It is speculated that only a limited range of biochemical processes is involved in disease resistance. As a consequence less genes might be responsible for the expression of resistance than for other agronomic traits such as stress tolerance or yield, which are the result of many different physiological processes and therefore truely polygenic. For many pathosystems two to ten effective factors were found to be responsible for the expression of quantitative resistance which seems to be a lower limit of the number of genes involved (Geiger and Heun, 1989).
132 B. Keller, C. Feuillet & M Messmer
Box 5. Basic concept of QTL analysis
In order to dissect quantitative resistance into Mendelian factors of inheritance, a population segregating for the resistance trait has to be studied from a cross between well defined parental lines. This can be an F2 population when the resistance trait can be assessed reliably on the F2 progeny, otherwise replicated tests have to be performed using homozygous progeny, vegetative clones (Soller and Beckmann, 1990) or recurrent backcrosses (Tanksley and Nelson, 1996). For each individual or progeny the resistance phenotype as well as the marker genotype for a large number of molecular markers has to be assessed (see Fig. 13 for an example). At each marker locus it is determined whether this individual is homozygous for parent A (AA) or parent B (BB) or whether it is heterozygous (AB).
Genetic maps are obtained by looking for cosegregation of such genetic markers in the segregating population. All markers that are linked with each other belong to one linkage group which corresponds to one chromosome. The degree of linkage determines their linear alignment on the chromosome. In contrast to the physical map which is given by the absolute length in I-lm of a chromosome at the metaphase or the size in kbp, the genetic length is given in centiMorgans (cM). One cM corresponds to an average of 1 % crossover between two markers during one meiotic phase in the life cycle. On the basis of such genetic maps it is now possible to pin down the location of a QTL on the chromosome relative to the marker positions and to estimate the genetic effect of this QTL. Without going into statistical details which are very well explained elsewhere (Lander and Botstein, 1989; Paterson et ai., 1991; Haley and Knott, 1992; Lee, 1995) the basic concept of QTL analysis is explained here very briefly. Based on information of an individual marker locus, the population can be divided into the three marker classes: AA, BB or AB, respectively.
If the three classes show a significant difference in the phenotypic expression of resistance (Fig. 14) it is assumed that this marker locus is linked with a QTL for resistance. In interval mapping, the information of two or more marker loci is used simultaneously. Thus, by looking at recombination frequencies between the trait and the two markers, it is possible to estimate the most likely position of the QTL within the marker interval. A measure for the significance of the presence of a QTL in the investigated marker interval is the LOD score.
The LOD (IoglO of the odds ratio) is defined as the logarithm (base 10) of the ratio of the likelihood that the phenotypic data of the segregating population have arisen assuming that there is a QTL present (i.e. linkage between marker and QTL) versus the likelihood that the data have arisen assuming the absence of a QTL ( i.e. independent segregation of marker and QTL). The LOD score indicates the probability of a QTL which determines the trait in question being linked to a marker (Lander and Botstein, 1989). The LOD threshold depends on the genome size and the marker density. Typically LOD thresholds between 2 and 3 are required to limit the rate of false positives to 5%. The map position with the highest LOD score is assumed to be the most likely position of the QTL. In most studies a QTL can be placed within 10 to 20 cM with an acceptable degree of certainty (Lee, 1995). Thus, interval mapping results in a less precise localization of QTLs than the mapping of monogenic traits. For each QTL the following genetic effects are estimated: the additive effect, which is calculated as half the difference between the homozygous class AA and BB at the QTL
Box 5. continued
locus, and indicates the average effect if one allele of parent A is substituted by one allele of parent B. The dominance effect is calculated as the deviation from the heterozygous class AB from the average of the homozygous classes AA and BB. If a QTL is totally linked with a marker locus, the additive and dominance effects can be directly calculated taking the phenotypic mean of the marker classes (Fig. 14). In some studies epistatic effects between QTL are calculated as well as QTL x environment interactions. The amount of phenotypic variation that can be explained by a single QTL is expressed as the coefficient of determination (R2). The accuracy of the QTL analysis depends on the heritability of the trait, type of population and progeny, population size, coverage of the genome and marker density (van Ooijen, 1992).
D. CHARACTERIZATION OF LOCI INVOLVED IN QUANTITATIVE
RESISTANCE
In recent years several QTL studies have been undertaken to dissect the quantitative resistance of major crop plants against various pathogens and pests (for review see Young, 1996). Results of these studies contributed towards a better understanding of the genetic basis of resistance. Besides the identification and localization of QTLs involved in the expression of quantitative resistance traits, new insight was gained concerning the race specificity of individual loci, gene-for-gene interactions and the common genetic basis of different components of quantitative resistance. The cosegregation of QTLs for resistance with major resistance genes or other genes with known function might give first hints of the possible function of genes contributing to quantitative resistance.
1. Number of Loci Involved in Quantitative Resistance and Their Genetic Effects Genetic studies using molecular markers have confirmed the oligogenic nature of inheritance for quantitative resistance traits. Between one and ten significant QTLs for resistance have been found for a wide range of different host/pathogen systems explaining between 9 and 90% of the genotypic variance. This is in agreement with the estimates based on quantitative genetic studies using the biometrical approach (Geiger and Heun, 1989). However, most QTL analyses have been performed with only one population, so that the number of identified QTLs is limited to the number of genes that differ between the parents of this specific cross. In addition, in most cases the amount of phenotypic variance explained by all QTLs simultaneously is smaller than the amount which is explained by the genotypic variance given by the heritability estimate (h2). They should be identical if all genetic factors involved in the resistance have been identified. Some experiments have been conducted with a limited number of segregating genotypes «100), a limited number of disease assessments (only one
134 B. Keller, C. Feuillet & M Messmer
environment) or a limited number of markers (<100) which reduces the chances of finding QTLs with small effects. Therefore, one to ten QTLs represent minimal numbers of genes involved in the expression of resistance in the total host germplasm, but it seems to be a reasonable estimate for the number of genes segregating in a cross between two breeding lines.
About one third of the studies revealed the presence of at least one QTL with a major effect, which accounted for 25 to up to 90% of the phenotypic variation.
For example, Dion et al. (1995) found one major QTL that explained between 57 to 84% of the phenotypic variation for blackleg disease resistance in rape seed. These results strongly indicate the presence of major resistance genes which are probably modified by the interaction with the environment or other genes with minor effects. For many host/pathogen systems, both monogenic and quantitative resistance have been described, showing binomial distribution in the seedling test with a defined pathogen isolate and continuous phenotypic distribution at the adult plant stage under field conditions. QTL analyses have been performed on resistance traits showing continuous variation in segregating populations, where known or unknown major resistance genes (conferring monogenic resistance under controlled conditions) may be involved in the expression of quantitative traits. Such major genes mask the effect of genes with minor effects.
After dissection of the quantitative resistance trait into individual QTLs, the genetic effect of the different alleles can be estimated for each QTL. In order to find many genomic regions involved in the expression of quantitative resistance, crosses were usually made between highly resistant (assumed to carry many positive alleles) and highly susceptible lines (presumably without any positive alleles). Nevertheless, in most studies, QTLs with positive and negative additive effects were found in both parents, indicating that both can contribute alleles for improved resistance. For example, Chang et al. (1996) observed four QTLs in an Fs popUlation for resistance of soybean against sudden death syndrome caused by the fungal pathogen Fusarium solani. Three QTLs with the positive allele from the resistant parent reduced the mean disease incidence by 30% and the QTL with the positive allele from the susceptible parent by 10%. If both parents contribute to resistance, transgressive segregation is expected, i.e. some of the progeny should be more resistant or more susceptible than the parents. This holds true for the latter example. While the resistant parent showed 16% and the susceptible parent 59% disease incidence for sudden death syndrome, the Fs progeny ranged between 5 and 95% (Hnetkovsky et aI., 1996).
When homozygous doubled haploid (DH), selfed progeny (Fs' F6, F7) or backcross (BC) populations are used for QTL mapping, only additive and epistatic effects can be estimated, whereas in the case of F2 derived progeny (F2:3, F2:4) one can also estimate the dominance effects for each QTL. In contrast to monogenic resistance genes that often show complete or partial dominant gene action (i.e. the heterozygous plants are nearly as resistant as the homozygous resistant parent) usually all kinds of allelic interactions at the different QTLs were found within the same host/pathogen systems. For example, partial or complete dominance for resistance, overdominance for resistance (heterozygous class is more resistant than each of the homozygous classes), partial dominance towards susceptibility as well as an intermediate reaction (heterozygotes are as resistant as the
mean of the parents) were reported at individual QTLs for resistance of maize against Northern corn leaf blight (Frey mark et aI., 1994) and for resistance of bean against common bacterial blight (Nodari et aI., 1993). Dominance or overdominance was mainly observed at QTLs with major effects for resistance against different populations of downy mildew in pearl millet (Jones et aI., 1995a).
Averaged over all QTLs, the additive effects were predominant in most host/pathogen systems studies so far, e.g. for resistance of maize against sugar cane borer (Bohn et aI., 1996) and gray leaf spot (Maroof et aI., 1996) or resistance in French bean against common bacterial blight (Nodari et aI., 1993) and in pea against Ascochyta pisi (Dirlewanger et aI., 1994). Additive and dominance effects of similar magnitude were reported for resistance in maize against Northern corn leaf blight (Freymark et aI., 1994) and in broccoli against club root (Dion et ai., 1995). In contrast, Li et ai. (1995) found greater dominance effects than additive effects for resistance of rice against sheath blight.
Epistasis is defined as non-allelic or interlocus interaction between different genes (Mather and Jinks, 1971). Epistasis occurs if different loci do not act additively together but influence each other in their phenotypic expression. Two extreme cases of epistasis are known from the interaction of monogenic resistance genes: in the case of complementary type of gene action, two genes need to be present to result in a resistance reaction whereas in the case of duplicated type action the presence of one gene is sufficient to confer resistance. There, two genes can replace each other but do not improve the resistance phenotype if they are combined. The power of detecting epistasis between genes influencing quantitative resistance in general is low and large population sizes are needed to find significant differences between the respective classes of gene combinations. Therefore, in only a few studies were digenic epistatic effects between pairs of QTLs tested. Significant interlocus interaction were found between QTLs for resistance against rice blast in rice (Wang et aI., 1994) and against downy mildew in pearl millet (Jones et ai., 1995a). Maroof et ai. (1996) illustrated the epistatic effects between two QTLs for gray leaf spot resistance in maize. With a large population size they were able to demonstrate that QTL4 had no or only little effect on resistance when QTL1 was homozygous for the allele of the resistant parent similar to a duplicate type of interlocus interaction. While in most studies, interlocus interaction was only tested between QTL with additive effects, Lefebvre and Palloix (1996) tested all pairs of markers in order to detect genomic regions acting in epistasis for resistance of pepper against Phytophthora capsici. For root rot resistance they found three significant digenic interactions (six loci) that explained together 62% of the phenotypic variance.
Only one of these six loci showed an additive effect. This indicates that some QTLs may only be effective in the presence of other QTLs (Lefebvre and Palloix, 1996) similar to the complementary type of interlocus interaction. One QTL without significant additive effect but significant interaction with another QTL was also reported for resistance against tan spot in wheat (Faris et aI., 1997). These results indicate that interlocus interaction might be very important for the inheritance of quantitative resistance traits.
However, epistatic effects are difficult to employ in breeding programs since positive gene combinations of the parental lines will be dissociated in the progeny during cultivar development.
136 B. Keller. C. Feuillet & M Messmer
2. Components of Quantitative Resistance
Quantitative resistance for a given host/pathogen system is often measured in terms of disease severity and results from different components of resistance such as infection frequency, latent period, rate of spore production or infectious period. Different components of resistance might be based on genes operating at different stages of infection. In most host/pathogen systems these components of resistance are correlated to a certain extent indicating that they are controlled by overlapping sets of partial resistance genes. The genetic basis of different components of quantitative resistance was analysed for resistance of maize against Northern com leaf blight. Freymark et al.
(1994) measured the average number of lesions per leaf (number), the percentage of diseased leaf tissue (severity) and the average lesion size (size) at one location in Iowa after artificial inoculation with Setosphaeria turcica race O. The number of lesions was highly correlated with disease severity, whereas lesion size was only weakly correlated with number of lesions. Across all components of resistance they detected six different QTLs: one QTL for all three traits, two QTLs for number of lesions and disease severity, whereas three QTLs were specific for either disease severity or for lesion size. Dingerdissen et al. (1996) examined incubation period and area under the disease progress curve (AUDPC) according to Shaner and Finney (1980) of the same population at three locations in Kenya inoculated with local pathogen races. They found four QTLs for AUDPC that overlapped with the QTLs detected for severity by Freymark et al.
(1994) and two QTLs for incubation period. Although the AUDPC based on 5 ratings of disease severity was moderately correlated with the incubation period, only one QTL was in common, explaining about 10% of the variance for both traits. The other QTL was specific for incubation period and explained 38% of the phenotypic variance. This indicates that lesion number and disease severity might be controlled by the same QTL whereas lesion size and incubation period are under the control of different genes.
Wang et al. (1994) mapped QTLs conferring complete resistance and partial resistance in rice against rice blast. They assessed the partial resistance in terms of lesion number, lesion size and diseased leaf area in the greenhouse and diseased leaf area in the field at two locations. Under greenhouse conditions, five genomic regions were found for complete resistance. For partial resistance, two QTLs were involved in lesion number, diseased leaf area and lesion size, seven QTLs contributed both to lesion number and diseased leaf area whereas three QTLs were specific for lesion number. Thus, a large set of genes determines the number of lesions, whereas no specific genes affecting only the lesion size or diseased leaf area were found. Eight QTLs were found for resistance in the field under natural infection pressure: four of them coincided with genes for complete resistance and three of them with QTLs for partial resistance under greenhouse conditions.
Molecular markers have also been used to examine the genetic basis of the resistance phenotype dependent on the developmental stages or particular plant organs. Steffenson et al. (1996) studied the difference between seedling and adult resistance of barley after infection with a single conidial isolate of Pyrenophora teres causing net blotch and Cochliobolus sativus causing spot blotch. Seven QTLs were found for adult plant resistance against net blotch, explaining 68% of the variance in the field, while three QTLs explained 50% of the phenotypic variance at seedling resistance. Two of these
QTLs were in common but less effective at the adult stage in the field. For spot blotch, one QTL with major effect was detected for seedling resistance tested in the growth chamber.
The same QTL was found for adult plant resistance but explained only 9% of the variation, whereas a second QTL explaining 62% of the field resistance was not detected at the seedling stage. By testing different inoculation methods, Danesh et al.
(1994) could show that the infection site has an influence on the effectiveness of the QTLs detected for bacterial wilt in tomato.
Such a detailed analysis of the genetic basis of different resistance components