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a Genetics Department, North Carolina State University, Gardner Hall, Raleigh, NC 27695
b National Corn and Sorghum Research Center, Kasetsart Univ., Klangdong, Pakchong, Nakhonratchasima 30320, Thailand
c USDA-ARS Plant Genetics Research Unit and Department of Agronomy, University of Missouri-Columbia, Curtis Hall, Columbia, MO 65211
* Corresponding author (DarrahL{at}missouri.edu)
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Abbreviations: CV, coefficient of variation • ECB, European corn borer • LOD, log-odds ratio • MoSCSSS-High1, MoSCSSS(H24-High Rind Penetrometer)C10S1 • MoSCSSS-High2, MoSCSSS(H24-High Rind Penetrometer)C10S2-1 • MoSCSSS-High3, MoSCSSS(H24-High Rind Penetrometer)C10S2-2 • MoSCSSS-Low, MoSCSSS(H25-Low Rind Penetrometer)C11 • MoSQB-Low, MoSQB(S10)C6 • PCR, polymerase chain reaction • QTL, quantitative trait locus • R2, percent of phenotypic variation explained • RFLP, restriction fragment length polymorphism • RCBD, randomized complete block • RPR, rind penetrometer resistance • SCS, stalk crushing strength • SSR, simple sequence repeat
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Stalk lodging counts are unreliable as a measure of stalk lodging resistance because the expression of stalk lodging is affected by diseases, insects, and wind. Data summarized from the national white food corn performance trials for the years 1986 to 2000 indicate that visual counts of percentages of stalk lodging have coefficients of variation (CVs) in the range of 22 to 150% with an average of 82% (L.L. Darrah, unpublished data). Several methods have been devised to measure stalk strength to improve stalk lodging resistance. Zuber and Grogan (1961) developed a stalk crushing strength (SCS) technique whereby a 5.1-cm dried section of stalk from the second or third internode above the ground was crushed vertically with a hydraulic press. The force required to "pop" the rind was significantly negatively correlated with stalk lodging in multiple studies (Zuber and Grogan, 1961; Thompson, 1963). The CVs for SCS ranged from 8 to 35%. However, because of the destructive nature of sampling by SCS, an alternative method of evaluating stalk strength was desired. More emphasis was placed on rind strength because studies indicated that the rind contributed 50 to 80% of the stalk strength (Zuber et al., 1980). Sibale et al. (1992) described use of a modified electronic rind penetrometer to measure stalk strength and found a highly significant correlation between SCS and rind penetrometer resistance (RPR) with a CV of 10.5% for RPR. More importantly, RPR was significantly and negatively correlated with stalk lodging (Chesang-Chumo, 1993; McDevitt, 1999; Spiess, 1995; Jampatong, 1999).
Little is known about the genetic nature of stalk lodging resistance and RPR. Previous studies have investigated the genetics of stalk strength at a broad level and found that multiple genic regions were involved in RPR (Heredia-Diaz et al., 1996; Lee et al., 1996). These studies, however, are too limited to identify the biochemical and physiological pathways underlying in stalk strength or to provide a basis for marker-assisted selection. The present study is first attempt to explore known candidate genes that might be associated with RPR. When investigating a complex trait such as RPR, where many physiological and anatomical factors are involved, QTL analysis is the most appropriate initial approach to study the underlying genetic mechanisms. The objectives of this study were to (i) locate QTL for rind penetrometer resistance in four maize populations, (ii) estimate their effects, and (iii) identify candidate genes for these QTL.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Populations 1 and 2 were designed specifically to map stalk rind strength QTL since both parents were selected for high and low stalk strength phenotypes. Population 4 was initially designed to map QTL for resistance to both first- and second-generation ECB, with Mo47 as the source of resistance. We decided to evaluate RPR in this population, where neither parent was selected for stalk strength per se. By not maximizing the difference between parents regarding stalk strength, we might be able to identify QTL that were not selected for in the other populations. Because both parents were inbred lines, this type of mapping population and the results it would provide would be more representative of experiments conducted by industry. Population 3 was designed to bridge the gap between Populations 1 and 2 and Population 4. While not the focus of this study, Population 3 would also lays the groundwork for subsequent studies investigating the relationship between stalk strength and stalk tunneling by second-generation ECB.
Phenotypic Data Collection
Locations used for evaluation trials in this study include Hinkson Bottom at Columbia, MO, on Freeburg silt loam; Agronomy Research Center near Columbia, MO, on Mexico silt loam; a site near Grand Pass, MO, in Saline County on Haynie silt loam; a site near Tipton, MO, in Cooper County on Clafork and Crestmeade silt loam; and a site managed by the Illinois Crop Improvement Association near Juana Diaz, Puerto Rico, on San Antón sandy clay loam. All experiments in Missouri were planted as 6.7-m-long single-row plots spaced 0.90 m apart for a final planting density of 53 800 plants ha-1. Standard cultural practices were used in fertilization, and weed and pest control for the Missouri experiments. All experiments in Puerto Rico were planted on a raised bed in paired rows with 50.8 cm between paired rows and 1.32 m between pairs of rows for a final planting density of 44 850 plants ha-1. Plots in Puerto Rico were irrigated by means of drip tape positioned between the 50.8-cm rows.
Population 1 was divided into three sets, each containing entries for 94 families and two entries for each of the parents and F1. Each set was planted as a 10x10 triple lattice. Locations included two replications (one replication of three planted was not usable) at the Agronomy Research Center in 1999, and three replications at the Agronomy Research Center and Tipton sites in 2000.
Populations 2 and 3 were divided into three sets apiece, each containing entries for 97 families and one entry of the parents and F1. Again, each set was planted as a 10x10 triple lattice. Locations for both populations included three replications at two locations in Puerto Rico in 1999, and three replications at the Agronomy Research Center and Tipton sites in 2000.
Population 4 was planted as a 16x16 triple lattice containing 244 families, two entries for each of the parents and F1, and six check entries (Jampatong et al., 2002). In 1996, three replications were planted at Grand Pass, and in 1997, three locations were planted with three replications at Grand Pass, Hinkson Bottom, and the Agronomy Research Center.
Rind penetrometer resistance was determined for 10 competitive plants plot-1 with an electronic rind penetrometer. The rind penetrometer is a modified Accuforce Cadet digital force gage (Ametek, Largo, FL), 22.7-kg capacity, powered by a 9-V alkaline battery (Sibale et al., 1992). About 2 wk after flowering, plants were probed in the middle of the flat side of the internode below the primary ear node. To increase the precision of our RPR measurements, only a single, skilled rind penetrometer operator evaluated a complete set. This allowed for removal of operator effects during subsequent data analysis.
Molecular Marker Genotype Analysis
Leaf tissue was collected from 20 F2:3 plants and bulked to reconstitute the F2 genotype for Populations 1 and 4, or from tissue of F2 plants for Populations 2 and 3. We extracted DNA using either a modified method based on Saghai-Maroof et al. (1984), or by a microextraction method developed at the University of Missouri by D. Davis and G. Xu. Populations 1, 2, and 3 were genotyped with SSR markers while Population 4 was genotyped by RFLP markers.
For genotyping using SSRs, the PCR reaction consisted of 50 ng each SSR primer, 50 ng genomic DNA, and either (i) 1x PCR Buffer, 2.5 mM MgCl2, 0.4 mM of each dNTP, and 0.3 units of Platinum Taq polymerase (Invitrogen, Carlsbad, CA) in a final volume of 15 µL, or (ii) 9.9 mL of Jumpstart Ready Mix REDTaq PCR reaction mix (Sigma-Aldrich, St. Louis, MO) in a 20-µL reaction. The thermocycling program was as follows: 95°C for 1 min, 65°C for 1 min, and 72°C (annealing temperature) for 1.5 min for the first cycle, and then a one-degree decrement for the annealing temperature, each repeated once, until the annealing temperature was 55°C. The regime thereafter was 95°C for 1 min, 55°C for 1 min, 72°C for 1.5 min, repeated for a total of 30 cycles. Amplification products were resolved by electrophoresis on 4 or 5% (w/v) super fine resolution-agarose gels (Amresco, Solon, OH). Populations 1, 2, and 3 were genotyped with 89, 77, and 86 SSR markers, respectively. For Population 4, genomic DNA was digested with one of six restriction enzymes and transferred to nylon membranes as previously described (Jampatong et al., 2002). Ninety-seven radioactively labeled probes were hybridized to these membranes to score RFLPs.
Data Analysis
Year–location combinations were treated as independent environments. Each set of a population within an environment was analyzed as both a lattice and randomized complete block (RCBD) by the ABIYO FORTRAN program (pers. comm., L. L. Darrah). Adjusted means (over replications) for each set at an environment were used where the lattice analysis was more efficient (relative efficiency >1.04) than the RCBD, while unadjusted means were used when the RCBD was equally efficient. For Populations 1, 2, and 3, means within a set were adjusted to the location mean to remove "set" effects (pers. comm., G.F. Krause, Missouri Agricultural Experiment Station statistician). A combined analysis of variance using means from each environment was conducted to estimate genotype variance, genotype x environment variance, phenotype variance, and broad-sense heritability on an entry-mean basis. Entry means across environments were used to compute phenotypic correlation coefficients by means of SAS PROC CORR (SAS Institute, Inc., 1998). Standard errors were used to determine whether parental RPR values were significantly different and whether transgressive segregants were present in the population.
MAPMAKER/EXP version 3.0b was used to construct linkage maps (Lander et al., 1987; Lincoln et al., 1992). QTL Cartographer version 1.14d (Basten et al., 1994; Basten et al., 2000) was employed for QTL analysis of family means across environments. Cofactors were identified using forward and backward stepwise regression with P (Fin) = P (Fout) = 0.01, and composite interval mapping (Zeng, 1994) was used to estimate QTL locations and their effects. For each trait-population combination, experiment-wise significance thresholds at P = 0.05 were determined by analyzing 1000 permutations of the data according to Churchill and Doerge (1994). When multiple peaks were found within a single-marker interval, the location with the highest log-odds ratio (LOD) score was defined as the QTL peak. When multiple peaks were found in adjacent marker intervals, either (i) the location with the largest LOD score was defined as the QTL peak when one peak had a very high LOD score and the other had a relatively low LOD score, (ii) an intermediate location was defined as the QTL peak when the multiple peaks were equal in LOD score and distance from the separating marker, or (iii) each peak was defined as a QTL peak when there was at least a one-half-LOD difference from the peak to the separating marker. A one-LOD drop from the peak position was used as a confidence interval for QTL location. Quantitative trait loci were placed on a composite QTL map on the basis of the markers of Population 3 by means of markers common among the populations. Overlapping confidence intervals were used to determine whether QTL were in common among populations. Estimates of dominance effects as calculated by Zmapqtl in QTL Cartographer (Basten et al., 1994; Basten et al., 2000) were multiplied by two because F2:3 families were used in the analysis rather than F2 individuals.
The statistical program EPISTACY was used to test for the presence of epistatic interactions between marker pairs at P < 0.001 (Holland, 1998). To build multilocus models, markers nearest to single-effect QTL, interactions, and the markers involved in the interactions were subjected to stepwise regression at P < 0.05 by SAS PROC REG (SAS Institute, Inc., 1998). Markers were added to the model in order of increasing P-value (forward regression in), and were removed if their significance while in the model exceeded 0.05 (backward regression out).
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RESULTS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Eight single-effect QTL for RPR were detected in Population 3. No significant epistatic interactions were detected, and a multilocus model including seven single-effect QTL accounted for 48.4% of the phenotypic variation. Six of the eight alleles that increased RPR originated from MoSCSSS-High3, while two alleles originated from Mo47.
Nine single-effect QTL and five epistatic interactions were detected in Population 4. Only two of the 10 loci involved in the interactions were detected as single-effect QTL. A multilocus model including six single-effect QTL and two interactions explained 58.7% of the phenotypic variation. Five of the nine alleles increasing RPR originated from B73 and four originated from Mo47. The QTL near umc38 on chromosome 6L had a large partial R2 (20.2%) relative to the other QTL detected in this study.
A composite QTL map based on the linkage map from Population 3 was used to display the relative location of the QTL among the four populations (Fig. 2). Refer to Flint-Garcia et al. (2003)(this issue) and Jampatong et al. (2002) for individual linkage maps. Only one region on chromosome 3 contained overlapping confidence intervals from all four populations. Two regions had overlapping QTL from three populations and five regions had overlapping QTL from two populations.
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DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Little transgressive segregation was seen in the first three populations; however, a large proportion of the families (75.0%) fell outside the range of the parental lines in Population 4 (Fig. 1). This could be explained by the fact that Population 4 was the only population in this study derived from two inbred lines unselected for RPR. The parental lines, B73 and Mo47, may contain complementary QTL for RPR where, through recombination, the wide array of genotypes produced by the F1 result in transgressive segregants in the following generation.
Quantitative traits have been defined as characters that display continuous distribution of phenotypes. The variability is usually associated with the segregation of multiple minor genes, which have small individual effects and are influenced by the environment. The phenotypic distributions shown in Fig. 1 demonstrate the continuous variation of the RPR phenotype in all four populations. The QTL analysis results summarized in Table 4 reveal that RPR is governed by numerous QTL with small-to-moderate effects. Eight, 10, eight, and nine QTL were found in the four populations. Of these QTL, only the QTL on chromosome 6L in Population 4 had a partial R2 above 15%, indicating a lack of a major QTL for RPR. In addition to a large number of single-effect QTL, significant epistatic interactions were detected in three of the four populations. Only three of the 22 loci involved in these interactions were detected as single-effect QTL, again indicating the complexity of RPR. The results of the current study are consistent with the results of previous studies investigating the genetic complexity of RPR. Heredia-Diaz et al. (1996) used allelic frequency changes at 16 RFLP loci over seven cycles of selection for RPR and found 12 loci correlated with RPR, three of which accounted for 99% of the total variation. Lee et al. (1996) found that hyperploidy or hypoploidy for 13 of 18 chromosome arms was associated with significant differences in RPR.
In Population 1, the majority of the alleles that increase RPR originated from MoSQB-low, the low SCS parent. This result was unexpected because the RPR phenotype of the low SCS parent was low. We believe that the correlation between RPR and EH may have contributed to this particular result (Flint-Garcia et al., 2003, this issue). In Population 2, all alleles responsible for increasing RPR originated from the high RPR parent. This result is consistent with bidirectional selection for RPR successfully separating MoSCSSS-C0 into two distinct subpopulations (Alsirt, 1993). Therefore, bidirectional selection was able to partition the high and low RPR alleles into the separate subpopulations. In Population 3, the majority of the high RPR alleles originated from MoSCSSS-High3, with Mo47 contributing only two of the eight favorable alleles. Inbred line Mo47 was developed as a source of resistance to both the first- and second-generation of ECB, and was derived from 50% exotic germplasm (Barry et al., 1995). Mo47 may contain alleles not normally found in Corn Belt germplasm that increase RPR, but, as expected, the majority of the alleles increasing RPR came from the MoSCSSS-High3 parent because of its selection for high RPR. In Population 4, five of the nine alleles increasing RPR originated from B73 and four originated from Mo47. This nearly equal contribution of high RPR alleles supports the concept presented earlier that B73 and Mo47 contained complementary sets of alleles at multiple QTL leading to the transgressive segregants for both high and low RPR among the families.
In terms of their QTL profiles, Populations 2 and 3 were most similar, having six QTL in common. Population 1 had only four and two QTL in common with Populations 2 and 3 for two possible reasons. The most likely reason is that most of the high RPR alleles in Population 1 came from the low parent, MoSQB. Because MoSQB was only involved as a parent for Population 1, one would not expect to see the MoSQB alleles in the other three populations. A less likely, but still a plausible reason, is the differences in the degree of heterozygosity between the high RPR parent of the three populations (Table 1). The high RPR parent of Population 1, MoSCSSS-High1, had been self pollinated to produce the high parents of Populations 2 and 3, MoSCSSS-High2 and MoSCSSS-High3. Population 4 was distinctly different than other populations with only two of the 10 QTL in common with any of the other populations. The two QTL in Population 4 were in common with those in Population 3 where Mo47 was the common parent. It is not surprising that Population 4 was different from the other populations since Populations 1, 2, and 3 have a high RPR parent in common. There are various explanations for differences in QTL detected among populations. The first is that QTL analysis will only detect loci that are segregating within the population. If both parents of a population have the same allele at a QTL, that QTL will not be detected in the analysis. The second explanation is that there may be significant epistasis. A QTL in one genetic background may interact with another locus, or a number of loci, to produce a specific genetic effect, whereas in the presence of a different set of background alleles, the QTL may behave another way. A third explanation is that QTL analysis lacks sufficient statistical power to detect QTL with small effects consistently (Beavis, 1998).
The partial R2 values on a single-QTL basis were low to moderate, ranging from 2.5 to 12.9%, with the exception of the QTL on chromosome 6 in Population 4 (R2 = 20.2%) (Table 4). These data indicate a lack of a major QTL for RPR in contrast to the results from Heredia-Diaz et al. (1996). In the Heredia-Diaz study, allelic frequency changes were surveyed at 16 RFLP loci over six cycles of divergent selection for RPR. We believe that the difference in results of the present study and those of the Heredia-Diaz study lies in the different methodologies and population structures used in the experiments. Many loci underlying RPR may be essential for numerous interconnected biochemical and developmental pathways and, therefore, may show fairly consistent expression among lines, resulting in small QTL effects.
Multilocus models only explained a moderate percent of the total phenotypic variation in the first three populations: 33.4, 44.8, and 48.4% for Populations 1, 2, and 3, respectively (Table 4). Because there were no major QTL detected in Populations 1, 2, and 3, the variation in RPR was due exclusively to the segregation of several minor QTL. As discussed, QTL analysis often cannot consistently detect QTL with small effects. In Population 4, however, where there was a QTL with a relatively large effect (R2 = 20.2%), a larger proportion of the phenotypic variation was explained (58.7%).
Potential candidate genes for QTL detected in this study include those involved in lignin synthesis, the general phenylpropanoid pathway, and vegetative phase change (Table 5). Magee (1948) reported a wide lignified zone in the rind to be associated with stalk strength. Previous studies involving maize brown-midrib (bm) mutants, showed that bm1 and bm3 caused a significant reduction in lignin content (Muller et al., 1971). Zuber et al. (1977) found a significant decrease in SCS in bm3 mutants compared to their normal counterparts. Several genes encoding other enzymes in the lignin pathway also fall within confidence intervals for QTL detected in this study. Genes that encode enzymes in early steps of the general phenylpropanoid pathway may influence flux through the pathway, and, thus, increase the amount of lignin synthesis. Alternatively, blockage of pathways that compete with the lignin pathway for common substrates may increase lignin production. Mutations at whp1 and c2, block the production of flavanoids and increase production of chlorogenic acid, a phenylpropanoid compound related to lignin precursors (S. Szalma and M. McMullen, unpublished data). Abedon et al. (1999) found that vegetative phase change occurred earlier in populations selected for high RPR than low RPR. Unfortunately, no candidate gene could be identified for the QTL on chromosome 3, the only region where there was a QTL in common across all four populations.
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Received for publication November 13, 2001.
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REFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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  S. A. Flint-Garcia, M. D. McMullen, and L. L. Darrah Genetic Relationship of Stalk Strength and Ear Height in Maize Crop Sci., January 1, 2003; 43(1): 23 - 31. [Abstract] [Full Text] [PDF]
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