic DNA at specific sites. The polymorphisms can arise either when mutations alter restriction sites, or result in insertions/deletions between these sites (Burr et al., 1983). The polymorphisms detected by RFLP technique compassed the recognition and cleavage by specific restriction enzymes and hybridization with a specific probe. Therefore, RFLPs have been shown the most reliable polymorphisms, which can be used for accurate scoring of genotypes. Further advantages of RFLP markers are the high level of information obtained by their co-dominant inheritance and their high level of reproducibility (Weeden et al., 1991; Helentjaris et al., 1985). However, several drawbacks limiting the use of RFLPs are: laborious, time-consuming, and low frequency of polymorphisms in crops especially in wheat (Bryan et al., 1997; Powell et al., 1996). 1.2.2.2 Random amplified polymorphic DNAs (RAPDs) The polymerase chain reaction (PCR) technique (Saiki et al., 1988) facilitated the development of simple and low-cost molecular markers such as random amplified polymorphic DNAs (RADPs, Williams et al., 1990), amplified fragment length polymorphisms (AFLPs) (Vos et al., 1995), and simple sequence repeats (SSRs) (also known as microsatellites, Tauz & Renz, 1984). RAPDs are based on amplification of DNA fragments by PCR using decamer primers homologous to random target sites in the genome (Williams et al., 1990). The polymorphisms revealed are either due to point mutations or insertions /deletions within the amplified region (Tingey & Deltufo, 1993). INTRODUCTION 5 RAPDs are much simpler and less laborious in comparison to RFLPs because they rely on a universal set of decamer primers without needs for prior sequence information and radioactive labeling of probes (Devos & Gale, 1992). However, since the natures of their random and short primer length, they cannot easily be transferred between species. They are mainly used as species- specific markers in diversity and phylogenetic studies, e.g. genome relationships in Triticeae (Joshi & Nguyen, 1993; Wei & Wang, 1995). Beside their dominant inheritance, their more general disadvantages are the sensitivity to the experimental conditions and a poor reliability and reproducibility (Karp & Seberg, 1996). 1.2.2.3 Amplified fragment length polymorphisms (AFLPs) AFLPs are based on PCR amplification of restricted fragments generated by the combination of two specific restriction enzymes, the ligation of restriction site specific adapters and the use of adapter specific oligo-nucleotides with additional nucleotides at the 3’ end (Zabeau & Vos, 1993). The polymorphisms detected are due to modifications of restriction sites e.g. after point mutation (Vos et al., 1995). AFLPs procedure involve three essential steps: (1) digestion of genomic DNA with two restriction enzymes (a low and a high frequent cutter), (2) ligation of adapter to the restriction ends, and (3) selective amplification of sets of restriction fragments by two successive PCR reactions using primers complementary to the restriction sites and adapter plus one to three additional nucleotides. Because this technique combines the reliability of the RFLPs technique with the power and ease of the PCR techniques (Jones et al., 1997), and exhibits intraspecific homology (Powell et al., 1996; Tohme et al., 1996), AFLP analysis is the most efficient method compared to RFLPs and RAPDs (Powell et al., 1996; Lin et al., 1996). However, the AFLPs method is technically difficult and expensive to set up, but it detects a large number of loci, reveals a great deal of polymorphisms and produces high complex DNA fingerprints, what is very useful in saturation mapping and for discrimination between varieties (Mohan et al., 1997; Jones et al., 1997). INTRODUCTION 6 1.2.2.4 Simple sequence repeats (SSRs) Plant genomes contain large numbers of simple sequence repeats (SSRs) (also termed microsatellites). The core units of SSRs are usually one to five nucleotides, which are tandemly repeated and widely scattered at many different loci throughout the genome (Taute and Renz, 1984). These small repetitive DNA sequences provide the basis for a PCR-based, multi-allelic, co- dominant genetic marker system (Saghai-Maroof, 1994). Because the genome regions flanking the microsatellite are generally conserved among genotypes of the same species, SSR primers are designed matching unique flanking sequences, composed of short nucleotides, by which the microsatellite locus can be defined (Powell et al., 1996). Polymorphisms revealed by PCR- amplification are due to the variation of the number of repeats in a defined region of the genome (Morgante & Olivieri, 1993; Jones et al., 1993). The utility of SSR markers is primarily deduced from their abundant distribution and hyper-variability in the whole genome (Morgante & Olivieri, 1993). Due to the existence of these hyper-variable regions, SSR markers exhibit a high power in distinguishing between closely related genotypes. Furthermore, the reproducibility of SSRs makes them interchangeable among different laboratories to produce consensus data. However, the development of SSR markers is laborious and expensive. Through a public database to access primer sequences would maximize the use of microsatellites and reduce the development costs (Powell et al., 1996). 1.2.2.5 Single nucleotide polymorphisms (SNPs) Single nucleotide polymorphisms (SNPs), referred to as single point mutations, have recently been developed into DNA markers, which offer high-throughput and automated genotyping approaches (Shi, 2001; Gupta et al., 1999). SNPs are highly abundant and distributed throughout the plant genomes such as maize (Edwards & Mogg, 2001; Tenaillon et al., 2001), barley (Kanazin et al., 2002) and in rice (Yu et al., 2002; Nasu et al., 2002). Various methods have been developed to genotype SNPs like pyrosequencing (Ahmadian et al., 2000), INTRODUCTION 7 TaqMan (Livak, 1999), fluorescence energy transfer (Chen et al., 2000) or allelic-specific PCR (Drenkard et al., 2000). However, those using automated systems developed for high-throughput applications, which often require specific detection equipment, have high development costs and the marker assays generated are commonly not transferable between laboratories (Bundock et al., 2006). The development and use of allele-specific PCR-primers would be preferred due to its simplicity, low cost and reproducibility of genotyping SNP (Lee et al., 2004; Hayashi et al., 2004). By this approach, SNPs can be identified simply using allele-specific PCR primers designed that the 3’ terminal nucleotide of a primer corresponds to the site of the SNPs. The PCR-amplified products can be resolved on a standard agarose gel (Hayashi et Figure 1 Single nucleotide polymorphisms identified in a 263-nt segment of the maize stearoyl-ACP-desaturase gene (A Ching, unpublished data). The horizontal rows correspond to each of the 32 individuals sequenced. The vertical columns identify nine polymorphic sites, including one insertion/deletion (I/D) polymorphism. Four distinct haplotypes are shown. These four haplotypes can be unambiguously identified using only three SNPs, for example, those marked with an asterisk (*). The remaining SNPs provide redundant information. No two SNPs are sufficient to distinguish all four haplotypes (adapted from Rafalski 2002). INTRODUCTION 8 al., 2004; Lee et al., 2004). Through sequencing the PCR-amplified products from a number of diverse individuals, DNA polymorphisms can be detected in the most straightforward way compared to the other types of DNA markers based on the indirect detection of sequence-level polymorphisms including SSRs (Rafalski, 2002). Moreover, the PCR primers designed are either derived from the known DNA sequences of genes available from public GeneBank, or from expressed sequence tags (ESTs) (Rafalski, 2002). Therefore, the detection of SNPs provides the opportunity to uncover allelic variation directly within the sequences of genes or expressed sequences of candidate genes (Snowdon & Friedt, 2004). With the rapid development of public sequence databases in crop species, haplotype analysis is possible and more informative compared to individual SNP analysis (Figure 1) (Rafalski. 2002). 1.3 Lodging, its occurrence and types Lodging of cereals is defined as permanent displacement of the culms from the upright position (Pinthus, 1973). It can result in buckling of the stem at a basal internode (stem lodging) (Figure 2) or in the rotation of the whole plant in the soil (root lodging) (Figure 3) (Zuber et al., 1999). Causes are usually a combination of wind and rain, but can be enhanced by different pathogens and pests affecting stems or roots (Keller et al., 1999) or by agronomic practices such as excessive fertilization and/or high seeding rates in wheat (Easson et al., 1993; Stapper & Fischer, 1990; Berry et al., 2000). Lodging can be a major constraint on yield potential in many crops, but it is of particular importance in the small-grain cereals, e.g. like wheat (Triticum aestivum L.) (Atkins et al., 1938; Easson et al., 1993), barley (Hordeum vulgare) (Baker et al., 1990; Travis et al., 1996), oat (Avena sativa) (Mulder, 1954; Murphy et al., 1958), corn (Zea mays) (Hondroyianni et al., 2000; Flint-Garcia et al., 2003), and rice (Oryza sativa) (Takahashi, 1960; Setter et al., 1997). At any stage of plant development (Atlins, 1938; Baker et al., 1990), lodging can occur and it is most detrimental at anthesis or in the early grain filling stage by reducing the number of kernels per ear and grain size (Laude & Pauli, 1956; Fisscher & Stapper, 1987; Briggs, 1990; Berry et al., 2004). INTRODUCTION 9 1.4 Effects of lodging on yield and quality of cereals Grain yield reduction always accompanies lodging at which the degree of loss depends on the cultivar, growth stage and severity of lodging (Jedel & Helm, 1991; Easson et al., 1993; Fischer & Stapper, 1987). Several reports mentioned that lodging can reduce cereal production up to 20% (Briggs, 1990), 30% (Pinthus, 1973) or even 40% (Easson et al., 1993). Lodging can complicate harvesting and may cause deterioration in the milling and baking quality of the grains due to the increased moisture content of the grains and pre-harvest sprouting (Weber & Fehr, 1966; Kono, 1995). Furthermore, in lodged plants the contamination with mycotoxins produced by Fusarium species on the ears can Figure 2 Stem lodging in barley (cited by Berry et al., 2004) Figure 3 Root lodging in barley (cited by Berry et al., 2004) INTRODUCTION 10 be significantly increased due to the humid atmosphere surrounding lodge crops (Langseth & Stabbetorp, 1996; Scudamore, 2000). 1.5 Factors affecting lodging Lodging resistance is an important goal of cereal breeding. Lodging researches can be traced to 1930s for over a century and numerous efforts have been made to find and establish methods to assess lodging resistance so far. Most published studies before 1980 have been conducted on determining the correlations between morphological traits and lodging resistance (Clark & Wilson, 1933; Brady, 1934; Atkins, 1938; Sato, 1957; Jellum, 1962; Kohli et al., 1970; Stanca et al., 1979), whereas more recent publications have tried to established mechanical models for lodging resistance (Jezowski et al., 1987; Dolinski, 1990; Ennos, 1991; Crook & Ennos, 1993; Berry et al., 2006) or have focused on physiological and chemical components of the culms and their histological distribution (Dunn & Briggs, 1989; Kokubo et al., 1989; Zhu et al., 2004; Tripathi et al., 2003; Wang et al., 2006). 1.5.1 Plant height Plant height is the major trait for the improvement of lodging resistance in cereal crops. A strong correlation between plant height and lodging has been reported for barley (Murthy & Rao, 1980, Stanca et al., 1979) and wheat (Atkins, 1938; Pinthus, 1967; Min et al., 2001). Since the 1960s, the introgression of dwarf genes has increased lodging resistance and grain yield (“green revolution”, Keller et al., 1999; Khush, 1999; Worland & Snape, 2001). Modern high-yielding cultivars are generally shorter with stronger straw, so a higher harvest index (Kelbert et al., 2004). Compared to high-growing plants, dwarfism increase lodging resistance through decreasing the centre gravity height of plant (Huang et al., 1988). According to Huang et al. (1988), plant height showed a significantly positive correlation with the centre gravity moment of plant (r = 0.969) while lodging resistance index is significantly negative correlated with the height at centre of gravity (r = -0.891). Hence, the history of lodging resistance INTRODUCTION 11 breeding was to some extent a history of dwarfism breeding (Wang et al., 1996). However, very short plants can reveal a decrease of biomass, high density of leaves, shrunken grains, premature senescence, aggravated diseases, etc. (Xiao et al., 2002). Therefore, continuous plant height reduction using dwarf genes may not be compatible with high yield (Berry et al., 2004). Moreover, lodging will still happen if the stem strength is not strong enough after dwarfing (Li et al., 1998; Min et al. 2001). 1.5.2 Stem characteristics Lodging usually occurs when the stems bend or break at the basal internodes (Pinthus, 1973). Thus, the stem basal internode traits seem to be more important in comparison to other aerial traits of plants (Huang et al., 1988; Wang et al., 1996; Xiao et al., 2002). The stem basal internode traits comprised stem basal internode morphologies, anatomic characters, physiological factors, chemical ingredients, etc. (Wang et al., 1996). 1.5.2.1 Morphological characters Under natural field conditions, lodging occurs in general sporadically. Thus, selection for lodging resistant cultivars is difficult in early generations of crops (Kelbert et al., 2004). Identification of easily measurable stem traits associated with lodging resistance may simplify the selection process and are a goal for cereal breeding. The differences among lodging resistant and susceptible cultivars regarding various morphological characters of stems have been found in barley (Dunn & Briggs, 1989; Stanca et al., 1979), whereupon resistant cultivars exhibited shorter basal internodes, wider basal culm diameter and thicker culm walls compared to susceptible ones. Similarly, wider basal culm diameter and thicker culm walls associated with lodging resistant cultivars have been reported in wheat (Mukherjee et al., 1967; Shevchuk et al., 1981; Zuber et al., 1999; Tripathi et al., 2003) and oat (Jellum, 1962). Studies of Zuber et al. (1999) indicating that stem diameter explain 48% of the phenotypic variance of lodging INTRODUCTION 12 resistance while 50% of the phenotypic variance of lodging can be explained by stem weight cm-1. Whether plants are resistant or susceptible to lodging is finally predicted by stem strength, implying mechanical elasticity and rigidity of the stem (Wang et al., 1996). The relation between lodging resistance and stem strength has been reported again in wheat (Atkins, 1938; Crook & Ennos, 1994; Pu et al., 2000) and barley (Clark & Wilson, 1933; Murthy & Rao, 1980). By path analysis, Pu et al. (2000) estimated the correlation between stem strength and lodging in a set of 11 high-yielding wheat varieties. The results indicated that an increase of one standard unit for stem strength was related to a decrease of 0.592 standard units for lodging index (Px-y = -0.592) in average, suggesting that plants with higher stem strength have a lower lodging index. Focusing on the relationships between stem strength and stem basal internodes, several investigations have been performed. Xiao et al. (2002) observed that the stem diameter of the basal internodes was significantly correlated with stem strength from the milk to maturity stage (r =0.379, 0.498 and 0.461), while the stem diameter of the upper internodes was not positively related to stem strength. More recently, Wang et al. (2006) found out that the thickness- diameter ratio showed significant correlation with stem strength at r = 0.780, whereas the stem wall thickness is significantly correlated with stem strength at r = 0.551. Similar results have been reported by Zhu et al. (2004). These two studies are comparable to the results of Huang et al. (1988) where the thickness-diameter ratio of stem showed a significant positive correlation with lodging resistance (r = 0.681). No significant correlation was observed between the stem wall thickness and the stem diameter versus lodging resistance in wheat, indicating that plants with high thickness-diameter ratio have high resistance to lodging (Huang et al., 1988). However, dependent on the plant materials and crops deployed, different or contradictory results have been reported. For example, some authors did not find a significant correlation between stem diameter and lodging resistance in wheat (Atkins, 1938; Pinthus, 1967; Al-Qaudhy et al., 1988; Kelbert et al., 2004; Wang et al., 2006). In oats and barley, the negative correlation between the stem diameter and stem strength have been observed (Norden et al., 1970; Dunn et al., 1989). INTRODUCTION 13 1.5.2.2 Anatomical structure The stem is one of the most important plant organs playing a key function in transportation, storage and mechanical support. Cereal stems are comprised of several nodes and internodes, mostly with pith cavities. The internodes close to the stem basis are generally shorter with a thicker stem wall compared to the upper internodes. The transverse section of internodes from the center to the outer layer is mainly composed of different tissues: pith, parenchyma, vascular bundles, sclerenchyma and epidermis. Vascular bundles are distributed within the transverse section in two circles: (1) small vascular bundles close to the epidermis embedded in sclerenchyma, which consists of fiber cells and (2) large vascular bundles, which are included in the parenchyma. Up to now, many studies have been carried out to determine the relationship between anatomic stem characters and lodging resistance in wheat (Ford, 1979; Li, 1979; Cenci et al., 1984; Huang et al., 1988; Han et al., 1990; Wang et al., 1991; Wang et al., 1998; Zhu et al., 2004; Wang et al., 2006) and barley (Kokubo et al., 1989; 1991), respectively. Already in 1934, Brady reported that an increased number of vascular bundles is the most significant anatomical feature of the stem related to lodging resistance in wheat. According to Han et al. (1990), the percentage of mechanical tissue of stem and the number of vascular bundles mm-2 are closely correlated with lodging. However, Wang et al. (2006) revealed in wheat that the total number of vascular bundles mm-2 is negatively correlated with stem strength, whereas the number of large vascular bundles showed a positive correlation with stem strength (r = 0.494). The same study mentioned a positive correlation between the percentage of sclerenchyma and stem strength (r = 0.804). Similar results that the thickness of the sclerenchyma was responsible to lodging resistant have been reported for barley (Jezowski & El-Bassam, 1985; Dunn & Briggs, 1989). Studies on anatomical stem structure in rice also proposed that the difference of stem strength between normal and brittle stems was due to the differences of sclerenchyma and vascular bundles (Li et al., 2003). One explanation for this phenomenon can be that fiber cells extensively exist in vascular bundles and sclerenchyma, and it has been shown that fiber cells play a key role in INTRODUCTION 14 mechanical support of plants (Wang et al., 2006). Beside, in an interfascicular fiber mutant (ifl1) of Arabidopsis thaliana, Zhong et al. (1997) detected that a lack of interfascicular fibers is correlated with a dramatic change of stem strength. Stems of the mutant were not able to stand erected and were easily broken by bending in comparison to wild type stems. 1.5.2.3 Physiological factors and chemical ingredients Many studies have indicated that the lodging resistance is not only due to morphological and anatomical stem characters, but well associated with physiological processes and chemical ingredients (Wang et al., 1996). The dry matter accumulated in the stem is mainly comprised of carbohydrates including monosaccharide, disaccharides and polysaccharides. The accumulation of dry matter, especially of polysaccharides the basis for cellulose and hemi-cellulose production, results in stem wall thickened and an increase of elasticity, which add up to an increase in stem strength (Wang et al., 1996). Several studies focusing on this issue have been performed and all indicated that the soluble carbohydrate content of the basal internodes of the stem contributed greatly to lodging resistance in wheat (Li et al., 1998) and rice (Sato, 1957; Takahashi, 1960; Matsuzaki et al., 1972; Taylor et al., 1999; Yang et al., 2001). According to Huang et al. (1988), the correlation between the carbohydrate content and lodging resistance can reach r = 0.991. Plant cell walls possess of a strong fibrillous netted structure that provides mechanical support to cells, tissues, and the entire plant body (Li et al., 2003). Cellulose, hemi-cellulose and lignin as the main components of the cell wall, seem to have an intrinsic correlation with lodging resistance (Bernards & Lewis, 1998; Wang et al., 1996). For example, Taylor et al. (1999) and Jones et al. (2001) reported that lignin and cellulose content is related to stem rigidity. Huang et al. (1988) revealed that the lignin content of basal internodes of strong stems was higher compared to week stems. Kokubo et al. (1989) found a high correlation between the cellulose content of barley cell wall and maximum bending stress (r = 0. 93). However, whether the lignin content or cellulose content of stems is a INTRODUCTION 15 determinant factor for the stem strength, different conclusions have been reported regarding more recently studies. Zhu et al. (2004) and Jones et al. (2001) emphasized that the lignin content of stems is more important than the cellulose content for increasing the mechanical support. In contrast, Wang et al. (2006) reported that the cellulose content is more important in mechanical support in comparison to the lignin content (r = 0.764 and r = 0.547, respectively). In addition, several authors have investigated the relationship between chemical elements and molecules versus lodging resistance. The stem of wheat contains 2.3 - 4.6% silicon, which is mostly present in the epidermis of wheat culms and considered to contribute to lodging resistance (Li, 1979). Comparing the silicon contents between lodging resistant and susceptible wheat varieties, Gartner et al. (1984) observed a significantly higher silicon content in the epidermis and mechanical tissue of culms in the lodging resistant variety. Moreover, silicon of the cell wall was thought to contribute to mechanical strength in rice stems. Other chemical elements, like K, Ca and Mg are also associated with lodging resistance (Takahashi, 1995). 1.6 Evaluation methods and index for lodging Several methods have been proposed and used to evaluate lodging. The most frequently used method is by visual ranking of naturally or artificially occurring lodging on a scale from 1 (all plants upright) to 9 (all plants flat) (Keller et al., 1999; Verma et al., 2005; Huang et al., 2006; McCartney et al., 2005). The ranking based on the fact that the degree and area of occurring lodging in the field directly reflect the lodging resistant level of crops. Different methods include the manually scoring of elasticity of the stem (Jezowski et al., 1987; Keller et al., 1999), the measurement of stem–breaking strength (Min et al., 2001; Wang et al., 1995) or testing the pushing resistance of the stem by specific instruments, like done for wheat (Xiao et al., 2002; Zhu et al., 2004; Wang et al., 2006), rice (Kashiwagi & Ishimaru et al., 2004; Terashima et al., 1992), barley (Kokubo et al., 1989) and corn (Fouere et al., 1995). Vaidya et al. (1982) suggested that stem length × height per root weight and INTRODUCTION 16 breaking strength per height × shoot weight are the most suitable indexes of lodging resistance in wheat. Other indexes have been proposed like the moment of the gravity centre × load per fresh weight (Huang et al., 1988), height × aerial fresh weight per stem strength (Wang et al. 1995) and height × aerial fresh weight per root weight × stem strength (Pu et al., 2000). Because it is known that lodging is a complex trait covering many factors, up to now no method or index has been considered to be a reliable evaluation method for measuring and estimating lodging resistance. 1.7 Inheritance mode and chromosomal location of genes related to lodging Studies concerning the mode of inheritance of lodging and related traits can be found in recent publications. Kohli et al. (1970) observed a transgressive segregation of traits related to lodging including stem strength, culm diameter and unit length weight in a segregating wheat F2 generation and suggested a quantitative mode of inheritance of these three traits. Moreover, Li et al. (1998) analysed the general combining ability (GCA) and specific combining ability (SCA) of plant height at center of gravity, stem strength, pith diameter, stem wall thickness and mechanical tissues and indicated that these traits were controlled by genes with additive and non-additive gene effects. Stem fresh weight was controlled by non-additive genes, whereas small and large vascular bundles were controlled by genes with additive effects. 1.8 Quantitative trait loci (QTL) mapping QTL analysis involved three main steps: (1) Generation of mapping population; (2) Genotyping and construction of a marker-based linkage map and (3) QTL analysis combining linkage map and phenotypic values of traits. INTRODUCTION 17 1.8.1 Mapping population Selection of parents is the key step for generation of mapping population. Most important is the discrimination of putative parental breeding lines, cultivars or landraces in at least one or even better in more traits of interest. Especially in self-pollination species such as most of the cereals, parental genotypes for mapping purposes should be almost homozygous. Several different types of mapping populations are used and can be classified into two categories according the genetic stability: • Temporary segregation populations like F2 and backcross (BC) populations • Fixed segregation populations like doubled haploid (DH) and recombinant inbred (RI) populations. 1.8.1.1 F2 population The F2 population is common directly derived from F1 hybrids. Their major advantage is the easily done development in a short time independent of the reproduction system (self-pollination or cross-pollination) (Fang et al., 2001; Collard et al., 2005). However, F2 population comprise the maximum of heterozygousity (each locus will segregate in a 1:3 and, 1:2:1 ratio, respectively), where unfortunately dominantly inherited traits and molecular markers can not distinguished between dominant homozygous genotypes and heterozygous genotypes. Further disadvantage is that is not possible to conserved and proliferate a single F2 genotype without further segregation. 1.8.1.2 Backcross (BC) population A BC population is derived from a cross between the F1 hybrid and one of the respective parents. This kind of population has similar advantages and drawbacks as F2 population. It also cannot be kept permanently. However, there is only a segregation ratio of 1 (homozygous): 1 (heterozygous) of each locus. Because the segregation ratio of the crossed F1 gamete directly reflects in BC population, the efficiency for mapping is higher compared to F2 population. INTRODUCTION 18 1.8.1.3 Doubled Haploid (DH) population DH population can be produced through inducing the production of haploid plants by anther- or microspore-culture starting from the F1 hybrids. Doubling of chromosomes will happen spontaneously or after induction with colchicine, a “mitotic poison” originated from Colchicum autumnale, commonly known as autumn crocus. However, the production of DH population is only possible in species that are amenable to tissue culture, e.g. cereal species such as wheat, rice and barley (Fang et al., 2001). The major advantage of a DH population is that each single line is homozygous at each locus and can be multiplied and reproduced without genetic change occurring (Collard et al., 2005). Thus, why DH population is called permanent population permitting to conduct replicated trials across different locations and years. Furthermore, DH lines can be transferred between different laboratories for further linkage analysis (Paterson, 1996a; Yong, 1994). A major disadvantage of DH population is the different capability in tissue culture and therefore selection effects occurs, which may cause segregation distortion affecting later the precision of linkage between marker loci and thus the whole genetic map (Fang et al., 2001). 1.8.1.4 Recombinant inbred (RI) population Population comprised of Recombinant inbreed lines (RILs) are developed by continued self-pollination of individuals starting from F2 plants by e.g. single seed descent (SSD) approach over several generations until almost all of segregating loci become homozygous. The major advantage of RILs just like DH-lines are that every line imply a unique combination of genomic segments from the ancestral parents in a homozygous manner and can be multiplied and reproduced without further segregation and change of genetic composition (Collard et al., 2005). RI populations as well as DH populations represent ‘eternal’ resources for QTL mapping and RI populations are also called permanent population (Yong, 1994; Paterson, 1996a). The major disadvantages of RI populations are the time consuming development and it may not be possible for each line to achieve homozygosity at every loci through limited INTRODUCTION 19 generations (in general six to eight) of self-pollination. A fact that decreases the efficiency for linkage map construction to some extent (Fang et al., 2001). 1.8.2 Linkage map construction The construction of a linkage map essentially includes two steps: (1) grouping of linked markers into linkage groups, and (2) arranging the markers within each linkage group. Linkage between markers is usually determined by odds ratios, which represents the ratio of linkage versus no linkage (Fang et al., 2001). The ratio is more convenient expressed as the logarithm of the ratio and is called a logarithm of odds (LOD) value or LOD score (Risch, 1992). The commonly used threshold for the LOD value is ≥ 3.0 for statistical acceptance of linkage (Kosambi or Haldane function, Lu et al., 1998; Lincoln et al., 1993). A LOD value = 3.0 between two markers indicate that the linkage is 1,000 times more probable than no linkage (Collard et al., 2005). Length and distance of a linkage map are measured according to the frequency of recombination between two markers (Paterson, 1996a). Because the recombination frequency and the frequency of crossing-overs are not always linearly related (Kearsey & Pooni, 1996), mapping functions are required to convert recombination fraction into centiMorgans (cM). Therefore, two mapping functions are commonly used: Kosambi function (Kosamb, 1944) and Haldane function (Haldane, 1919). The main difference is that the Kosambi function assumes that recombination events influence the occurrence of adjacent recombination events, while Haldane function assumes no interference between crossover events (Hartl & Jones, 2001; Collard et al., 2005). Several software programmes can be used to perform the construction of linkage map. The most common ones are Mapmaker/EXP (Lincoln et al., 1993) and JoinMap (Biometris, Wageningen, The Netherlands, http:// www.joinmap.nl). 1.8.3 Statistical methods for QTL mapping The principle of QTL detection is to devide the mapping population into different genotypic groups based on genotypes at the marker locus and to determine http://www.joinmap.nl/ INTRODUCTION 20 whether significant differences exist between groups with respect to the trait being measured. If the phenotypes between groups differ significantly, it indicated that the marker locus used to subdivide the population is linked to a QTL affecting the trait (Tanskley, 1993). Three main methods commonly used for QTL analysis are single-marker analysis, simple interval mapping (SIM) and composite interval mapping (CIM) (Liu, 1998; Tanksley, 1993) 1.8.3.1 Single-marker analysis Single-marker analysis is the simplest method of QTL analysis and focus on individual associations between a single marker and the phenotypic characteristics. If an association is discovered, it is likely that there is a QTL affecting the trait linked to that marker locus (Zeng, 1994). Single-marker analysis based on analysis of variance (ANOVA) statistics, t-test and multiple regression analysis (Soller & Brody, 1976; Simpson, 1989; Rodolphe & Lefort, 1993). The most commonly used method is multiple regression analysis proposed by Rodolphe & Lefort (1993). Single-marker analysis by multiple regression analysis, e.g. for a DH population, is based on the following model: yi , 1 iij m j j xb εμ ++= ∑ = yi is the phenotypic value of the ith individual i is individual;j is marker; m is the number of the indicator variables; µ is the mean value; b is the partial regression coefficient of the phenotype y on the jth marker; xij is an indicator variable of the jth marker in the ith individual, taking a value of 1 if the ith individual has the marker genotype j and 0 if otherwise; εi is a residual error. Regarding this model, the degree of correlation between each marker locus and phenotypic value is decided by the coefficient of partial regression. Generally, if the coefficient of partial regression reaches a determinated significance level, the QTL is indicated to be linked with the specific marker. Using this method, the (1) INTRODUCTION 21 phenotypic variation explained by QTL can be determined by the coefficient of partial regression. However, using single marker analysis the genomic position of the QTL cannot be determined (Fang et al., 2001). 1.8.3.2 Simple Interval Mapping (SIM) method SIM method is an extension of single-marker analysis and simultaneously analyses intervals between adjacent pairs of linked markers along several linkage groups (chromosomes), determines the likelihood profile of a QTL position at any particular point of each marker interval and calculates the LOD value (Lander & Botstein, 1989). So, SIM method can be considered as a statistical more powerful procedure compared to the single marker analysis (Liu, 1998). SIM procedure is also based on regression analysis. However, unlike single-marker analysis, SIM is based on the regression of phenotype and QTL instead of the regression of phenotype and marker locus. For example, regarding a DH population with only one QTL on a chromosome, Lander & Botstein (1989) proposed the following regression model to test for a QTL located on an interval markers j and j + 1: b* is the effect of the QTL; xi* is an indicator variable of the putative QTL with a value of 0 or 1 with a likelihood depending on the genotypes of markers j and j + 1 and position being tested for the putative QTL. Definitions of other parameters see (1) Likelihood ratio (LR) test statistics uses the LOD score to estimate parameters and determine the significance of the regression: LOD = lg [L (b ≠ 0) / L (b = 0)], where L (b=0) and L (b≠0) represent the maximum likelihood value (LOD≈0.217 LR) when b=0 and b≠0, respectively. If LOD exceeds a pre-defined threshold (b≠0), that is the effect of the putative QTL is not equal to 0, the existence of a (2) yi = μ + b*xi * + εi, (3) INTRODUCTION 22 QTL can be deduced (Lander & Botstein, 1989). Using this method, the probable position of the QTL can be inferred by the supporting interval. The estimated locations and effects of QTL tend to be unbiased if there is only one QTL on a chromosome. However, if there are two closely linked QTL in one marker interval, a “ghost QTL” might appear between these two real linked QTL and the two real QTL will be hidden by the “ghost QTL” (Moreno-Gonzalez, 1992), which may result in a bias estimation of QTL and a decrease in the testing power (Fang et al., 2001). 1.8.3.3 Composite Interval mapping (CIM) method CIM method is an extension of the simple interval mapping technique and is a combination of interval and multiple regression analysis (Zeng, 1994). The SIM method is modified by inclusion of additional markers as ‘cofactors’ in the regression model to get rid of the influence and background of other QTL to the target QTL interval (Fang et al., 2001). Regarding a DH population, testing of a QTL in a marker interval (j, j + 1) by CIM, Zeng (1994) proposed the following regression model: b* is the effect of the putative QTL; bk is the partial regression coefficient of the phenotype y on the kth marker; xi is an indicator variable of the putative QTL, taking a value 1 or 0 with probability depending on the genotypes of markers j and j + 1 and position being tested for the putative QTL; xik is a known coefficient for the kth in the ith individual, taking a value of 1 for the same marker genotype with one of the parents and 0 for the same marker genotype with the other. Definitions of other parameters are the same as (1) CIM method used the similar likelihood ratio test statistic compared to SIM method: LR = - 2ln [L (b = 0) / L (b ≠ 0)], Compared to SIM, the threshold of the test statistic for the CIM is different. i jjk ikki xbxb εμ +++= ∑ +≠ 1, ** (5) (4) yi INTRODUCTION 23 Using multiple regression analysis in CIM, the test statistic is more or less uncorrelated for different interval, because the entire genome is tested for the presence of QTL rather then focusing on a particular interval by SIM (Zeng, 1994). Because CIM method uses appropriate unlinked markers, which can partly account for the variation due to the unlinked QTL, and linked markers, which can reduce the variation resulting from linked QTL, in comparison to SIM method, the power of QTL detection is greatly improved by the CIM method (Jansen, 1996). 1.8.4 QTL mapping of agronomic traits in wheat Up to now, numerous studies for QTL mapping have been carried out in many crop species. Particularly in cereals, QTL for major agronomic traits like yield and its components have been described in barley (Marquez-Cedillo et al., 2001; von Korff et al., 2006), in rice (Septiningsih et al., 2003; Takeuchi et al., 2003) and maize (Ho et al., 2002; Moreau et al., 2004). In wheat, QTL for major agronomic traits have been extensively investigated so far. These traits includes grain filling time (Börner et al., 2002), maturity time (McCartney et al., 2005; Huang et al., 2006), heading date (Kato et al., 1999; Huang et al., 2003; Marza et al., 2006), seed dormancy, pre-harvest sprouting, grain color (Gross et al., 2002), and grain quality (Charmet et al., 2005; James et al., 2006; Perretant et al., 2000; Prasad et al., 2003; Cambell et al., 2001). Moreover, grain yield and yield components like grain weight, grain number and 1000-grain weight have been mapped by several studies (summarised in Table 1). 1.8.5 QTL mapping of lodging resistance and related traits in wheat Focusing on QTL for lodging resistance and related traits, reports in many cereal crops like barley (Backes et al. 1995; Hayes et al., 1993; Tinker et al., 1996), rice (Champoux et al., 1995; Kashiwagi & Ishimaru, 2004), oat (De Koeyer et al., 2004) and maize (Flint-Garcia et al., 2003) are available. INTRODUCTION 24 Table 1 Summary of mapped QTL for yield and yield components in wheat (Triticum aestivum L.) Trait Chromosome Population Population type Marker Reference Grain yield 4A CS/CS(kanto1074A) RILs RFLPs Araki et al. Grain 4A (1999) Grain 4A 50-grain 4A Plant height 4A Spikelet 4A 1000-grain 3A CNN /CNN (W13A) RILs RFLPs Shah et al. Grain 3A (1999) Plant height 3A Grain yield 5A CS(Capelle-esprez RILs RFLPs Kato et al. Grain 5A 5A)/CS (T. spelta5A) (2000) 50-grain 5A Spikelet 5A Grain 2D, 4A W7984/Optata85 RILs SSRs Börner et al. Grain 4A, 7D RFLPs (2002) 1000-grain 5A Plant height 1B, 3B, 3D, 5D, 6A Grain yield 3A Cheyenne/Wichita RILs RFLPs Campbell et 1000-grain 3A (2003) Grain 3A Plant height 3A Grain yield 7D Renan/Récital RILs SSRs Gross et al. 1000-grain 2B, 5B, 7A AFLPs (2003) RFLPs Grain yield 2A, 2B, 3D, RI4452/AC Domain DH SSRs McCartney et 4D (2005) 1000-grain 2A, 3D, 4A, 4D, 6D Plant height 2D, 4B, 4D, 7A, 7B Grain yield 1A, 1B, 2B, 7B, CS/SQ1 RILs SSRs Quarrie et al. 4A, 4B, 5A, RFLPs (2005) 5D, 7A, AFLPs 1000-grain 1B, 3D, 4A, 4D, 5A, 5B, 6B, 7B Grain weight 5A, 7A, 7B ACKarma/87E03S2B1 DH SSRs Huang et al. 1000-grain 2B, 2D, 3B, (2006) 4D, 6A Plant height 4B, 4D, 5D, Yield weight 1A, 1B, 2B, Ning7840/Clark DH AFLPs Marza et al. 4B, 5A, 5B, SSRs (2006) Grain 1A, 1B, 2B, 3B, 4B, 6A, Plant height 2B, 2D, 3B, 6A INTRODUCTION 25 Furthermore, QTL for lodging resistance have been reported in several studies on wheat summarised in Table 2. 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Genet 136: 1457- 1468 Zhong RQ, JJ Taylor & ZH Ye, 1997. Disruption of interfascicular fiber differentiation in an Arabidopsis mutant. Plant Cell 9: 2159-2170 Zhu L, GX Shi, ZHSH Li, TY K, B Li, QK Wei, KZH Bai, YX Hu & JX Lin, 2004. Anatomical and chemical features of high-yield wheat cultivar with reference to its parents. Acta Bot Sini 46 (5): 565 – 572 (in Chinese with English abstract) Zuber U, H Winzeler, MM Messmer, M Keller, B Keller, J E Schmid & P Stamp, 1999. Morphological traits associated with lodging resistance of spring wheat (Triticum aestivum L.). J Agron Crop Sci 182: 17-24 Objectives 37 2 OBJECTIVES (1) SSR markers were used to describe and characterize the genetic diversity in a set of 69 spring bread wheat accessions from different geographical areas of the world but for the most part belonging to the European gene pools used for breeding purposes. (2) QTL analysis of stem strength and related traits including stem diameter, culm wall thickness and pith diameter of basal internodes of wheat (Triticum aestivum L.) based on a DH population (cross CA9613 × H1488) to determine and analyse (i) genomic locations of the traits, (ii) markers associated with QTL, (iii) phenotypic effects, (iv) the homologous relationships among QTL and (v) to explore their utilization in wheat lodging resistance breeding by means of marker-assisted selection (MAS). L Hai et al. 2006. Genetica DOI 10.1007/s10709-006-9008-6 38 3 PUBLICATIONS 3.1 Quantitative structure analysis of genetic diversity among spring bread wheat (Triticum aestivum L.) from different geographical regions Genetica (2006) Lin Hai, Carola Wagner & Wolfgang Friedt* Department of Plant Breeding, Research Centre for Biosystems, Land Use and Nutrition, Justus-Liebig University, Giessen, Heinrich-Buff-Ring 26-32, D-35392, Giessen, Germany *Author for correspondence 3.1.1 Abstract Genetic diversity in spring bread wheat (T. aestivum L.) was studied in a total of 69 accessions. For this purpose, 52 microsatellite (SSR) markers were used and a total of 406 alleles were detected, of which 182 (44.8%) occurred at a frequency of < 5% (rare alleles). The number of alleles per locus ranged from 2 to 14 with an average of 7.81. The largest number of alleles per locus occurred in the B genome (8.65) as compared to the A (8.43) and D (5.93) genomes, respectively. The polymorphism index content (PIC) value varied from 0.24 to 0.89 with an average of 0.68. The highest PIC for all accessions was found in the B genome (0.71) as compared to the A (0.68) and D genomes (0.63). Genetic distance-based method (standard UPGMA clustering) and a model- based method (structure analysis) were used for cluster analysis. The two methods led to analogical results. Analysis of molecular variance (AMOVA) showed that 80.6% of the total variation could be explained by the variance within the geographical groups. In comparison to the diversity detected for all accessions (He = 0.68), genetic diversity among European spring bread wheats was He = 0.65. A comparatively higher diversity was observed between wheat L Hai et al. 2006. Genetica DOI 10.1007/s10709-006-9008-6 39 varieties from Southern European countries (Austria/Switzerland, Portugal/Spain) corresponding to those from other regions. Key words: genetic diversity, microsatellites, spring bread wheat, Triticum aestivum L., SSRs, quantitative structure analysis 3.1.2 Introduction Effective crop improvement depends on the existence of genetic diversity. Trends concerning the loss of genetic diversity due to modern breeding practice have been reported by several studies (Russell et al., 2000; Roussel et al., 2004; Fu et al., 2005). Therefore, it seems necessary to understand the levels and distribution of genetic diversity in existing crop gene pools, as a basis for developing strategies of resource management and exploitation. Considering broadening the genetic base of crops for the maintenance of substantial breeding progress, exotic germplasm has shown to be a valuable resource, especially basic materials possessing specific agronomic traits such as disease and pest resistance. Furthermore, for incorporating exotic germplasm into respective breeding programmes, the genetic relationship between exotic accessions and adapted cultivars should be studied. Molecular markers have been shown to be reliable tools to assess genomic diversity. However, some of the marker systems, such as restriction fragment length polymorphism (RFLP) (Botstein et al., 1980) and random amplified polymorphic DNA (RADP) (Williams et al., 1990) have been of limited use for crop plants due to their low polymorphism, particularly in self-pollinating species with a narrow genetic basis, such as bread wheat (Sharam et al., 1983). On the other hand, simple sequence repeats (SSRs) (Tautz et al., 1989) have been widely exploited in wheat due to their high level of polymorphism, co-dominant inheritance and equal distribution in the wheat genome (Röder et al., 1995; Parker et al., 2002). Up to now, besides their application for identifying genotypes and detecting genetic diversity (Plaschke et al., 1995; Prasad et al., 2000), SSRs have been used for the characterization of the genetic integrity of gene bank accessions (Börner et al., 2000), the genetic differentiation caused L Hai et al. 2006. Genetica DOI 10.1007/s10709-006-9008-6 40 by selection (Stachel et al., 2000), and temporal changes in genetic diversity (Donini et al., 2000; Christiansen et al. 2002; Roussel et al., 2004). Moreover, comparisons of genetic diversity among gene pools from different geographical origin in Europe or worldwide have been carried out by this approach (Roussel et al., 2005; Röder et al., 2002; Huang et al., 2002). For statistical data analysis and presentation, the UPGMA clustering is commonly used as a standard procedure. More recently, a model-based clustering method, the so–called structure analysis was developed by Pritchard et al. (2000), and first used for association studies in Human genetics (Pritchard and Przeworski, 2001; Rosenberg et al., 2002). This method uses Bayesian clustering and allows to characterize populations (or groups) by allele frequencies at each locus, and individuals in the samples can be assigned to one or more population(s) or group(s) based on probability. Therefore, structure analysis is considered as a more suitable approach to fine statistical inference than the distance-based UPGMA clustering (Pritchard et al. 2000). This method has recently been applied for structural analysis of populations or the identification of genetically distinct groups in crop species, such as rice (Jain et al., 2004; Lu et al., 2005), maize (Liu et al., 2003), barley (Ordon et al., 2005) and wheat (Maccaferri et al., 2005). In the present study, SSR markers were used to characterize the genetic diversity in a set of 69 spring bread wheat accessions selected on the basis of their diverse origin from different geographical areas of the world. Cluster analysis was performed by, both, the genetic distance-based and the model- based methods. Finally, genetic relationship and diversity levels were analysed to describe and characterise the European gene pools for breeding purposes. 3.1.3 Materials and methods 3.1.3.1 Plant materials A total of 69 spring bread wheat (Triticum aestivum L.) accessions including 66 cultivars and three landraces out of a German evaluation program (EVAII) were used for this study. Among them, 56 accessions originated from different L Hai et al. 2006. Genetica DOI 10.1007/s10709-006-9008-6 41 European countries like Austria and Switzerland (A/CH), Czech Republic (CZ), France, Germany and Netherlands (F/G/NL), Norway and Sweden (N/S), Portugal and Spain (E/P), the United Kingdom (UK), and 13 varieties originated from North America (Canada, USA), South America (Argentina, Brazil), and East Asia (China, Japan). Nyubay and Aruakomugi (from Japan) and Wangshuibai (from China) represent landraces (Table 3). Table 3 Sixty-nine spring bread wheat accessions used in this study with their country of origin. 3.1.3.2 DNA extraction and SSR analysis Seeds were sown and grown in pots in the greenhouse. For each accession fresh leaf material of five plants were pooled and bulk genomic DNA was extracted according to a standard CTAB method (Doyle and Doyle, 1990). Fifty- No. Accessions Origin* No. Accessions Origin 1 Diablon Switzerland 36 Bastian Norway 2 Fiorina Switzerland 37 Bjarne Norway 3 Gerina Switzerland 38 Brakar Norway 4 Nadro Switzerland 39 Melissos Germany 5 Pizol Switzerland 40 Monsun Germany 6 B5769 Switzerland 41 Munk Germany 7 Tirone Switzerland 42 Nandu Germany 8 Kommissar Austria 43 Naxos Germany 9 Alva Portugal 44 Perdix Germany 10 Amazonas Portugal 45 Quattro Germany 11 Coa Portugal 46 Star Germany 12 Eufrates Portugal 47 Thasos Germany 13 Mondego Portugal 48 Triso Germany 14 Roxo Portugal 49 Velos Germany 15 Sever Portugal 50 Remus Germany 16 Sorraia Portugal 51 Fasan Germany 17 Jordao Spain 52 Baldus Netherlands 18 Asby the United Kingdom 53 Cracker Netherlands 19 Cadenza the United Kingdom 54 Minaret Netherlands 20 Chablis the United Kingdom 55 Sarina Netherlands 21 Samoa the United Kingdom 56 Josselin France 22 Shiraz the United Kingdom 57 Transec the United States 23 Aranka Czech Republic 58 IDO232 the United States 24 Leguan Czech Republic 59 AC Reed Canada 25 Linda Czech Republic 60 Colotana266/51 Brazil 26 Sandra Czech Republic 61 Frontana Brazil 27 Saxana Czech Republic 62 Eureka FCS Argentina 28 Avle Sweden 63 Universal Argentina 29 Dragon Sweden 64 Yang89-110 China 30 Hugin Sweden 65 Ning7840 China 31 Lavett Sweden 66 Sumai3 China 32 Polkka Sweden 67 Wangshuibai China 33 Tjalve Sweden 68 Nyubay Japan 34 Troll Sweden 69 Arurakomugi Japan 35 Vinjett Sweden L Hai et al. 2006. Genetica DOI 10.1007/s10709-006-9008-6 42 Table 4 Chromosomal location, number of alleles, number of rare alleles and polymorphism information content (PIC) values per locus for 52 microsatellite loci in two sets of data: all 69 accessions (aa) and 56 European accessions (ea), respectively. *The chromosome location of SSR markers according to Somers et al (2004) No of alleles No of rare alleles PIC. Locus Chr* aa ea aa ea aa ea wmc24 1AS 7 5