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Plant Biotechnology Division, Dep. of Plant Agriculture, Univ. of Guelph, Guelph, ON, Canada, N1G 2W1
* Corresponding author (ppauls{at}uoguelph.ca)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Abbreviations: AFLP, amplified fragment length polymorphism • CBB, common bacterial blight • cM, centimorgan • GH, growth habit • LOD, logarithmic of odds ratio • MAS, marker-assisted selection • QTL, quantitative trait loci • RAPD, random amplified polymorphic DNA • RFLP, restriction fragment length polymorphism • SCAR, sequence characterized amplified region • SSR, simple sequence repeat
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Common bean (2n = 2x = 22) has the estimated size of genome of 637 Mbp or 0.66 pg/1C (Arumuganathan and Earle, 1991). Several bean molecular linkage maps have been constructed (Adam-Blondon et al., 1994; Jung et al., 1996, 1997; Nodari et al., 1993; Vallejos et al., 1992). On the basis of the shared RFLP markers among these maps, Freyre et al. (1998) integrated information from several maps into a core linkage map for bean. Other studies identified specific markers for disease resistance (Adam-Blondon et al., 1994; Ariyarathne et al., 1999; Bai et al., 1997; Haley et al., 1993; Johnson et al., 1995; Jung et al., 1997; Miklas et al., 1996, 1998, 2001; Nodari et al., 1993; Park et al., 1999a; Schneider et al., 2001; Young and Kelly, 1997; Yu et al., 1998), morphological traits (Jung et al., 1996; Park et al., 1999b), seed size (Park et al., 2000), canning quality (Walters et al., 1997), tolerance to drought stress (Schneider et al., 1997), and characters that are affected by domestication processes in common bean (Koinange et al., 1996). Only few of these traits have been integrated into the core map for common bean (Freyre et al., 1998; Gepts, 1999).
In the present study, we examined the genetic loci associated with 14 quantitative traits responsible for seed yield, plant architecture, and plant development. These traits are important to all common bean breeding programs in the world. The current study also provided estimates of the minimum number of QTL affecting each trait, identified the chromosomal locations of these loci, estimated the magnitudes of the effects for each QTL and tested the robustness of these QTL across environments. Connections between the linkage map developed in this study with the bean core map were also made.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Data on yield components and other morphological traits (Table 1) were collected by sampling 15 plants per row at physiological maturity (90% of the pods were yellow-green to brown). Data on growth habit, days to flowering, days to maturity, lodging, and seed yield were based on evaluation of all the plants in a row.
DNA Isolation
Twenty-four seeds of each F2:4 line, OAC Seaforth, and OAC 95-4 were planted in a growth room. An equal quantity of fresh leaf tissue from an average of 20 plants of each line were harvested at the first trifoliate leaf stage. Genomic DNA was isolated following the CTAB method with minor modification (Doyle and Doyle, 1990).
RFLP Assay
Bean genomic clones (kindly provided by Dr. J. Tohme of the Biotechnology Unit, CIAT, Cali, Colombia) were hybridized to restriction enzyme digested (BamHI, DraI, EcoRI, EcoRV, HindIII, PstI, PvuI, and SmaI) parental DNAs to identify clone–enzyme combinations that resulted in polymorphic patterns. The restriction enzyme that gave the clearest RFLP for a particular clone was used to digest the DNA samples from each of the 142 F2:4 lines.
The clones used to probe the Southern blots were labeled with Digoxigenin (DIG) (Boehringer Mannheim, Laval, QC, Canada). Hybridization and washing steps were performed following manufacturer's instruction. The membrane was placed in contact with X-ray film for approximately 40 min prior to development. The RFLP markers were named following the previous nomenclature that consisted of three letters (BNG) followed by the serial number of the clones (Vallejos et al., 1992). In addition, the enzyme that was used to cut the genomic DNA was also included for each clone.
RAPD, AFLP, SCAR, and SSR Assays
The RAPD procedure was the same as described in Bai et al. (1997). The RAPD markers were named according to their primer names designated at the University of British Columbia or Operon Technologies Inc. (Alameda, CA) and the molecular weight of the band.
The AFLP analysis was performed essentially as described in the AFLP Plant Mapping Kit protocol (Perkin Elmer, Foster City, CA; Part #402083) except that the selective amplification was done with nonfluorescently labeled EcoRI and MseI primers. The selective amplifications products were then loaded onto a 6% (w/v) denaturing polyacrylamide gel. The gel was run in 1x TBE at 40 W for 4 h and silver-stained following the protocols described by Bassam et al. (1991). AFLP marker nomenclature was designed to facilitate marker transfer among laboratories. The first three letters represent the EcoRI + 3 selective nucleotides. The second three letters represent the MseI + 3 selective nucleotides. The number following these six letters is the size of the polymorphic amplified fragment for a given primer pair.
The primer sequence for the SCARxan-700 was kindly provided by Dr. S. Beebe (CIAT, Cali, Colombia). The marker was linked to common bacterial blight resistance in XAN 159 line (S. Beebe, 1998, personal communication). The forward and reverse sequences of this primer are 5' ttttgtatgtgtttctctggtgtag 3' and 5' atctcttttatccctcctttgtgtg 3', respectively. The amplification was done at the annealing temperature of 58°C with the following reaction mixture: 50 ng of bean genomic DNA, 1 mM of MgCl2, 0.15 µM each of forward and reverse primers, 200 µM each of dNTPs, 10x PCR buffer (Gibco BRL, Life Tech.), and 1 U of Taq Polymerase (Gibco BRL, Life Tech.).
The primer sequences (Table 2) for the SSR assays were kindly provided by Dr. Kangfu Yu, Agriculture and Agrifood Canada, Greenhouse and Processing Crops Centre, Harrow, ON. The PCR amplifications were done in a PTC-100 Thermocycler (MJ Research Inc., Watertown, MA). The 15-µL PCR mixture used for the SSR assay contained 25 ng of bean genomic DNA, 1 mM of MgCl2, 0.15 µM each of forward and reverse primers, 200 µM each of dNTP, 10x PCR buffer (Gibco BRL, Life Tech.), and 1 U of Taq Polymerase (Gibco BRL, Life Tech.). The amplification process was performed for 35 cycles with the following temperature profile: 94°C for 30 s, (47–50)°C for 30 s, and 68°C for 1 min. A total of 7.5 µL of PCR products were separated on a 10% (w/v) polyacrylamide gel run at 50 W for 3 h. The gels were stained with silver nitrate to visualize the fragments (Bassam et al., 1991). The SSR loci (Table 2) were named following the nomenclature described previously in Yu et al. (1999).
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2. The mean values for each trait across the replications and locations were used for the QTL analyses. The goodness-of-fit of the observed segregation ratio for each marker was tested against the expected ratios of 1:2:1 or 3:1. Linkage groups of the markers were determined by the Group command of MAPMAKER/EXP program version 3.0 (Lander et al., 1987) at a LOD score of 3.0 with a maximum distance between two markers of 50 centimorgans (cM). A subset of RFLP markers found from each group was used as a framework. The order within this subset of markers was determined by the Compare command at a LOD score of 3.0. Additional markers were subsequently added by the Try command. The best order of the markers was then verified by the Ripple command with a LOD score of 2.0 or higher. The associations between molecular marker and QTL were analyzed by interval mapping using MAPMAKER/QTL (Lander and Botstein, 1989). A LOD score of 2.5 or higher was used to declare cosegregation of putative QTL and genetic markers. The amount of phenotypic variation simultaneously explained by all QTL found for a given trait was determined by a multiple QTL model. |
RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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0.10). RFLP markers with distorted segregation ratios were placed on linkage group G1 and G2 (Fig. 1). These markers had a greater frequency of heterozygotes and a smaller frequency of homozygotes either for OAC Seaforth or OAC 95-4 alleles. Seven out of 11 SSR markers fit the 1:2:1 ratio. Thirty-seven of the 49 AFLP markers fit the expected 3:1 ratio (P 0.05). The alignment of the current map with that of Vallejos et al. (1992) identified 30 RFLP loci in common positions (Fig. 1).
Field Data Analysis
The means of the parental lines and the F2:4 lines, and the variance components of the F2:4 lines for the Elora, Woodstock, and mean locations are shown in Table 3. For the F2:4 lines, means of most traits, except for days to flowering, days to maturity, branch angle, and upper pods, were higher at Woodstock. Deviations from normality were found for seeds per pod, upper pods, 100-seed weight, and lodging. A logarithmic transformation was applied to these data sets prior to QTL analysis. Genotypic variances for traits in the F2:4 population varied according to the locations and were significant for all the traits that were measured (Table 3). However, the estimates in single locations were biased upwards because there were no estimates of the genotype x environment variance components. Significant genotype x environment interactions were observed for seed yield, plant height, days to flowering, maturity, branch angle, total branch, hypocotyl diameter, pods per plant, upper pods, harvest index, and lodging. No significant genotype x environment interactions were observed for total nodes, seeds per pod, and 100-seed weight.
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2 analysis showed a good fit of the growth habit distribution to a 1:2:1 ratio (2 = 0.761, 0.50 < P < 0.70) indicating that a single gene determined the trait. On the basis of the result of the segregation analysis, the gene for growth habit (GH) was mapped along with the molecular markers.
QTL Analysis
Significant associations between molecular markers and putative QTL that were measured for the F2:4 population in the study were found for all traits. In total, 20 QTL were detected for the 14 traits that were analyzed (Table 5). The number of QTL identified per trait ranged from one to three. Single QTL (LOD = 2.5) were found for days to flowering, total nodes, total branch, hypocotyl diameter, pods per plant, upper pods, seeds per pod, harvest index, and lodging. Two QTL were identified for days to maturity, plant height, branch angle, and 100-seed weight. Three QTL were found for seed yield. At least one QTL was detected on every linkage group, except on linkage groups G4, G6, and G12. The largest number of QTL (5 QTL) was found on linkage group G11. The multiple QTL model for each trait showed that these genomic regions accounted for 11.3 to 43.1% of the total phenotypic variation of the traits.
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The morphological marker (GH), located at the end of linkage group G11 was significantly associated with QTL for days to flowering, days to maturity, and angle. This region explained 24.2, 29.4, and 11.2% of the variation of days to flowering, days to maturity, and angle, respectively. For the first two traits, this region acted in a partial dominance manner and the allele from OAC 95-4 delayed the flowering date and maturity. The genetic locus near the SCARxan-700 marker accounted for 16.8% of the phenotypic variation for days to maturity.
Two QTL were detected for plant height. A genomic region on linkage group G1 at 6.0 cM from BNG42/PstI explained 36.2% of the variation in plant height. It acted in an overdominant fashion and the allele from OAC 95-4 increased plant height. Another genomic region near the UBC446-1200 locus on linkage group G3 explained 8.6% of the phenotypic variation in plant height. The allele from OAC Seaforth increased the height of the plants. Both QTL simultaneously explained 38.8% of the variation in plant height.
A QTL for total nodes was detected near an AFLP marker AAGCAT-520 on linkage group G9. This genomic region explained 28.1% of phenotypic variation for this trait. A QTL on linkage group G2 was identified for total branch and accounted for 13.9% of the phenotypic variation for this trait. The angle of the branches to the main stem is an important component of upright plant architecture. Two QTL were detected that contributed to the angle of the branch and they collectively accounted for 16.6% of the phenotypic variation for this trait. These genomic regions (BNG5/DraI and GH) were located on linkage group G11 and they acted in an overdominant genetic fashion to the angle. The allele from OAC Seaforth increased the angle at both loci.
A genomic region near BNG130/EcoRV on linkage group G2 affected hypocotyl diameter, which is a desirable component of upright plant architecture. The QTL explained 11.3% of the phenotypic variation and acted in an overdominant genetic fashion. The allele from OAC Seaforth increased the diameter of hypocotyl at this locus.
Pods per plant, seeds per pod, and 100-seed weight are considered to be major components of seed yield. However, only pods per plant was significantly correlated with seed yield in this study. One QTL each on linkage group G2 (BNG151/EcoRV) accounted for 18.8% of the phenotypic variation for pods per plant. This locus had an overdominance genetic effect with the allele from OAC 95-4 increasing the number of pods per plant. A QTL on linkage group G5 (AGGCTT-510) explained 28.2% of the variation for seeds per pod. Two QTL, one on linkage group G2 (ACGCTA-350) and another on linkage group G10 (ACACTG-530) accounted for 17.8% of the phenotypic variation for 100-seed weight. A QTL near ACTCAT-490 on linkage group G7 explained 21.4% of the phenotypic variation for harvest index, which was also significantly correlated with seed yield. A genomic region on linkage group G9 (AAGCAT-520) accounted for 15.3% of the variation in pod distribution to the upper two thirds of the plant. A single major QTL on linkage group G8 (AAGCAT-418) was detected that explained 43.1% of the phenotypic variation for lodging.
Consistency of QTL across Locations
The QTL analyses in single environments demonstrated that 11 QTL were detected at Elora and 22 QTL were identified at Woodstock (Table 6). Only 25% (5 of 20) of the QTL that were detected in mean location were also detected at Elora and Woodstock. These included one locus each for plant height, total nodes, upper pods, seeds per pod, and lodging. Fourteen QTL that were not detected in the mean location were identified at either Elora or Woodstock. The strengths of the QTL effects for a given trait appeared to be slightly different from one environment to another, but the positions of the were mostly stable.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Information about the correlations among traits is important for defining bean ideotypes for selection. Positive correlations among the components of bean architecture and yield would be desirable. However, negative relationship known as compensation phenomenon among yield-related traits have often been observed (Adams, 1967), and can hinder progress to improve yield. The current study revealed positive and significant correlations between seed yield and plant height (r = 0.23), total nodes (r = 0.18), harvest index (r = 0.24), and pods per plant (r = 0.18), respectively, whereas, a negative correlation was found between seed yield and branch angle. The correlation values obtained in the current study were comparable to those reported previously for beans. Beattie (1998) demonstrated positive and significant correlations between seed yield and harvest index (r = 0.27), plant height (r = 0.46), pods per plant (r = 0.32), and lodging (r = 0.44). Scully et al. (1991) reported a positive correlation between yield and harvest index (r = 0.27). Nienhuis and Singh (1988) reported a significant correlation between yield and plant height (r = 0.67) and between yield and pods per square meter (r = 0.33).
Several linkage maps have been developed for common bean (Adam-Blondon et al., 1994; Ariyarathne et al., 1999; Bai et al., 1997; Jung et al., 1996; Nodari et al., 1993; Vallejos et al., 1992). The large number of maps reflects the fact that no single population will segregate for all of the agronomic traits in common bean. To benefit fully from the existing maps, some connections among the maps are required. Sharing probes for RFLP analyses or primer sequences for PCR-based markers are a means to connect maps. The current map shares 43 RFLP markers with the previous bean linkage map that was developed by Vallejos et al. (1992). However, there were only 30 RFLP markers that could be aligned to the same linkage groups of the previous map. These markers were distributed among 10 (linkage groups G1, G2, G3, G4, G5, G6, G7, G9, G10, and G11) of the 12 linkage groups identified in current study. There was no RFLP marker located on linkage groups G8 and G12 of the current map. Therefore, no connection was made between the latter linkage groups with the Florida map.
Some rearrangements in the marker order within linkage groups were observed in the current study. Rearrangements among markers were also reported by Freye et al. (1998) and Gepts (1998). Differences in the genetic background between the current mapping population and the previous populations may account for some marker rearrangements. Also, several RFLP loci (for example BNG142/HindIII, BNG130/EcoRV, BNG125/EcoRV, BNG205/DraI, and BNG170/BamHI) that were placed on different groups in the current map were generally located at the tips of the corresponding linkage groups of the Florida map. These locations are known to be difficult to map accurately in small populations or with limited number of markers.
Bean is a diploid and contains 11 pairs of chromosomes (2n = 2x = 22). The current map consists of 12 linkage groups and is approximately 1717 cM in size. The size of the map is larger than the estimated genome size (1200 or 1226 cM) of bean (Vallejos et al., 1992; Freyre et al., 1998). Some regions of the current map do not have dense coverage; for example, linkage groups G2, G3, G8, and G10. These areas accounted for most of the discrepancies with the previous map. The presence of an additional linkage group in the current map suggests that a fragment linking two of the existing groups is missing. These discrepancies may be resolved by future studies with larger population sizes and more markers.
Growth habit is an important trait for common bean. The fin gene controls determinate growth habit in bean (Coyne and Schuster, 1974; Koinange at al., 1996; Park et al., 1999b). Gepts et al. (1993) identified a RFLP marker (D1051) linked to the fin gene. The fin gene maps to linkage group D1 of the University of California Davis map, which corresponds to linkage group H of the Florida map (Freyre et al., 1998). In the current map, the GH locus is located on linkage group G11 or linkage group K of the Florida map. Therefore, our results suggest that a second gene for determinate growth habit exists in bean that is located on a different linkage group than fin.
Evidence for a common genetic basis for some correlated traits was obtained in this study. Phenotypic and genotypic correlations indicate that the genes for the traits are either linked, have pleiotropic effects or are influenced similarly by the environment (Aastveit and Aastveit, 1993). Pleiotropy and/or gene linkage may explain the clustering of QTL observed in several regions of the current map. For example, days to flowering and days to maturity, which were highly correlated (r = 0.64), each had a QTL near the GH locus. The OAC 95-4 parent contributed to delayed flowering and maturity in this region. Therefore, this may be an example of two traits influenced by a common gene. In contrast, the branch angle also had a QTL close to the GH locus. However, this trait was negatively correlated to delayed flowering and maturity. OAC Seaforth contributed the allele that increased branch angle. Therefore, these associations may represent an example of gene linkage. Pleiotropic effects of a gene that condition different traits have previously been reported in common bean. For example, Koinange at al. (1996) reported pleiotropic effects of fin on the number of days to flowering and maturity, number of nodes on the main stem and number of pods. An earlier study by Miklas et al. (1996) demonstrated that a common genetic factor was responsible for resistance to CBB in leaves and pods of common bean. Ariyarathne et al. (1999) found that QTL for pod resistance to CBB, leaf resistance to halo blight [caused by Pseudomonas syringae pv. phaseolicola (Burkholder 1926) Young, Dye & Wilkie 1978] and the I gene (that confers resistance to bean common mosaic virus) were linked.
The cumulative strength of the QTL effects, expressed as the total phenotypic variation explained, ranged from 11% for hypocotyl diameter to 43% for lodging. In other studies of common bean, comparable amounts (14–34%) of phenotypic variation for resistance to CBB, foliar resistance to web blight [caused by Thanatephorus cucumeris (A.B. Frank) Donk (anamorph—Rhizoctonia solani Kühn)] and resistance to rust [caused by Uromyces appendiculatus (Pers.: Pers.) Unger] were reported by Jung et al. (1996). As high as 16% of the variability for plant uprightness was captured by a single marker in the same study (Jung et al., 1996). Park et al. (2000) identified five QTL that explained simultaneously 44% of the phenotypic variation for seed weight in a recombinant inbred population derived from a cross between PC-50 and XAN 159. Heritability estimates from previous studies showed that between 0.32 and 0.34 of the phenotypic variation for seed yield of common bean was genetic in origin (Davis and Evans, 1977; Singh et al., 1999). For seed weight, the heritability estimates ranged from 0.24 to 0.65 (Kornegay et al., 1992; Singh et al., 1999). Higher heritability estimates (0.77–0.96) were reported for days to maturity (Davis and Evans, 1977; Singh et al., 1999). In the current study, the amount of phenotypic variation explained by the QTL for seed yield, 100-seed weight, and days to maturity were 27.8, 17.8, and 42.4%, respectively. The discrepancies between previous heritability estimates and the proportion of phenotypic variation explained by the detected QTL for various traits in the current study indicate that additional QTL for these traits exist, perhaps with small and/or nonadditive effects. The large genetic distance (>20 cM) in some regions of the current linkage map and the intermediate size of the population that was used in the current study may have prevented the detection of QTL with small effects.
The majority of the QTL in the current population were identifiable in the mean location, therefore, the QTL that were detected were considered to be representative of the population. Alleles from OAC 95-4 increased trait values at 6 of the 10 QTL. For seed yield, days to flowering, days to maturity, and pods per plant, only OAC 95-4 increased the values of the traits. In contrast, OAC Seaforth contributed alleles for increasing the branch angle and hypocotyl diameter. Both parents contributed alleles for increasing plant height.
The relatively high level of overdominance that was detected at the QTL in the present study may be attributable to a pseudo-overdominance effect (Moll et al., 1964). These QTL may be in regions of the genome containing several loci that contribute to the overall effect. In particular, loci in repulsion linkage (which are at their maximum in F2-derived lines) could account for some of the overdominance effects. The additive effect of such genes in repulsion linkage would partly cancel each other, which would allow the overall dominance effect to seem larger than the underestimated additive effect and result in an overdominance effect at the locus. A large proportion of overdominance gene effects of the QTL for grain yield and yield components, plant height, and flowering in corn (Zea mays L.) was reported by Veldboom and Lee (1996a)( b).
In the present study, only five of the 20 QTL that were detected in the mean location were detected in both locations. In particular, none of the three QTL for seed yield that were detected by the mean location data was detected in both locations. Park et al. (2000) reported that only two out of seven QTL for seed size of common bean were expressed in two environments. It is not known if the restriction of QTL to only one environment is related to gene expression or is indicative of sampling variation (Beavis, 1994). The inconsistency of QTL over environments may be a major limitation in utilizing QTL for marker-assisted selection (MAS) of genotypes for the environments that differ from the environment in which the QTL were detected.
There is accumulating evidence that associations between molecular markers and QTL controlling traits in bean are maintained across different populations. For example, Tar'an et al. (1998) demonstrated that the association between molecular markers and resistance to CBB remained stable across different bean populations. Furthermore, Jung et al. (1999) reported that a QTL for CBB resistance was significantly associated with the trait at least in three bean populations for three X. campestris pv. phaseoli strains. Of six QTL affecting leaf and pod resistance to CBB (Jung et al., 1997), at least three were confirmed in a different bean population (Park et al., 1999a). Therefore, the 20 QTL identified in the present study for seed yield, yield components, and plant architecture with medium to large effects may be valuable for use in MAS. Furthermore, the QTL were placed on a map that is connected to the University of Florida and the core linkage maps for P. vulgaris. This should allow bean geneticists and breeders to benefit fully from the current information.
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ACKNOWLEDGMENTS |
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Received for publication January 26, 2001.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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