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a Plant Breeding and Acclimatization Institute, Mlochów Research Center, 05-832 Rozalin, Poland
b Max-Planck Institute for Breeding Research, Carl von Linne Weg 10, D-50829 Köln, Germany
gebhardt{at}mpiz-koeln.mpg.de
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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Abbreviations: AFLP, amplified fragment length polymorphism • cfu, colony forming units • GLM, general linear model • LG, linkage group • QTL, quantitative trait locus • RFLP, restriction fragment length polymorphism • RGL, resistance gene like
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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DNA markers provide excellent tools for the analysis of QTL controlling genetic resistance to Erwinia species. Knowledge of number and position of QTL in resistant germplasm allows the development of marker assisted selection strategies for efficient introduction and maintenance of desired resistance traits in a breeding pool.
Routine testing since 1985 of genetic material for resistance to tuber soft rot resulted in identification of several diploid interspecific hybrid clones with superior resistance. Their pedigree contains—among others—the wild species Solanum chacoense, known as a source of resistance to Erwinia spp. (Zimnoch-Guzowska et al., 1999a, b). A cross between one of these highly resistant hybrids with a susceptible clone generated a progeny that was used in the study reported here to map QTL for resistance of tubers and leaves to Erwinia carotovora ssp. atroseptica.
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Materials and methods |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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The pollen parent DG 81-68 (P2) was susceptible to tuber soft rot, but resistant to wart and PVX. The pedigree of this clone for the seed parent was tbr x [(chc x chc) x tbr] and for the pollen parent tbr x [(chc x yun) x tbr].
Bacterial Cultures and Inoculum Preparation
Two isolates of Erwinia carotovora ssp. atroseptica (Eca) were used for tuber and leaf inoculation. In 1994, the isolate Eca SCRI #1039 which originated from a blackleg infected potato plant in Scotland was kindly provided by Dr. M. Perombelon (Scottish Crop Research Institute, Dundee, UK) ( Bain and Perombelon, 1990). In 1995, the isolate Eca M 2/95 was obtained from a blackleg infected potato plant at Mlochów, Poland.
Bacteria were grown at 25°C on plates of Luria Broth Base, Miller medium (SIGMA, St. Louis, MO) containing 10 g bacto-tryptone, 5 g bacto-yeast extract, 10 g sodium chloride, and 12 g agar per liter deionized water. After 24 h of growth, bacterial cells were washed out from the surface of the medium with sterile, distilled water and adjusted to an optical density of 0.80 at 530 nm (Beckman spectrophotometer DU 640; Beckman Instruments, Inc., Palo Alto, CA) equivalent to 5 x 108 cfu/mL.
Test for Tuber Resistance
Tubers free of mechanical damage and disease symptoms were washed and conditioned at 18 to 20°C overnight. On the next day, tubers were sprayed with 70% (v/v) ethanol before inoculation. For inoculation, tubers were pricked once by inserting an empty sterile pipette tip in the central part of the tuber (Austin et al., 1988). The tubers were then point inoculated by adding 50 µL bacterial suspension (Eca, 6.8 x 108 cfu/mL) to each tip and pressing the tip 8 to 10 mm deep into the tuber (Zimnoch-Guzowska et al., 1996). Tubers were incubated in the dark in mist chambers at 27°C and 95% relative humidity. After 72 h incubation, tubers were vertically sliced and the width of decayed tissue was measured in millimeters. Resistance level of each genotype was calculated as the mean of three infected tubers in 1994 and of five infected tubers in 1995. Two independent tests were performed in both years during the winter season (datasets TR94_1, TR94_2, TR95_1 and TR95_2). In addition, results of the two tests per year were averaged over each year (datasets TR94 and TR95) and both years of testing (dataset TR94/95). Four cultivars—Irys, Sokól, Tarpan, and Grot—were included as standards. Normality of the distribution of phenotypic data in the Erwinia population was checked by standard 
2 test. Effects of genotypes, years, and interaction between genotypes and years was assessed by the Anova procedure of the STATISTICA software package (StatSoft, Inc., 1997).
Test for Leaf Resistance
Tests were performed according to Zimnoch-Guzowska et al. (1999b) on detached leaves of 6-wk-old potato plants. Three leaves with petioles were collected per genotype from the middle part of plants grown in a screen house. Petioles were dipped (about 20 mm deep) in a glass vessel containing 25 mL of an Eca inoculum suspension (5 x 108 cfu/mL). As a control, three leaves per genotype were kept in a glass vessel with sterile water. The detached leaves were incubated at 26°C for 48 h in a mist chamber with 95% relative humidity and 16 h light. Resistance was evaluated by visually assessing the area of rotted leaf tissue on the basis of a 1-to-5 scale, where 5 = 0 to 5%, 4 = 5 to 25%, 3 = 25 to 60%, 2 = 60 to 85%, and 1 = 85 to 100% of rotted leaf area. Resistance per genotype was calculated as the average score of three infected leaves. Three independent tests were performed in 1994, two in 1995, and another two tests in 1996 (datasets LR94_1, LR94_2, LR94_3, LR95_1, LR95_2, LR96_1 and LR96_2). In addition, scoring data of two or three tests per year were averaged over individual years (datasets LR94, LR95 and LR96) and all 3 yr of testing (LR94/95/96). The standards included in the test were the same as used for testing tuber resistance. Statistical analysis of the data was the same as used for tuber resistance. Correlation between tuber and leaf resistance was assessed by Pearson's correlation coefficient.
DNA Isolation
Leaf material was harvested from 6-wk-old plants, frozen in liquid nitrogen, and freeze dried. Total genomic DNA was extracted and purified from 300 mg freeze dried potato leaf tissue as described elsewhere (Schäfer-Pregl et al., 1998).
RFLP (Restriction Fragment Length Polymorphism) Analysis
Between 4 and 5 µg total genomic DNA of parents and offspring were digested with TaqI, RsaI, or MseI. Size separation of restriction fragments, blotting, and hybridization of nylon filters to marker probes was performed as previously described (Gebhardt et al., 1989). Thirty-one polymorphic RFLP markers of known map position (Gebhardt et al., 1991, 2000) were scored in the parents and in a subset of 80 F1 plants. Loci harboring RGL sequences were identified by RFLP mapping in the Erwinia population with resistance gene homologous fragments 1.2.1, 1.2.4 and 3.3.13 described by Leister et al. (1996) used as markers.
AFLP (Amplified Fragment Length Polymorphism) Analysis
AFLP fingerprints were generated from 0.5 µg total genomic DNA of parents and 158 F1 plants according to Vos et al. (1995). Adapter and primer sequences for HindIII–MseI and EcoRI–MseI primer combinations have been described (Vos et al., 1995; Meksem et al., 1995). The nine pairs of +3 nucleotides extensions used are given in Table 2
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QTL Analysis
A two sample t-test was performed on the phenotypic means of the two genotypic classes distinguished by presence or absence of single AFLP or RFLP fragments. Programs written with SAS Language (SAS Institute Inc., 1989, 1990) performed the t-test automatically on all single marker fragments and all phenotypic datasets (Schäfer-Pregl et al., 1998). When analyzing single marker fragments, the variance explained (R2) by the marker was computed by relating the sum of squares caused by allelic differences at the marker locus to the total sum of squares. The H0 hypothesis of no QTL linked to a single marker was rejected when (i) means of marker genotypic classes were significantly different in at least one individual test with P < 0.01 and in the dataset(s) averaged over 1, 2, or 3 yr with P < 0.05, (ii) means of marker genotypic classes were significantly different in at least two individual tests with P < 0.05 and in the dataset(s) averaged over 1, 2, or 3 yr with P < 0.05, and (iii) means of marker genotypic classes were significantly different in at least two individual tests with P < 0.10 and in the dataset(s) averaged over 1, 2, or 3 yr with P < 0.01. The rational for the choice of thresholds was the reduced risk of declaring a false positive QTL when the same marker was significant in repeated trials and/or when data from independent tests were averaged over 
1 yr. Marker loci linked to each other and all revealing significant effects were assumed to be linked to the same putative QTL. QTL were positioned on parental and/or common linkage groups on the basis of the location of the marker with the most significant and most reproducible effect.
In offspring of heterozygous, non-inbred parents, up to four different quantitative trait alleles segregate at each QT locus. Let Q1 and Q2 be the alleles of the seed parent P1 and Q3 and Q4 the alleles of the pollen parent P2. In the F1 offspring, four quantitative trait allele combinations Q1Q3, Q1Q4, Q2 Q3, and Q2Q4 are expected to segregate with a ratio of 1 : 1 : 1 : 1. For assessing the effects of the four allele combinations, a marker allele of parent P1, linked to a putative QTL, was combined with a different marker allele of parent P2 located in an "opposite" position on the P2 linkage group. Such a pair of marker alleles allowed distinction of four marker Genotypic Classes 13, 14, 23, and 24 in the offspring (13: P1 and P2 fragments both present, 14: P1 fragment present, P2 fragment absent, 23: P1 fragment absent, P2 fragment present, 24: P1 and P2 fragments both absent). In cases where no "allelic bridge" (Ritter et al., 1990) was available between the P1 and P2 marker allele (a frequent situation when using AFLP analysis), allelism of such marker fragment pairs was not evident. In these cases, close linkage between two markers was considered equivalent to allelism in the capacity to mark alternative allelic states at a linked QTL. To identify the most suitable pairs of marker fragments in a map segment, several combinations were tested. The four marker classes were tested for deviation from the expected segregation ratio of 1 : 1 : 1 : 1 by a 2 test, and for significant differences between phenotypic means (P < 0.05) by using the SAS procedure GLM with Scheffe's multiple comparison procedure.
For multiple QTL modeling, the General Factorial ANOVA procedure of the SPSS software (Norusis, 1992) was used to calculate the adjusted variance explained by multiple sets of markers.
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Results |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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AFLP-RFLP Linkage Map
AFLP fingerprinting of the Erwinia population using nine different primer combinations resulted in 421 segregating AFLP fragments. Primer combinations, numbers of fragments scored for each primer combination, and their distribution among the parents (present in P1 and absent in P2, present in P2 and absent in P1, present in both parents) are listed in Table 2. AFLP data were complemented by segregation data of RFLP markers with known map position. Ninety-six percent of the AFLP fragments (404) could be assigned to linkage groups corresponding to one of the 12 potato chromosomes. The distribution of AFLP loci on the linkage maps was unequal. Fifty-three percent (213) of the AFLP fragments mapped to only 20 different positions, whereas other regions of the genome were not covered or scarcely populated by AFLP markers. Fourteen AFLP clusters were positioned as pairs opposite each other on the two parental maps of chromosomes I, III, IV, VI, VIII, IX, and X. Figure 2
shows the maps of the 24 parental linkage groups based on 139 AFLP and RFLP marker loci. Linkage groups based on marker fragments shared among the parents have been omitted from Fig. 2 for reasons of clarity, except for few markers that were strategic for anchoring a linkage group or for QTL detection. Twelve loci corresponding to RGL sequences were identified by RFLP mapping using the three marker probes 1.2.1, 1.2.4, and 3.3.13. Six of those have been identified previously in an unrelated mapping population (Leister et al., 1996): St1.2.4(a) on LG I, St1.2.1(b) on LG III, St3.3.13(c) on LG VI, St1.2.4(c) on LG X, and St3.3.13(a) and St1.2.1(a) on LG XI. Six others were newly discovered: St3.3.13(d) on LG I, St1.2.4(e) on LG IV, St1.2.4(g) on LG VII, St1.2.1(d) on LG XI, and St3.3.13(e) and St1.2.4(f) on LG XII (Fig. 2).
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Effects on leaf resistance to E. carotovora ssp. atroseptica were less consistent than the effects on tuber resistance. Of 15 putative QTL on 10 chromosomes (Table 4), only Eca3 on LG III was detected in all 3 yr, in four of seven independent tests and in combined datasets LR96 and LR94/95/96 . Marker alleles of the more susceptible parent DG 81-68 (P2) explained up to 5% of the phenotypic variance (Table 4). This QTL mapped to the same map segment as the QTL Eca3 for tuber resistance (Fig. 2). Whereas effects on tuber resistance at Eca3 were detected with marker alleles of P1, the effects on leaf resistance were detected with marker alleles of P2 (Fig. 2). The remaining effects were detected in one or two of seven individual tests and in at least one of the combined datasets LR94, LR95, LR96 and LR94/95/96 (Table 4). The effects on leaf resistance linked to markers on linkage groups I, II (Eca2B), V, X, and XI were identified mainly in single years. Among those, the most reproducible (two significant individual tests) were Eca2B, Eca5B, Eca10A, and Eca10B on linkage groups II, V, and X, respectively. Six putative QTL, Eca2A, Eca3, Eca7A, Eca8A and Eca8B, Eca9, and Eca11B on linkage groups II, III, VII, VIII, IX, X, and XI, respectively (Fig. 2), were identified on the basis of individual tests and the combined dataset LR94/95/96. Multiple QTL models were developed from markers linked to putative QTL and datasets LR94, LR95, LR96, and LR94/95/96. The markers used are indicated in Table 4. Eight, six, and four markers, different in each year, explained 14.1, 23.8, and 19.9% of the variance in dataset LR94, LR95, and LR96, respectively. In the combined dataset LR94/95/96, six markers linked to QTL Eca2A, Eca3, Eca7A, Eca8A, Eca8B, and Eca9 explained 43% of the variance.
Besides Eca3 on LG III, effects on both tuber and leaf resistance to E. carotovora ssp. atroseptica were linked to markers located in similar map segments on LG I (Eca1A, Eca1B), LG II (Eca2A), LG V (Eca5B), LG IX (Eca9), LG X (Eca10B), and LG XI (Eca11)(Fig. 2). Only two markers, HM4-14 on LG II and EM1-17 on LGXI, were significant for both traits.
GLM Analysis
A QTL effect in progeny of non-inbred parents is the result of the interaction of four alleles, two of each parent (Leonards-Schippers et al., 1994). Analysis by the t-test of differences between means of two marker classes considers only two alleles at a time, either the maternal or the paternal alleles. To estimate more precisely effects of QTL allele combinations, pairs of maternal and paternal marker fragments were analyzed by GLM for differences between the phenotypic means of four marker classes (MATERIALS AND METHODS). For analyzing the QTL Eca1A and Eca6A on LG I and VI, respectively, maternal and paternal fragments of the RGL marker 3.3.13 were combined. Only results of the analysis of datasets TR94/95 and LR94/95/96 are reported here. Significant differences (P < 0.05) between at least two of the four marker classes were found for five pairs of marker fragments, three linked to QTL for tuber resistance (Eca1A, Eca4A, Eca6A) and two linked to QTL for leaf resistance to Eca (Eca2A, Eca11B, Fig. 3)
. The RGL locus St3.3.13(c) linked to Eca6A on LG VI explained 15.7% of the variance when using the GLM procedure, whereas AFLP marker HM5-17, linked to the same QTL, explained only 5.3% when using the t-test (Table 3). QTL Eca11B on LG XI was significant with GLM in three individual tests (LR94_3, LR96_1, LR96_2, not shown) and explained 13.3% of the variance of dataset LR94/95/96, whereas it did not reach the threshold criteria used for the t-test analysis of single markers. Results for the other three QTL (Eca1A, Eca4A and Eca2A) were similar for GLM and t-test analysis. A multiple QTL model was developed considering four alleles each at RGL marker loci St3.3.13(d) and St3.3.13(c) on linkage groups I and VI, respectively. In this model, the two RGL loci together explained 28.9% of the variance of dataset TR94/95.
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Discussion |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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QTL analysis revealed that the genetic control of resistance to E. carotovora ssp. atroseptica is complex and truly polygenic. QTL for tuber resistance were more reproducible than QTL for leaf resistance. No QTL was, however, reproducibly detected in all single tests. This indicates the difficulty of reliably assessing resistance to E. carotovora ssp. atroseptica at the phenotypic level. For the identification of QTL, we considered, therefore, not only the data of single resistance tests but also the data averaged over all tests per year and all years of testing. The rationale was that the size of an effect occurring by chance alone in a single test will be reduced when data are averaged over several independent tests whereas non random effects will persist or even increase when analyzing pooled data. In fact, in some cases, error probability levels were lower at a particular marker locus when using pooled data for QTL analysis when compared with single test data. Examples are Eca4A for tuber resistance and Eca8A for leaf resistance (Tables 3 and 4). In contrast, effects detected with pooled data were accepted as putative QTL only when they were supported also by single tests. It cannot be excluded, however, that none of the QTL reported did occur by chance alone. For the purpose of identifying the genomic positions of factors controlling resistance to E. carotovora ssp. atroseptica, we considered it more important to avoid a type II error (rejecting an existing QTL) than a type I error (declaring as QTL a chance effect). For the purpose of marker assisted selection for resistance to E. carotovora ssp. atroseptica, only the largest and most reproducible effects will be considered.
On the basis of the criteria chosen for declaring a QTL present, we estimate that at least 13 factors control tuber resistance to E. carotovora ssp. atroseptica (Table 3). This number may be underestimated as QTL may have escaped detection because of the scarcity of markers in few map regions and because of the fact that more than one gene may cause the QTL effects detected in a specific genomic region. Since only half of the Erwinia mapping population was genotyped for RFLP markers to reduce labor, QTL effects linked to RFLP marker loci were estimated with less precision, whereas the whole population of 158 plants was genotyped for AFLP markers. Examples are Eca8A linked to the GBSSI gene marker (encoding granule bound starch synthase I) on linkage group VIII and Eca9 linked to marker CP110 on linkage group IX (Tables 3 and 4).
Eca1A on linkage group I was the most prominent and consistent QTL for tuber resistance to E. carotovora ssp. atroseptica, and is, therefore, the best target for marker assisted selection. Eca1A was linked to the anchor marker CP108 which is tightly linked to the self-incompatibility locus S mapped in a different cross (Gebhardt et al., 1991). In the Erwinia population, highly distorted segregation ratios as expected for marker alleles linked to S on the paternal linkage group I were not observed. This indicates that only compatible S alleles were present in the parents, probably due to their highly interspecific pedigree. When linked to an incompatible S allele, transmission to the offspring of paternal resistance alleles at the Eca1A locus will be prevented or greatly reduced. This has to be considered when resistance alleles at this QTL are going to be introgressed into a breeding pool.
On the phenotypic level, correlation between tuber and leaf resistance to E. carotovora ssp. atroseptica was low. This observation is consistent with the fact that most QTL effects on tuber resistance were linked to markers different from the ones linked to QTL effects on leaf resistance. This suggests that most of the factors controlling tuber resistance in the Erwinia population are different from factors controlling leaf resistance.
On the basis of the phenotypic analysis of resistance to E. carotovora ssp. atroseptica, interaction between genotypes and years was not significant for tuber resistance but was significant for leaf resistance. QTL analysis of leaf resistance showed, in fact, more inconsistency between single years of testing than QTL analysis of tuber resistance. Effects on leaf resistance were mainly detected in single years and the variance explained by individual QTL effects was low. This indicates that different members of the group of factors controlling leaf resistance were effective in different years. Evaluation of leaf resistance may be more sensitive to changes of environmental factors than evaluation of tuber resistance, for example, to the change of the Eca isolate used for inoculation in years 1994 and 1995 versus 1996. Despite the significant genotype x year interaction, six of 15 rather small QTL effects on leaf resistance persisted when data of seven independent tests were pooled (Table 4). These QTL may represent a subgroup of factors which is less dependent on the environment.
Molecular cloning and sequence analysis of a number of major plant genes for resistance has revealed structural similarities among genes of different plant species that confer resistance to different types of pathogens like bacteria, fungi, viruses, and nematodes (reviewed in Hammond-Kosack and Jones, 1997). Characteristic for one class of plant genes for pathogen resistance is a nucleotide binding site domain (NBS) and, downstream from the NBS domain, a peptide motif (GLPLAL) of unclear functional significance. These sequence motifs have been used to amplify by PCR and to map by RFLP analysis a number of potato gene fragments with sequence similarity to known resistance genes. On the basis of cosegregation with major resistance loci, some RGL sequences are candidates for potato genes for resistance (Leister et al., 1996). When three different types of RGLs were mapped by the RFLP assay in the Erwinia population, 12 RGL loci were identified, some of which were linked to QTL for resistance to E. carotovora ssp. atroseptica. Among those were the two most significant and reproducible QTL for tuber resistance, Eca1A and Eca6A, which were linked to RGL loci St3.3.13(d) and St1.2.4(a), both on linkage group I, and to St3.3.13(c) on linkage group VI, respectively. The total number of RGL loci present in the potato genome is not known to date. It is most certainly higher than the 12 loci mapped in the Erwinia population because (i) all three RGL markers used for RFLP mapping detect multigene families and (ii) the markers represent only three of an unknown number of different RGL types. Linkages between RGL loci and QTL for resistance to E. carotovora ssp. atroseptica may have been detected, therefore, by chance alone. Alternatively, these linkages suggest that components of quantitative resistance to E. carotovora ssp. atroseptica may be controlled by factors which, at the molecular level, are similar to genes encoding qualitative resistance. Linkage among QTL for resistance to pathogens and RGLs have also been observed in sunflower, Helianthus annuus L. (Gentzbittel et al., 1998).
Several QTL for resistance to E. carotovora ssp. atroseptica do map to similar positions as occupied by QTL and/or major genes for resistance to various pathogens in potato, tomato, or even tobacco. This was inferred by comparing positions of qualitative and quantitative resistance loci on different molecular maps of potato, tomato, and tobacco on the basis of linkage to common anchor RFLP loci. Most remarkable in this respect are QTL Eca1A on linkage group I, Eca6A on linkage group VI, and Eca11A and Eca11B on linkage group XI. As inferred from the positions of RGL locus St1.2.4(a) and RFLP locus CP108 tightly linked to Eca1A on linkage group I (Fig. 2), this major QTL for resistance to E. carotovora ssp. atroseptica occupies in the potato genome a similar position as, in tomato, a family of genes for resistance to the fungus Fulvia fulva (Cooke) Cif. (syn = Cladosporium fulvum Cooke). Cf genes have been analyzed at the molecular level and share structural domains that are typical for one class of plant resistance genes (Jones et al., 1994; Parniske et al., 1997). The RGL locus St3.3.13(c) on linkage group VI (Fig. 2) anchors the QTL Eca6A to a genomic region which, in tomato, contains a number of qualitative and quantitative genes for resistance to nematodes, fungi, viruses, and even insects (Leister et al., 1996). The nematode resistance gene Mi and the Cf-2 gene for resistance to Fulvia fulva are located among others in this region of the tomato genome. Both genes have been cloned and shown to be "typical" members of resistance gene families (Milligan et al., 1998; Dixon et al., 1996). In potato, no major gene for resistance has yet been located in this region. Besides Eca6A, a QTL for resistance to the Oomycete Phytophthora infestans (Mont) de Bary (Oberhagemann et al., 1999) maps to a similar position based on the anchor marker GP79 which is linked to St3.3.13(c) (Leister et al., 1996). RGL loci St3.3.13(a) and St1.2.1(a) as well as RFLP loci GP125 and GP185 (Fig. 2) anchor QTLs Eca11A and Eca11B, respectively, to two distal map segments on linkage group XI which are syntenic with segments of the potato, tomato, or tobacco genome containing genes for resistance to various other pathogens (Leister et al., 1996). Eca11B maps to a similar position as, in potato, QTL and major genes R3, R6, and R7 for resistance to Phytophthora infestans and, in tomato, the I2 gene for resistance to Fusarium oxysporum Schlechtend.:Fr. Eca11A maps to a similar position as yet another potato QTL for resistance to Phytophthora infestans, virus resistance genes Ry and N of potato and tobacco, respectively, and the potato gene Sen1 for resistance to the fungus Synchytrium endobioticum (Hehl et al., 1999). Genes N and I2 have been cloned and molecularly characterized (Whitham et al., 1994; Ori et al., 1997; Simons et al., 1998). Both are members of gene families and contain structural domains characteristic for plant resistance genes, among others the NBS domain which also defines the RGL loci identified in the syntenic regions of the potato genome.
A considerable number of mapping studies of factors controlling qualitative and quantitative resistance to various agronomically important pathogens has been carried out to date in the closely related crop species potato and tomato. The only links between mapping experiments of different research groups in different genetic materials are RFLP markers used in common and the alignment existing between molecular maps of tomato and potato (Bonierbale et al., 1988, Gebhardt et al., 1991, Tanksley et al., 1992). Although most certainly incomplete, a genome wide view on hotspots of resistance factors in important crop species of the Solanaceae family emerges from this research. These hotspots are characterized by presence of different types and specificities of resistance traits in genomic regions which also contain several molecular variants of known plant resistance genes. Four examples on potato–tomato chromosomes I, VI, and XI and on an unidentified tobacco chromosome have been discussed here in more detail. The clustering of qualitative and quantitative resistance traits with RGLs may be observed as a result of reduced recombination rates or of incomplete information on total numbers and positions of resistance genes and RGLs. On the other hand, hotspots for pathogen resistance and RGLs suggest that (i) genes conferring qualitative or quantitative resistance against different pathogens may have evolved from common ancestor(s) by local gene duplications and (ii) components of quantitative resistance to pathogens may be controlled by genes that are structurally and functionally similar to known major genes for resistance. Since the resolution of linkage analysis is not sufficient to distinguish between these possibilities, molecular and functional analysis of the total genome in these regions will be required.£ojkowska Kelman 1989; Norusis Inc. 1992; StatSoft 1997
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ACKNOWLEDGMENTS |
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Received for publication June 30, 1999.
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TOPABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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