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Published online 27 May 2005
Published in Crop Sci 45:1241-1248 (2005)
© 2005 Crop Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
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CROP BREEDING, GENETICS & CYTOLOGY

Localization of a Quantitative Trait Locus Providing Brown Stem Rot Resistance in the Soybean Cultivar Bell

M. E. Patzoldta, C. R. Graub, P. A. Stephensc, N. C. Kurtzweilb, S. R. Carlsona and B. W. Diersa,*

a Dep. of Crop Sciences, Univ. of Illinois, 1101 W. Peabody Dr., Urbana, IL 61801
b Dep. of Plant Pathology, Univ. of Wisconsin, 1630 Linden Dr., Madison, WI 53706
c Pioneer Hi-Bred Int'l, Inc., Princeton, IL 61356

* Corresponding author (bdiers{at}uiuc.edu)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Many soybean [Glycine max (L.) Merr.] genotypes that carry resistance to soybean cyst nematode (Heterodera glycines Ichinohe) (SCN) from plant introduction (PI) 88788 also carry resistance to brown stem rot (BSR) caused by the soilborne fungus Phialophora gregata (Allington & Chamberlain) W. Gams f. sp. sojae Kobayashi, Yamamoto, Negishi, and Ogoshi. The objectives of our research were to map and localize BSR resistance quantitative trait loci (QTL) from Bell, a BSR resistant cultivar with SCN resistance from PI 88788. Initial mapping was done with a population of 93 F4–derived lines developed from a cross between Bell and the SCN and BSR susceptible cultivar, Colfax. Lines were evaluated for BSR resistance in two field environments, a greenhouse, and with genetic markers from linkage group (LG) J. To confirm and further localize a resistance QTL, three near isogenic line (NIL) populations were created using an F4–derived line from the Bell x Colfax population. In the F4 population, markers on LG J were significantly (P < 0.001) associated with BSR resistance in both the field (R2 = 45%) and greenhouse (R2 = 51%). Data from the NIL populations indicates the QTL is near the closely linked markers 21E22.sp1, 21E22.sp2, and 35E22.sp1, which is the same genetic region where Rbs1, Rbs2, and Rbs3, the three named BSR resistance genes, were previously mapped. In addition, the genetic region is tightly linked to a previously identified SCN resistance QTL from PI 88788. These results explain why many SCN resistant cultivars also carry BSR resistance and should assist breeders when selecting for resistance to both diseases.

Abbreviations: BSR, brown stem rot • CAPS, cleaved amplified polymorphic sequence • cM, centimorgans • LG, molecular linkage group • NIL, near isogenic line • PCR, polymerase chain reaction • PI, plant introduction • QTL, quantitative trait locus/loci • RIL, recombinant inbred line • SCN, soybean cyst nematode • SSR, simple sequence repeat


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
BROWN STEM ROT is an important soybean disease in northern U.S. soybean production regions (Gray and Grau, 1999) and is caused by P. gregata f. sp. sojae (Kobayashi et al., 1991). This pathogen causes both browning of stem pith tissue and interveinal chlorosis and necrosis of leaves. In conditions that favor BSR development, yield losses up to 38% have been reported (Gray, 1972). The disease can be controlled in most fields by utilizing cultivars with resistance genes (Bachman et al., 1997). Genetic studies have resulted in the discovery and naming of three BSR resistance genes. These genes are Rbs1, which was identified in the germplasm line L78-4094 (Hanson et al., 1988), Rbs2 from PI 437833 (Hanson et al., 1988), and Rbs3 from PI 437970 (Willmot and Nickell, 1989). Recent studies have resulted in the genetic mapping of all three resistance genes to a region near the simple sequence repeat (SSR) markers Satt431 and Satt244 on LG J (Bachman et al., 2001; Bachman and Nickell, 2000; Cregan et al., 1999; Klos et al., 2000; Lewers et al., 1999).

Several researchers have noted that soybean cultivars with SCN resistance from PI 88788 have high levels of BSR resistance in field and greenhouse environments (Kurtzweil et al., 1999; MacGuidwin et al., 1995; Waller et al., 1992). This is significant because PI 88788 is the most common source of SCN resistance in northern U.S. soybean germplasm (Diers and Arelli, 1999). In a population developed by crossing Bell, a cultivar with SCN resistance derived from PI 88788, and Colfax, an SCN susceptible cultivar, a minor SCN resistance QTL from PI 88788 was identified in the region where the Rbs genes map (Glover et al., 2004; Bachman et al., 2001; Lewers et al., 1999). Bell was developed from a cross between the SCN resistant cultivar Fayette (Bernard et al., 1988) and the SCN susceptible line LN80-10398 (Nickell et al., 1990). Fayette was developed through a single backcross using PI 88788 as a donor parent and ‘Williams’ as a recurrent parent (Bernard et al., 1988). The minor SCN resistance QTL in Bell can be traced to PI 88788 and is consistent with reports of an SCN resistance QTL in the same region on LG J in other SCN resistant sources (Concibido et al., 1997). Linkage between BSR and SCN resistance QTL on LG J may be the reason that cultivars with SCN resistance from PI 88788 also have BSR resistance. Selection for SCN resistance would have resulted in the simultaneous selection of both SCN and BSR resistance genes.

For purposes of marker-assisted selection and gene cloning, it is important to map the precise location of QTL. However, a limitation in QTL mapping is the large confidence intervals associated with QTL locations in mapping populations. Kearsey and Farquhar (1998) found that these confidence intervals typically span more than 30 cM and are difficult to reduce to less than 10 cM. Quantitative trait loci can be further localized using a series of advanced populations that segregate for different areas of a genetic region where a QTL has been mapped (Paterson et al., 1990). In this method, called substitution mapping, QTL are identified in an early generation and advanced generation populations are developed that are alternatively fixed or segregating for molecular markers in the region where the QTL had been mapped. Advanced generation populations are then phenotyped and QTL mapping is done to determine which population is segregating for the QTL. This analysis results in the unambiguous identification of the interval containing the QTL of interest. This experimental design results in more precise QTL placement to specific intervals without increasing mapping population sizes to an unmanageable numbers of individuals. Paterson et al. (1990) used this approach to narrowly define intervals of 3 cM, allowing them to separate effects from tightly linked QTL that controlled tomato fruit size, shape, and content.

Further experiments using this method have since been described in the literature. Monforte and Tanksley (2000) fine mapped QTL controlling agronomic characteristics in tomato (Lycopersicon spp.) and determined heterosis components of yield. This technique was used by Graham et al. (1997) to identify yield QTL in maize (Zea mays L.). Tunistra et al. (1998) employed substitution mapping in NILs to identify drought resistance QTL in sorghum [Sorghum bicolor (L.) Moench]. The objectives of this research were to map BSR resistance QTL from Bell and then further localize a resistance QTL through substitution mapping methods.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
F4 Population
A population of 93 F4–derived lines was developed through single-seed descent from a cross between the soybean cultivars Bell and Colfax. Bell (Nickell et al., 1990) has resistance to SCN populations PA3 (HG type 7, race 3) and PA14 (HG type 1.3.5.6.7, race 14) from PI 88788 and has resistance to BSR (Hughes et al., 2001; Kurtzweil et al., 1999; MacGuidwin et al., 1995). Colfax has no known relationship to PI 88788 and is susceptible to both BSR and SCN (Graef et al., 1994).

Lines in the F4:6 generation were field tested for BSR resistance in BSR disease nurseries in Princeton and LaSalle, IL, and Madison, WI, during the summer of 2000. The LaSalle location was planted on April 26, the Princeton location on April 28, and the Madison location on May 2. The field tests were arranged in a randomized complete block design and the plants were naturally infected with P. gregata f. sp. sojae from inoculum present in the field. In Wisconsin, the lines were planted in three replicates of single row plots that were 6 m long with 0.76-m row spacing and a seeding rate of 27 seeds m–1. The lines in Princeton and LaSalle, IL, were planted in two replicates of two row plots that were 1.5 m long with a row spacing of 0.76 m and a seeding rate of 60 seeds m–1. No disease data were taken at the Princeton location because the plots were damaged by hail during the growing season. At the LaSalle and Madison field locations, the weather patterns for 2000 were similar to the 20-yr average. Precipitation was normal at the LaSalle location and precipitation at Madison was higher than normal in the spring but was close to the 20-yr average for the remainder of the summer.

Foliar ratings in Madison were taken at the R6 and R7/R8 growth stages (Fehr et al., 1971) by visually estimating the percent leaf chlorosis and necrosis of leaves. This percentage was then assigned a value from the Horsfall–Barratt (HB) Scale (Horsfall and Barratt, 1945; Mengistu and Grau, 1987; Table 1). In September 2000, stem ratings were taken at the Madison and LaSalle locations by randomly selecting five plants per plot after the R8 growth stage. Stems from the selected plants were split longitudinally and the percentage of each plant stem showing browning was visually estimated and assigned a value from the HB scale. These HB scale values were later converted to the Elanco (division of Eli Lilly & Co., Indianapolis, IN) weighted percentage for the purpose of data analysis (Table 1).


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  		Table 1. Horsfall–Barratt disease scale for normalization of disease data and conversion of those scores to weighted percentages for data analysis.

 
The F4:6 lines were evaluated in the greenhouse in Urbana, IL, during the winter of 2000–2001 using the root dip technique first described by Sebastian et al. (1985) with modifications specified in Patzoldt et al. (2003). The plants were inoculated with P. gregata f. sp. sojae isolate Oh2, which is classified as a pathotype I (A) isolate (Gray, 1971; Hughes et al., 2002). This isolate was used because of its ability to consistently produce stem symptoms in the greenhouse and because it remains relatively stable in continuous culture. Isolate Oh2 was obtained from soybean grown in Ohio and was provided by Dr. L.E. Gray.

Plants were inoculated with liquid culture broth media, modified from Gray (1971). The media was prepared using steamed, strained, and autoclaved seed from the BSR susceptible cultivar Century 84 and the stationary liquid cultures were kept in the dark and incubated at 24°C. Inoculum was prepared by blending the liquid cultures and adjusting fragment concentration to 1.2 x 106 propagules mL–1. The inoculation procedure was adapted from Sebastian et al. (1985) with modifications outlined in Patzoldt et al. (2003). In short, 10 seeds of each entry were planted in sand and five uniform, healthy plants were selected after 9 to 14 d. Roots were gently washed, blotted dry, and dipped into 50 mL of inoculum before transplanting into 150 mm sterilized clay pots containing 1:1 sand/topsoil mixture. The inoculum was poured over the roots before being covered with soil. Plants were grown under a 14-h photoperiod and watered as needed.

Pots were arranged in a completely randomized design that included one replicate (one pot of five plants) for each line in the population and five replicates of each check genotype. The checks included the three resistance sources L78-4094 (Rbs1), PI 437833 (Rbs2), PI 437970 (Rbs3); the cultivars Fayette, Jack, and Bell (all containing SCN resistance from PI 88788); and the susceptible genotypes ‘Century 84’, Williams, Colfax, and PI 88788. Four to 6 wk after inoculation, when most of the plants had reached the R1 to R3 growth stage (Fehr et al., 1971), the five plants in each pot were rated for BSR stem symptoms. Stem ratings were taken by splitting each stem longitudinally and identifying the percentage of browned nodes. These percentages were then assigned values from the HB Scale for later conversion to Elanco weighted percentages to normalize the data for analysis.

Simple sequence repeat markers from the USDA LG J consensus map (Perry Cregan, personal communication, 2003) and the cleaved amplified polymorphic sequence (CAPS) markers 21E22.sp1, 21E22.sp2, and 35E22.sp1 (Klos et al., 2000), also from LG J, were used to genotype the population. DNA was extracted from expanding trifoliate leaves taken from 10 plants in each F4:6 line. DNA extractions were done according to a protocol modified from Keim and Shoemaker (1988), which was scaled to use only one-third the volume of the solutions as described in the original protocol. Simple sequence repeat markers were amplified through polymerase chain reaction (PCR) according to Cregan et al. (1999). Amplified samples were run on 6% nondenaturing acrylamide (Wang et al., 2003) or 3% Metaphor agarose gels. Ethidium bromide was used to stain the gels and bands were visualized under UV light. Some SSR markers were analyzed on an ABI Prism 377 DNA Sequencer (ABI-PEC, Foster City, CA). This system uses 0.2 mm thick, 4.8% acrylamide gels with a 36-cm well-to-read distance. These sequencer gel images were viewed with GeneScan v3.1 Analysis Software (Applied Biosystems, Foster City, CA) and manually scored.

Polymerase chain reactions for the CAPS markers contained 70 ng of genomic DNA, 0.4 µM forward and reverse primer, 1x PCR buffer (10 mM Tris HCl at pH 8.0, 50 mM KCl, and 0.001% Piorex Type A gel), 1 mg mL–1 BSA (bovine serum albumin), 3 mM MgCl2, 0.167 mM of each nucleotide, and 1 unit of Taq polymerase in a total volume of 20 µL. DNA amplification was accomplished using PCR cycling conditions described by Klos et al. (2000) and an annealing temperature of 62°C. Ten units of restriction enzyme were added to each reaction post amplification and incubated for 1.5 h at 37°C. The marker 21E22.sp1 was digested with MspI, and the markers 21E22.sp2 and 35E22.sp1 were digested with HhaI. Samples were stored at 4°C until run on 1% agarose gels in 0.5 x TBE (modified from Klos et al., 2000). Ethidium bromide was used to stain the gels and bands were visualized under UV light.

Elanco weighted percentages from the field and greenhouse tests were analyzed with the SAS statistical package (SAS, 2000). Pearson product-moment correlations were calculated with PROC CORR of SAS using the means of lines at environments to calculate the phenotypic correlations among environments and rating methods. Estimates of variance components and broad-sense heritabilities for resistance were calculated from mean squares (Fehr, 1987) obtained from PROC GLM of SAS. Molecular marker data were analyzed with JoinMap V3.0 (VanOoijen and Voorrips, 2001) to determine genetic distances among markers. Genotypic and phenotypic data were combined and analyzed by single-factor analysis of variance in PROC GLM of SAS and by composite interval mapping with MapQTL V4.0 (VanOoijen et al., 2002) to map BSR resistance QTL. A threshold of {alpha} = 0.01 was used to declare significance for the single-factor analysis and a likelihood of odds (LOD) threshold of 3.0 was used to declare significance for the interval mapping.

Near Isogenic Line Populations
The location of the BSR resistance QTL was further characterized by testing three populations of NILs that were developed from selected plants from one line in the F4 population. The F4–derived line was selected because it was heterogeneous on the basis of genetic markers for the region on LG J where the BSR resistance QTL mapped. The selected line was inbred to the F7 generation in bulk and F7 plants that were heterozygous for the region were selected. F8 progeny from these selected plants were grown in the field and tested with the markers Satt431, Satt244, Satt547, and 21E22.sp2 from the region where the QTL was believed to map to identify plants with recombination in this region. Three plants heterozygous for one or more markers and homozygous for the remaining markers were selected and individually threshed. The three NIL populations can each be traced to one of these selected plants. For Population 1, F9 progeny from a selected heterozygous F8 plant were grown in the greenhouse and were individually harvested to develop F9–derived lines. Thirty-eight lines from Population 1 were increased in the field in 2002 and F9:11 seed was used for BSR resistance testing. Population 1 segregates for both Satt547 and Satt431 and is fixed for Satt244 and all three CAPS markers (Fig. 1). In Populations 2 and 3, insufficient seed was produced on selected F8 plants for population development. Therefore, seed was increased by growing F9 plants in the greenhouse where a single plant was selected to form each population. These two selected individuals were heterozygous and fixed for the same regions on LG J as the selected F8 plants. Populations of F10 plants developed from the selected F9 plants were grown in the field in 2002 and individually threshed. These populations were at the F10:11 generation for this study. Population 2 had 80 lines, was fixed for Satt547 and Satt431, and was segregating for Satt244 and the CAPS markers. Population 3 had 67 lines, was segregating for the CAPS markers, and was fixed for Satt244, Satt431, and Satt547 (Fig. 1).



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  		Fig. 1. Diagrammatic representation of the regions segregating on linkage group J (LG J) for the near isogenic line (NIL) populations developed by crossing the cultivars Bell and Colfax. Genetic distances between markers in centimorgans (cM) are listed between the markers. Segregation patterns for each population are denoted by the shading of bars.

 
These NIL populations were screened for BSR response in the greenhouse according to the inoculation protocol listed above (Patzoldt et al., 2003). The greenhouse experiments were completed at two locations, the University of Illinois, Urbana, and at the University of Wisconsin, Madison. At Urbana, Populations 1 and 3 were not tested in replicates and Population 2 was tested in two replicates, with each replication separated by time. At Wisconsin, two replicates of both Populations 2 and 3 were tested at the same time in a completely randomized block design. The inoculation procedure in Wisconsin was slightly modified from the procedure used in Illinois. The modifications in Wisconsin included trimming roots to one-third of their total length, dipping the roots for 20 min in the inoculum before being transplanted, and fertilizing plants with time release fertilizer beads at the time of transplantation. An experimental unit consisted of one pot with three plants in Wisconsin. The slight differences in inoculation procedures at the two locations did not affect P. gregata f. sp. sojae infection or subsequent disease development, as the disease results were consistent across locations. DNA was collected from five to 10 plants from each line in each population and the lines were genotyped with segregating markers on LG J according to the protocols listed above. Data from the NIL experiments were analyzed by single factor analysis of variance using SAS PROC GLM as described previously. For nonreplicated experiments, QTL mapping was done using the means across plants for each experimental unit. For replicated experiments, means of lines across replicates were used to map QTL.


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
F4 Population
There was significant (P < 0.001) genetic variability for stem symptoms among the lines at Madison and LaSalle and across environments as well as a significant genotype by environment interaction (data not shown). The field locations had a significant phenotypic correlation of 0.73 (P < 0.001). The phenotypic correlations between field and greenhouse tests at Urbana were significant with a correlation value of 0.68 (P < 0.001) between the greenhouse and LaSalle, and 0.50 (P < 0.001) between the greenhouse and Madison. The phenotypic correlation of 0.83 (P < 0.001) between foliar and stem BSR ratings at Madison was significant. No foliar ratings were taken in LaSalle or in the greenhouse due to the lack of scorable foliar symptom development. The broad sense heritability estimate for BSR resistance based on stem symptoms across field environments was 0.75 which was similar to other reported heritabilities for BSR resistance.

Molecular markers Satt431, Satt547, Satt244, 21E22.sp1, 21E22.sp2, K375, and 35E22.sp1 were significantly associated (P < 0.001) with BSR resistance based on single-factor ANOVA, suggesting that a major BSR resistance QTL is segregating on LG J in this population (Table 2). Lines homozygous for the marker alleles from Bell were significantly more resistant than lines homozygous for the Colfax alleles (Fig. 2 and Table 2). The marker with the greatest R2 value in the greenhouse test was 21E22.sp2 (Table 2), which explained 51% of the variability for resistance. The marker with the greatest R2 value in the field tests was Satt547, which explained 53% of the variability for resistance in Madison and 73% of the variability for resistance in LaSalle. The composite interval mapping results from MapQTL were mostly consistent with those from single-factor ANOVA. Markers that were significantly associated with BSR resistance in the single-factor ANOVA also have LOD peak values greater than 3, indicating that they are associated with disease response. The LOD peak for resistance was closest to 21E22.sp2 for both the field and greenhouse tests (Fig. 3). The LOD peaks for the greenhouse and field results map sufficiently close to suggest that they map the same QTL. The marker closest to the minor SCN resistance QTL previously mapped from Bell was Satt431 (Glover et al., 2004). The linkage between the BSR and SCN resistance QTL supports our hypothesis that BSR resistance may have been serendipitously selected during selection for SCN resistance in this germplasm.


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  		Table 2. Means of genotypic classes, R2 values, and probability values for molecular markers on linkage group J that are most significantly associated with brown stem rot (BSR) resistance in the greenhouse (21E22.sp2) or in the field (Satt547). These values are based on single-factor ANOVA in the F4 population developed from crossing the cultivars Bell and Colfax. The resistance data are from the field in Madison, WI, and LaSalle, IL, and the greenhouse in Urbana, IL.

 


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  		Fig. 2. Distribution of mean brown stem rot (BSR) disease reactions (A) across two field locations and (B) in the greenhouse for a population of F4–derived soybean lines developed by crossing the cultivars Bell and Colfax. Genotypes of lines for Satt547 are designated by the shading of bars. BSR symptoms were a visual estimation of the proportion of stem pith tissue that was browned. The BSR disease scores for the parents are designated by the placement of their names on the figure.

 


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  		Fig. 3. Plots of likelihood of odds (LOD) scores for mapping quantitative trait loci (QTL) on linkage group J for brown stem rot (BSR) resistance in a population of F4–derived lines developed from a cross between the soybean cultivars Bell and Colfax. The phenotypic data are based on the average stem browning in a greenhouse test (Urbana, IL) and across field tests in LaSalle, IL, and Madison, WI. Distances in centimorgans (cM) are indicated on the x axis.

 
Near Isogenic Line Populations
The NIL populations were grown in greenhouse experiments in both Urbana and Madison. To determine if location had a significant impact on disease development, the resistance data from the check genotypes were analyzed. Neither location nor the genotype by location interaction were significant ({alpha} = 0.05) for any population, indicating that the genotypic response is consistent between environments. For Population 1, neither foliar nor stem browning symptoms in Urbana were significantly ({alpha} = 0.05) associated with Sat_224, Satt431, or Satt547 (Fig. 1). These results suggest that the BSR resistance QTL is not in the region between Sat_224 and Satt547.

Four markers (Satt244, 21E22.sp2, 21E22.sp1, and 35E22.sp1) (Fig. 1, Table 3) segregating in Population 2 were significantly (P < 0.05) associated with stem BSR symptoms at both the Illinois and Wisconsin locations, across locations and with foliar symptoms at Illinois. Of the four markers, 35E22.sp1 had the greatest association with resistance. At the Madison location, no foliar data were collected due to complications with other diseases such as powdery mildew caused by Microsphaera diffusa Cooke & Peck.


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  		Table 3. Means of genotypic classes, R2 values, and probability values for molecular markers most significantly associated with brown stem rot (BSR) resistance based on single-factor ANOVA of greenhouse tests for two populations of near isogenic lines.

 
Population 3, with 21E22.sp1, 21E22.sp2, and 35E22.sp1 segregating (Fig. 1) and Satt244, Satt431, and Satt547 fixed, was evaluated in both locations in an attempt to better define the interval containing the QTL. At both locations, the three CAPS markers were significantly associated with stem symptoms and with foliar symptoms in Urbana (Table 3). Consistent with population 2, 35E22.sp1 had the greatest association with resistance. Foliar symptoms were not rated at Madison because of complications from other diseases.


   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The BSR resistance QTL identified in our study maps to the same general region as Rbs1, Rbs2, and Rbs3. Lewers et al. (1999) mapped a BSR resistance QTL close to the restriction fragment length polymorphism (RFLP) marker K375 on LG J in a RIL population. This QTL had a large effect, explaining 40% of variability for foliar symptoms and 45% of variability for stem symptoms. The resistant parent of their population was ‘BSR 101’ which is believed to carry Rbs3 and a second gene with a minor effect (Eathington et al., 1995). In a greenhouse study, Bachman et al. (2001) mapped Rbs1 and Rbs2 to LG J in two different populations. They found that the closest marker to Rbs1 was Satt431, which explained 74% of the variability for resistance, and the closest marker to Rbs2 was Satt244, which explained 67% of the variability for resistance. Our across field location heritability estimates of 0.75 for stem symptoms was similar to other reported estimates. Lewers et al. (1999) reported a heritability of 0.66 for foliar BSR symptoms and 0.73 for BSR stem symptoms in a growth chamber environment using recombinant inbred lines (RIL). On the basis of greenhouse evaluations, Bachman et al. (2001) reported a heritability of 0.88 for a population segregating for Rbs1 and a heritability of 0.86 for a population segregating for Rbs2.

The testing of the three NIL populations allowed us to confirm the QTL mapped in the F4 population and to more precisely map the location of this QTL. In Population 1, which segregates for Satt431 and Satt547, but not for Satt244 or the three CAPS markers, no significant association between markers and resistance was found. This indicated that the resistance QTL was not in the region between Satt431 and Satt547. This finding is also supported by Populations 2 and 3. In Population 2, significant association was found between resistance and segregation for Satt244 and the three CAPS markers, indicating the QTL is in the region where these markers map. The location of the QTL was better defined in Population 3, which segregated for the three CAPS markers but not for Satt244. A significant marker QTL association was found in this population showing that the QTL is in the region near the three CAPS markers.

We were not able to conclusively show that the QTL maps to a small interval because no polymorphic PCR-based markers were available to select for recombinants between the CAPS markers and Satt529. In the F4 population, the interval between Satt529 and the nearest CAPS marker was 11 cM. It is possible that the QTL could be located anywhere in the interval between Satt529 and Satt244. However, the QTL is most likely near the CAPS markers because the LOD peak localized there in the F4 population and these markers were the most significant in the NIL populations. The NIL populations did allow us to exclude the possibility of the QTL being located between Satt244 and Sat_224, which is part of the region where Rbs1, Rbs2, and Rbs3 were mapped in other studies.

A fingerprint analysis with SSR markers indicates that BSR resistance in our Bell by Colfax population can be traced to PI 88788. These markers show that Bell has an approximately 55 cM region originating from PI 88788 on LG J that includes the area where BSR resistance has been mapped in this study. While PI 88788 is resistant to SCN and is the source of genetic resistance to BSR in this population, it is paradoxically highly susceptible to BSR in our greenhouse test (Table 4). In fact, PI 88788 is significantly (P < 0.001) more susceptible than the susceptible check, Century 84, in greenhouse studies. Although PI 88788 is the source of the BSR resistance gene, its susceptibility could be conditioned by a number of factors. These include interference from other loci that negatively impact BSR resistance and a lack of one or more additional genes that the LG J resistance gene must epistatically interact with to give resistance. Our results do not dispute and may support a genetic model for BSR resistance proposed by Bachman and Nickell (2000). This model states that BSR resistance is the result of an epistatic interaction between pairs of loci.


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  		Table 4. Stem browning of soybean genotypes inoculated with Phialophora gregata f. sp. sojae in field locations near Madison, WI, and LaSalle, IL, and in a greenhouse root dip inoculation test in Urbana, IL.

 
Substitution mapping resulted in the localization of a BSR resistance QTL from Bell to a region near the CAPS markers on LG J. The mapping of the BSR resistance QTL to this region where a SCN resistance QTL was previously mapped explains why many cultivars with SCN resistance from PI 88778 also are BSR resistant. These findings should allow breeders to design marker assisted selection strategies for resistance to both diseases through selection for this region. An unanswered question is whether Bell has one of the previously described Rbs resistance genes, or a new gene. This could be addressed by substitution mapping of the named Rbs genes to determine if resistance genes from these sources can be placed unambiguously into different intervals. A more thorough understanding of the allelic variation for BSR resistance on LG J will help breeders better deploy these resistance genes.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Research supported in part by the United Soybean Board and the Illinois Soybean Program Operating Board.

Received for publication November 25, 2003.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 


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P. S. Guzman, B. W. Diers, D. J. Neece, S. K. St. Martin, A. R. LeRoy, C. R. Grau, T. J. Hughes, and R. L. Nelson
QTL Associated with Yield in Three Backcross-Derived Populations of Soybean
Crop Sci., January 22, 2007; 47(1): 111 - 122.
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