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CELL BIOLOGY & MOLECULAR GENETICS

Quantitative Trait Loci for First- and Second-Generation European Corn Borer Resistance Derived from the Maize Inbred Mo47

^a National Corn and Sorghum Research Center, Kasetsart Univ., Klangdong, Pakchong, Nakornratchasima,Thailand
^b USDA-ARS, Plant Genetics Research Unit and Dep. of Agronomy, Univ. of Missouri, Columbia, MO 65211
^c USDA-ARS, Plant Genetics Research Unit (retired) and Dep. of Entomology, Univ. of Missouri, Columbia, MO 65211
^d Dep. of Soil and Crop Sciences, Colorado State Univ., Ft. Collins, CO 80523
^e Dep. of Agronomy, Univ. of Missouri, Columbia, MO 65211

* Corresponding author (mcmullenm{at}missouri.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION SUMMARY REFERENCES

 
European corn borer (ECB), Ostrinia nubilalis (Hübner), family Crambidae, order Lepidoptera, is a serious insect pest of maize (Zea mays L.) in the USA. Understanding the genetic basis for ECB resistance should increase the efficiency of breeding insect-resistant germplasm. The objectives of this study were to determine the number, genomic positions, and genetic effects of quantitative trait loci (QTL) conferring resistance to leaf feeding damage cause by first-generation ECB (1ECB, defined as the trait of leaf feeding damage) and stalk tunneling caused by second-generation ECB (2ECB, defined as the trait of stalk tunnel damage). The study included 244 F_2:3 families derived from the cross of B73Ht (susceptible) x Mo47 (resistant). Inbred Mo47 represented a novel source of ECB resistance containing 50% tropical germplasm. The QTL analyses for three individual environments and combined across environments were performed by composite interval mapping using QTL Cartographer. Nine QTLs were identified for 1ECB on chromosomes 1 (three QTLs), 2, 4 (two QTLs), 5, 6, and 8, on the basis of data combined across environments. Seven QTLs for 2ECB were found on chromosomes 2, 5 (two QTLs), 6 (two QTLs), 8, and 9. Several of the QTLs detected are located in genomic regions reported for resistance to other stem borer pests of maize. Inconsistency of QTLs across environments complicates use of Mo47 for marker-assisted selection of ECB resistance.

Abbreviations: ARC, Agronomy Research Center • CIM, composite interval mapping • ECB, European corn borer • 1ECB, leaf feeding damage from first-generation European corn borer • 2ECB, stalk tunneling damage from second-generation European corn borer • DIMBOA, 2,4-dihydroxy-7-methoxy-2H-1,4-benzoxazin-3(4H)-(one) • GP, Grand Pass • HB, Hinkson Bottoms • LOD, log₁₀ of the likelihood odds ratio • MAS, marker assisted selection • QTL, quantitative trait locus • RFLP, restriction fragment length polymorphism • RIL, recombinant inbred line • SCB, sugarcane borer • SSR, simple sequence repeat • SWCB, southwestern corn borer • UMC, University of Missouri, Columbia

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION SUMMARY REFERENCES

 
THE EUROPEAN CORN BORER is a serious insect pest of maize in the USA. Losses to farmers resulting from ECB damage and control costs may exceed $1000 million each year (Mason et al., 1996). The two generations of ECB prevalent in the Corn Belt cause damage in distinct ways. Damage by the first-generation ECB is predominately by leaf-feeding at the mid-whorl stage of plant development. Throughout this manuscript we abbreviate the trait of first-generation ECB leaf feeding damage as 1ECB. Damage by the second-generation ECB beginning at the anthesis stage of plant development, is predominately by sheath feeding and stalk and shank tunneling. Likewise, for this manuscript we abbreviate the trait of second generation stalk tunneling damage as 2ECB. This damage causes direct yield losses and indirect losses due to broken stalks, dropped ears, stalk rots, and other diseases (Mason et al., 1996). The damage caused by second-generation ECB is a major concern of producers in the U.S. Corn Belt.

Previous studies using molecular markers have identified QTLs for resistance to 1ECB and 2ECB. Schön et al. (1991) identified four QTLs for resistance to 1ECB on chromosomes 1, 4, 6, and 9 in F₃ families from the cross of Mo17 (susceptible) and H99 (resistant). Schön et al. (1993) identified seven QTLs for resistance to 2ECB in an F₃ population derived from the cross of B73 (susceptible) x B52 (resistant) on chromosomes 1 (two QTLs), 2 (two QTLs), 3, 7, and 10. Lee (1993) reported 16 QTLs for resistance to 2ECB in three populations of F₃ families derived from crosses of B73 x B52, B73 x DE811 (resistant), and Mo17 (susceptible) x B52. Beavis et al. (1994) identified QTLs for resistance to 2ECB on chromosomes 7, 8, and 9 from topcrosses and F₄ families from the cross of B73 x Mo17. In the F₃ families derived from the cross of two European maize lines, D06 and D408, Bohn et al. (2000) identified six QTLs for ECB tunnel length on chromosomes 1, 3, 5 (two QTL), 9, and 10. Although some of the differences in QTLs detected may be due to methods used in measuring ECB damage, these results suggest that different QTLs for ECB resistance are present among resistant lines leading to the potential for pyramiding multiple genes which may control different mechanisms for resistance.

Quantitative trait locus analysis has also been used to study resistance to other maize stem borers. Bohn et al. (1996) identified 10 QTLs affecting resistance to the leaf-feeding generation of sugarcane borer (SCB), Diatraea saccharalis (Fab.), from 171 F₃ families from the cross of CML131 (susceptible) x CML67 (resistant). Bohn et al. (1997) extended this study to detect QTLs for resistance to the leaf-feeding generation of southwestern corn borer (SWCB), Diatraea grandiosella (Dyar), in the CML131 x CML67 population. While the majority of the QTLs detected within a population were pleiotropic for both insect species, only two or three QTLs were detected in concurrent genomic positions in a second population formed from the cross Ki3 (susceptible) x CML139 (resistant). Khairallah et al. (1998) and Groh et al. (1998) further extended these studies by mapping QTLs for leaf feeding damage by SCB and SWCB in recombinant inbred line (RIL) versions of the CML131 x CML67 and Ki3 x CML139 populations. The QTLs detected were more consistent across the CML131 x CML67 populations (RIL vs. F_2:3) than the Ki3 x CML139 populations (RIL vs. F_2:3). These studies indicate that while there was evidence for a shared genetic basis of resistance against leaf feeding by SCB and SWCB, inconsistent QTLs among environments and populations limit utility.

In this study, we report QTLs for resistance to 1ECB and 2ECB in a population of 244 F_2:3 families derived from the cross of B73Ht x Mo47. Inbred Mo47 was developed to be resistant to both 1ECB and 2ECB (Barry et al., 1995). It was extracted from the sixth cycle of half-sib recurrent selection in a topcross population developed by crossing 13 ECB-resistant tropical collections to Pioneer Brand 3184. Inbred Mo47 cytoplasm traces back to the race ‘Candela’ (collection ‘ECU 344’ from Ecuador). Because Mo47 contains novel exotic germplasm it has the potential to serve as a new source of resistance to both 1ECB and 2ECB, thus broadening the germplasm base of U.S. ECB-resistant maize. Inbred B73Ht, chosen to represent the Iowa Stiff Stalk Synthetic germplasm, is highly susceptible to both 1ECB and 2ECB. Objectives of this study were to (i) detect the number and the genomic positions of QTLs for resistance to 1ECB and 2ECB, (ii) determine the magnitude and type of their genetic effects, and (iii) quantify QTL x environment interactions.

	MATERIALS AND METHODS

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Population Development
The cross of B73Ht x Mo47 was made during the summer of 1994; during the winter of 1994–1995 F₁ plants were selfed in a nursery in Puerto Rico. In the 1995–1996 winter nursery, F₂ plants from two F₁ ears were selfed to develop the 244 F_2:3 families. During the summer of 1996, seed of the F_2:3 families was increased by sib-mating.

Experimental Design
Field experiments were conducted in 1996 and 1997. In the 1996 summer season, the experiment was grown at Grand Pass (GP), MO (Haynie silt loam). Two hundred forty-four F_2:3 families, duplicate entries of the parents and F₁, and six checks were evaluated in a 16 x 16 triple-lattice design with single-row plots spaced 0.90 m apart and 6.70 m long. Each plot was planted with one seed per hill and had a total stand of about 45 670 plants ha^-1. In the 1997 summer season, the experiment was repeated at Hinkson Bottom (HB) in Columbia, MO (Sharon silt loam), and the Agronomy Research Center (ARC) near Columbia, MO (Mexico silt loam), by means of an identical experimental design. Each location was considered as a separate environment.

Agronomic Trait Evaluation
Insect infestation
European corn borer eggs were provided by the USDA-ARS Corn Insects Research Laboratory, Ames, IA. For evaluating resistance to 1ECB, 10 plants per plot were infested manually by placing about 160 neonate ECB larvae into the plant whorl at the 8- to 10-leaf stage of plant development (mid-whorl stage) with a mechanical dispenser known as a bazooka (Mihm, 1983). Leaf-feeding damage was assessed on the 10 infested plants plot^-1 3 wk after infestation by a visual rating scale in which 1 represented no visible leaf-feeding and 9 represented severe leaf-feeding damage (Guthrie et al., 1960).

For evaluation of resistance to 2ECB, 10 plants plot^-1 that had not been infested for 1ECB were infested manually at anthesis by placing about 160 neonate ECB larvae in the leaf axil at the ear node and/or just above or below the ear node. Approximately 7 wk later, stalks of the 10 infested plants plot^-1 were split lengthwise and number of tunnels counted and the length of tunnelling estimated visually in inches. One tunnel was assigned a minimum length of 1 inch; tunnel length measurements were subsequently transformed to centimeters for analysis.

Phenotypic Data Analysis
The initial lattice analysis of variance, including parents and checks, was done by means of a proprietary FORTRAN program. The efficiency of the lattice analysis compared with a randomized complete block analysis was >110% for most character-environment combinations. Lattice-adjusted means were used as input for subsequent combined analyses across environments, excluding parents and checks. Variance components of the 244 F_2:3 families were estimated from linear functions of the mean squares. Standard errors of the estimated variances were calculated as the square root of the variance of the estimated variance component according to Anderson and Bancroft (1952) as follows:

where a_i is the linear coefficient used in calculating S², M²_i is the mean square, and the f_i is the number of degrees of freedom associated with the mean square.

Broad-sense heritability (H_b) for the F_2:3 families on an entry-mean basis was calculated by dividing the genotypic variance (²_g) by the phenotypic variance (²_p) (Hallauer and Miranda Fo., 1981). Confidence intervals on heritability estimates ( = 0.05) were calculated according to Knapp et al. (1985) as follows:

where F_g = MS,

with = 0.05.

Coefficients of phenotypic correlation among agronomic traits based on adjusted entry means of the F_2:3 families were calculated by SAS PROC CORR (SAS Institute, 1990). The distribution of the means of phenotypic traits for the the F_2:3 families were checked for normality as described by Shapiro and Wilk (1965) using SAS PROC UNIVARIATE. We used the non-transformed data for QTL analysis. As noted by Doerge and Churchill (1994) and Mutschler et al. (1996) a mixture rather than normal distribution is expected if a substantial portion of the variance for a quantitative trait is controlled by genes with moderate to large effects. Normalizing data by transformation may misrepresent differences among individuals by moving individuals from the tails toward the center of the distribution, thereby lessening power to estimate the actual gene effects (Mutschler et al., 1996).

Molecular Marker Assays
Genomic DNA was extracted from bulked-leaf tissue of 20 to 25 plants of each F_2:3 family and parental line, and digested with six restriction enzymes, EcoRI, HindIII, EcoRV, BamHI, DraI, and XbaI. Fragments of DNA were separated by agarose gel electrophoresis and transferred onto membranes by Southern transfer (Southern, 1975). Hybridization of membranes was performed with 97 radioactive RFLP probes, largely from the University of Missouri-Columbia (UMC) core RFLP marker set (Davis et al., 1999), previously determined to be polymorphic among the parents. One simple sequence repeat (SSR) primer pair was used to detect an additional locus. Southern hybridization and SSR amplification were performed as previous described (Byrne et al., 1996; Davis et al., 1999).

The genetic linkage map was determined by MAPMAKER/EXP 3.0 (Lander et al., 1987; Lincoln et al., 1992). Each marker locus was analyzed to detect significant deviations of genotypic frequencies from the expected Mendelian segregation ratio of 1:2:1 by chi-square (²) tests (P < 0.001) by means of PLABQTL (Utz and Melchinger, 1996).

Quantitative Trait Locus Analyses
All QTL analyses for individual environments and combined across environments were performed by QTL Cartographer v. 1.13g (Basten et al., 1997). The method of composite interval mapping (CIM; Jansen and Stam, 1994; Zeng, 1994), model 6 of the Zmapqtl program module, was employed for detecting QTLs and estimating their effects. The genome was scanned at 2-cM (centimorgan) intervals and the window size was set at 10 cM. Cofactors were chosen using the forward-backward method of stepwise regression at p(F_in) = p(F_out) = 0.01. A series of 1000 permutations (Doerge and Churchill, 1996) was performed to determine experiment-wise significance levels at P = 0.05 of LOD (log₁₀ of the likelihood odds ratio) 3.51 for 1ECB and LOD 3.64 for 2ECB. Additive and dominance effects for detected QTLs were also estimated by the Zmapqtl procedure of QTL Cartographer. Because dominance effects calculated from F_2:3 families are expected to be reduced by half relative to their F₂ parents, estimates of dominance effects were multiplied by two (Mather and Jinks, 1971). Gene action was determined by the ratio of the absolute value of the estimated dominance effect divided by the absolute value of the estimated additive effect (||/|â|) following Stuber et al. (1987); (additive = 0 to 0.20; partial dominance = 0.21 to 0.80; dominance = 0.81 to 1.20; and overdominance > 1.20). The R² value, the percentage of the phenotypic variance explained by marker genotype at the QTL, (coefficient of determination) was taken from the peak QTL position as estimated by QTL Cartographer.

Digenic epistatic interaction between all pairs of marker loci was evaluated with the program EPISTACY (Holland, 1998). Multiple locus models based on Type III sums of squares were constructed (SAS GLM) using the closest marker loci to QTLs identified by CIM and epistatic interaction terms (P < 0.001) (Byrne et al., 1996, 1998). The best model was determined to be that which explained the greatest fraction of the phenotypic variance and in which individual loci or their interactions remained significant at P < 0.05.

	RESULTS

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Agronomic Trait Analysis
From the means in the individual environments, it was clear that the highest ECB pressure for both traits was obtained at the ARC environment (Table 1). The distribution of phenotypic means, combined across three environments, displayed a normal distribution for 1ECB. In contrast, 2ECB showed a significant deviation from normality as tested by Shapiro and Wilk (1965) (Fig. 1) , with the majority of the lines exhibiting greater than midpoint resistance. No significant transgressive segregation was observed for either trait.

View larger version (18K): [in this window] [in a new window]   Fig. 1. (A) First-generation European corn borer leaf-feeding damage rating frequency distribution for 244 F2:3 families evaluated at three environments over 1996 and 1997. (B) Second-generation European corn borer tunnel length frequency distribution for 244 F2:3 families evaluated at three environments over 1996 and 1997.

View larger version (26K): [in this window] [in a new window]   Fig. 2. Molecular linkage map of 97 RFLP and 1 SSR loci and location of the QTLs from the combined analysis. Numbers to the right of the chromosomes indicate the cumulative distance in centimorgans (cM). The small, filled boxes along the chromosome indicate the positions of the bin boundaries. The numbers between the bin boundaries indicate bin number. The open circles along the chromosomes indicate approximate centromere position. The Bars indicate the region in which LOD scores exceed 3.5 and the number within the bars indicates the maximum LOD score and its position.

	DISCUSSION

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QTLs for Resistance to 1ECB
In the combined analyses, we identified nine QTLs affecting resistance to 1ECB (Table 3). For seven QTLs, the alleles for increasing resistance were contributed from resistant parent, Mo47, and two were from the susceptible parent, B73Ht. The QTL in chromosome bin 4.01, contributed by Mo47, was located in the region of bx1 gene that is required for synthesis of DIMBOA (Simcox and Weber, 1985). Inbred line Mo47 was developed from a cross containing 50% tropical and 50% temperate germplasm. A gene controlling high DIMBOA content might have come from either the temperate germplasm (Pioneer Brand 3184) or the tropical germplasm. According to Sullivan et al. (1974), some tropical germplasm showed a high level of resistance to 1ECB, but levels of DIMBOA were generally low. Barry et al. (1994) showed that Pioneer Brand 3184 was very low in DIMBOA. This result suggests that if the 1ECB resistance in Mo47 from chromosome bin 4.01 is DIMBOA based, the DIMBOA came from the tropical germplasm, or was caused by novel epistatic interactions among alleles from the tropical and temperate parents. High performance liquid chromatography assays indicated that whorl-leaf tissue of Mo47 contained approximately twice the DIMBOA as B73Ht (Byrne and McMullen, data not shown).

To determine if QTLs determined in this study may be equivalent to QTLs from other studies, we used the bin concept. The bins in maize are generally 15 to 20 cM in length, essentially the same length as the confidence interval for most QTLs. We considered QTLs from separate studies or traits to be potential matches if they occurred in the same or adjacent bin. Quantitative trait loci for 1ECB detected in our study share bin position with the QTLs detected for 1ECB by Schon et al. (1991) in chromosome bins 1.06 and 6.02. The QTLs for resistance to 1ECB in our study at chromosome bins 1.06, 1.11 2.09, 5.05, and 8.06 were in similar bins as QTLs for resistance to SWCB at 1.06, 1.07, and 1.10 and to QTLs for resistance to SCB at 1.07, 1.12, 2.08, 5.06, and 8.06 (Bohn et al., 1997). The QTLs for resistance to 1ECB in chromosome bins 1.01, 1.06, 1.11, 5.05, and 8.06 may correspond to QTLs for resistance to SWCB at 1.01/02, 1.06/08, 1.10/11, 5.05/06, and 8.05/06 and to QTLs for resistance to SCB at 1.06/08, 1.11/12, 5.06, and 8.05/06 (Groh et al., 1998). From our study, the QTLs in chromosome bins 4.01 (LOD = 11.2, R² = 14.6%), and 4.06 (LOD = 5.1, R² = 9.3%) were new QTLs which have not been previously reported by other researchers for ECB or other leaf-feeding insect species.

Thome et al. (1992) studied a 10-parent diallel cross of eight CIMMYT inbreds (CML59, CML73, CML121, CML122, CML123, CML135, CML136, and CML137), Ki3 and B73 for leaf-feeding damage by SWCB, SCB, and ECB. First-generation SWCB and first-generation SCB leaf feeding damage scores were highly correlated, indicating that selection for resistance to one insect conferred resistance to the other insect. First-generation ECB leaf-feeding damage was lowest and correlation coefficients of ECB with SWCB and ECB with SCB were lower than between SWCB and SCB. While selection for resistance to ECB leaf-feeding may not confer resistance to SWCB and SCB, selection for SWCB or SCB appears to confer resistance to ECB leaf-feeding (Thome et al., 1992). These results demonstrated that more aggressive insects provided a higher level of damage for germplasm screening and better distinguish between resistant and susceptible germplasm. These results also suggested that the mechanism of resistance to SWCB or SCB may provide resistance to ECB, which is in line with shared QTL chromosome regions in ECB, SWCB, and SCB studies.

Quantitative trait loci in our study in chromosome bins 1.06, 1.11, 5.05, and 8.06 were located in similar bins as QTLs for leaf toughness (Groh et al., 1998). Similarly, QTLs for 1ECB resistance in chromosome bins 1.06, 1.11, 5.05, and 8.06 were close to QTLs reported for leaf protein content. While we did not attempt to measure the correlated traits of leaf toughness and protein content studied by Groh et al. (1998), the presence of QTLs in similar genomic regions suggests these antibiosis mechanisms may be involved with 1ECB resistance in Mo47.

QTLs for Resistance to 2ECB
In the combined analyses (Table 4), we found seven QTLs for resistance to 2ECB. All alleles contributing resistance were from the resistant parent, Mo47, except for the QTL detected in chromosome bin 6.00. In this population, QTLs for resistance to 2ECB were not in the same regions as QTLs for resistance to 1ECB, except in chromosome bins 5.05 and 6.01/02. This result clearly indicated that different genes control mechanisms for resistance to 1ECB and 2ECB in Mo47.

Quantitative trait loci for resistance to 2ECB in chromosome bins 5.05 and 5.08 were in the same regions as QTLs for resistance to 2ECB in chromosome bins 5.06 and 5.09 from the cross of B73 x DE811 (Lee, 1993). In a recently completed study of 2ECB resistance in RILs of B73 x B52 (Cardinal et al., 2001), nine QTL were detected. Quantitative trait loci from chromosome bins 5.05 and 9.03–9.04 were in common between the B73 x B52 RIL population and our study. Additional evidence for QTLs from chromosomes bins 5.05 and 9.03 was also obtained by Bohn et al. (2000) in the (D06 x D408) F₃ population.

Quantitative trait loci for resistance to 1ECB and 2ECB from this study were compared with disease and insect resistance loci reported by McMullen and Simcox (1995). We found that QTLs for resistance to 1ECB were in the same chromosome regions as some major maize disease resistances on chromosomes 1, 2, 4, 5, 6, and 8. Quantitative trait loci for resistance to 2ECB in this study were in the same chromosome regions as some major maize disease resistance genes on chromosomes 5, 6, 8, and 9. This result supports the concept that resistance genes for diseases and insects in maize are not randomly distributed over the genome, but located in clusters (see also Bohn et al., 2000).

Type of Gene Action of 1ECB and 2ECB QTLs
For 1ECB, four QTLs showed additive gene action, three QTLs showed partial dominance, one QTL showed dominance, and one QTL displayed overdominance. These results indicated that additive gene action was most frequent for resistance to 1ECB, but partial dominance was also present. This is consistent with the distribution of family means for 1ECB with a normal distribution, and F₁ and family means approximately at the mid-parent value (Table 1 and Fig. 1). For 2ECB, three QTLs showed overdominance, two QTLs showed additive gene action, and two QTLs showed partial dominance. Overdominance in the direction of resistance to 2ECB provides an explanation to the skewed distribution of family means for 2ECB. In addition, the F₁ was closer to the resistant parent suggesting that the majority of gene action of QTLs for 2ECB is dominant or overdominant. In contrast, Schön et al. (1991)( 1993) and Bohn et al. (2000) identified QTLs with mainly additive gene action for both 1ECB and 2ECB resistance in F₃ populations derived from three different crosses.

Consistency of QTLs Across Environments
One of the major goals of QTL mapping is to locate markers for a trait of interest and to select markers linked to QTLs controlling the trait. The selected markers would then be used for marker assisted selection (MAS) in a breeding program. One major concern in using MAS for QTLs has been the lack of consistency of QTLs across environments. Generally, plant breeders test genotypes over a range of environments and have found that significant genotypes x environments interactions are common for quantitative traits. However, individual QTLs might show a range of sensitivity to environmental changes. In this experiment, a total of 11 QTLs for 1ECB and 2ECB were detected in three environments and data combined over environments; only one (9%) was detected in all three environments and the combined analysis, three (27%) were detected in two environments and the combined analysis, and seven (64%) were detected in only a single environment and in the combined analysis. The result was in close agreement with a classic study in tomato (Lycopersicon esculentum Mill.) by Paterson et al. (1991) where they found that among a total of 29 QTLs mapped in three environments, four (14%) were expressed in all three environments, 10 (34%) were expressed in two environments, and 15 (52%) were expressed in only a single environment. In contrast, Stuber et al. (1992) measured seven agronomic traits in maize in six diverse environments in the same population. They found that QTLs detected in one environment were frequently found in the other environments, suggesting little QTL x environment interaction. Schön et al. (1994) reported similar results in that most likelihood peaks were identified in the same marker intervals for all environments and differed only in the level of significance and the size of estimated genetic effects.

An additional factor to consider in QTL studies on insect resistance is the potential variability introduced with the biology of the insect. For both 1ECB and 2ECB, the insect damage was much greater at the ARC environment than the other two environments. This could be due to differences in the insects, environmental conditions, or physiology of the plants at infestation. While it cannot be discerned which environment(s) would best mimic natural infestation, it would seem wise to pick markers linked to QTLs which produced major effects and which were consistent across environments. Quantitative trait loci mapping of ECB from other researchers may also be included when choosing molecular markers. The knowledge obtained from this study can be used to set up a MAS program. The QTLs in chromosome bins 4.01 and 6.02 are good candidates for incorporating ECB leaf-feeding resistance and the QTLs in chromosome bins 5.05, 5.08, and 9.02 are good candidates for resistance to second generation ECB stalk tunneling.

	SUMMARY

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION SUMMARY REFERENCES

 
The results from this study are as follows.

1. We found novel QTL alleles for resistance to 1ECB and 2ECB derived from the inbred Mo47 that may be used for improving host-plant resistance. These were contributed from tropical germplasm parents and broaden the germplasm base of ECB-resistant maize lines.

2. Many of the genomic regions that control resistance to 1ECB have also been reported to control resistance to leaf feeding by SWCB and SCB, suggesting common genetic control for multiple species.

3. Few consistent QTLs for all individual environments and combined over environments were found for either trait.

	ACKNOWLEDGMENTS

 
We are grateful to Ms. Guilin Xu, Ms. Theresa Musket, and Ms. Katherine Houchins for technical assistance with the RFLP and SSR analyses; Dr. Gary F. Krause for statistical advice; Mr. Arturo Garcia for help drawing the genetic map; and Dr. E.H. Coe for advising on map detail. We thank the entire breeding, entomology, and genetics support staff and students for help with infestations and stalk splitting. We thank Andrea Cardinal and Mike Lee for sharing prepublication results on the B73 x B52 RIL population. This research was supported by North Central Biotechnical Initiative Grant 593-0220-07 and funds to USDA-ARS. Brand names are important to factually report on available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by USDA implies no approval of the product to the exclusion of others that may also be suitable.

Received for publication January 27, 2001.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION SUMMARY REFERENCES

 

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