HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (20) Citing Articles via Google Scholar Google Scholar Articles by Austin, D. F. Articles by Hallauer, A. R. Search for Related Content PubMed Articles by Austin, D. F. Articles by Hallauer, A. R. Agricola Articles by Austin, D. F. Articles by Hallauer, A. R.

CROP BREEDING, GENETICS & CYTOLOGY

Genetic Mapping in Maize with Hybrid Progeny Across Testers and Generations

Grain Yield and Grain Moisture

^a Pioneer Hi-Bred International, Inc., 7100 N.W. 62nd Ave, Johnston, IA 50131 USA
^b Dep. of Agronomy, Iowa State Univ., Ames, IA 50011 USA
^c Holden's Foundation Seeds, Inc., P.O. Box 839, Williamsburg, IA 52361 USA

mlee{at}iastate.edu

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Most complex quantitative traits in maize (Zea mays L.), especially grain yield, display low correlations between the trait values observed in inbred and hybrid progeny. Comparisons of quantitative trait loci (QTL) controlling inbred per se and hybrid performance are needed to understand the underlying genetic factors, and to determine the utility of QTL detected in the two progeny types. DNA markers were used to identify QTL for grain yield and grain moisture in hybrid progeny of F_2:3 and F_6:8 lines from a Mo17xH99 population. For both generations, testcross progeny were developed by crossing the lines to three inbred testers (B91, A632, B73). Each testcross population was evaluated in field trials with two replications in eight environments. The testcross progeny from the two generations were evaluated at the same locations but in different years. QTL were identified within each testcross population and for mean testcross (MTC) performance. Individual tester QTL effects were not consistent in rank or detection across generations; however, parental contributions were consistent. MTC effects were more consistent across generations with most of the QTL with large effects being detected across generations. QTL detected with only one tester were not necessarily detected for the other two testers, especially for grain yield, but parental contributions were consistent when QTL were detected in a region for more than one tester. The QTL for grain yield identified in this population for inbred and hybrid progeny show only partial correspondence, indicating that marker-assisted selection programs would need to identify and incorporate QTL for both progeny types.

Abbreviations: cM, centimorgan • MTC, mean testcross • QTL, quantitative trait locus(i) • RFLP, restriction fragment length polymorphisms • RIs, recombinant inbreds • SI, support intervals • SSR, simple-sequence repeats

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
MOST TRAITS IN MAIZE, especially grain yield, display low correlations between the trait values observed in inbred and hybrid progeny (Hallauer and Miranda, 1988). Thus, evaluating hybrid combinations of inbred lines, or testcrosses, is the primary concern in maize breeding programs. Although the low correlations observed between inbred and hybrid performance may indicate the importance of non-additive gene action for hybrid progeny, a model with only additive and dominance effects can also explain the empirical observations due to the masking effects of favorable dominant alleles in the tester (Smith, 1986). Lines with superior hybrid performance would be those with a high frequency of favorable alleles that are absent in the tester; however, these lines may not necessarily have a high frequency of favorable alleles for inbred per se performance. Therefore, QTL which have been identified on the basis of inbred per se evaluations may not be the same QTL controlling hybrid performance for a given set of testers. Comparisons of QTL controlling inbred per se and hybrid performance are needed to understand the underlying genetic factors and to determine the utility of QTL detected in the two progeny types.

Previous studies have revealed mixed success for QTL detection across testcross progeny of different testers. Guffy et al. (1988, 1989) reported that genetic background had a large effect on the detection of QTL for grain yield and morphological traits across three testcross populations. With progeny from two testers, Schön et al. (1994) reported highly consistent QTL locations for kernel weight and plant height but not for protein content. Ajmone-Marson et al. (1995) evaluated grain yield, dry matter content, and test weight in two divergent populations of hybrid progeny and reported that QTL exhibited by one population were not necessarily detected with the second population, but QTL with larger effects were consistent across populations. Lübberstedt et al. (1997) reported consistent QTL detection across tester populations for dry matter content and plant height but not for dry matter yield. Kerns et al (1999) reported few common marker-trait associations across two diverse testers for grain yield and other agronomic traits. On the basis of these reports, the consistency of QTL across testcross populations seems to be trait dependent and varies with the relationship of the testers.

In agreement with the low correlations observed between per se and testcross performance, little evidence of common QTL detection for the two progeny types has been observed. Guffy et al. (1988, 1989) reported inconsistent detection of QTL between inbred per se and testcross progeny from three testers. In a comparison of F_2:4 progeny per se and testcross progeny for a single tester, Beavis et al. (1994) identified few common QTL for grain yield and several morphological traits. Similarly, Schön et al. (1994) reported only one QTL for kernel weight and two QTL for protein content were common to inbred per se and testcross (two testers) progeny. Groh et al. (1998) reported two of four QTL identified with testcross progeny (single tester) for leaf feeding resistance (two corn borer pests evaluated) were also detected in per se progeny evaluations; however, few QTLs for agronomic traits were common to the two progeny types. Kerns et al. (1999) reported that the number of common marker-trait associations between inbred per se and testcross progeny varied by tester (two testers evaluated) and trait (six traits evaluated). The most common marker-trait associations were observed for plant and ear height with the tester unrelated to the population, and the fewest common associations were observed for grain yield with a related tester. These empirical studies seem to indicate that QTL controlling inbred performance, in general, are not strongly related to those controlling hybrid performance.

In the present study, F_2:3 and F_6:8 progeny derived from a single-cross population of lines Mo17 and H99 were crossed to three inbred testers to create three populations of testcross progeny for each generation. Similar to an early generation breeding program, the testcross progeny from the two generations were evaluated for grain yield and grain moisture at the same locations but in different years. The first objective was to compare performance and QTL detection across testcross progeny of early (F_2:3) and late (F_6:8) generations. The second objective was to compare the detection of QTL across the three testers. Grain yield QTL for the hybrid progeny were also compared with QTL from F_2:3 and F_6:7 per se evaluations of inbred progeny from the same population (Austin and Lee, 1998).

	Materials and methods

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Population Development
The single-cross population was developed from the adapted and widely used U.S. Corn Belt maize inbreds Mo17 and H99, both classified as members of the Lancaster Sure Crop (LSC) heterotic group on the basis of on pedigree and RFLP data (Melchinger et al., 1991). One hundred ninety-four unselected F_2:3 lines were developed from the population (Veldboom, 1994). From the same population, 186 unselected F_6:7 lines were produced by single-seed descent, 147 of which are descendants of the F_2:3 lines (Austin and Lee, 1998). Ten plants per F_6:7 line were self-pollinated, and equal quantities of F_6:8 seed per plant were bulked.

Genetic Maps
The F_2:3 and F_6:8 genetic maps have been presented by Veldboom (1994), Veldboom et al. (1994), and Austin and Lee (1998). The laboratory methods used to derive these maps were described by Veldboom et al. (1994). The F_2:3 map was developed with marker data from 303 lines, including the 194 F_2:3 lines used here to produce testcross progeny. The F_6:8 map was developed with marker data from 185 of the 186 F_6:8 lines used here to produce testcross progeny. The F_2:3 linkage map spans 1413 cM, and consists of 106 RFLP loci and one color locus (P1), with an average interval length of 15 cM. The F_6:8 linkage map (available electronically on MaizeDB website, http://www.agron.missouri.edu; verified August 10, 1999) spans 1601 cM, and consists of 100 RFLP loci, 41 SSR loci (Senior et al., 1996), and one color locus (P), with an average interval length of 12 cM. Approximate positions of centromeres were based on previous maps (Coe et al., 1990, 1995; Veldboom et al., 1994; Matz et al., 1995). On the basis of centromere placement, chromosomal regions will be referred to using a number for the chromosome (from 1–10) and the letter L (long arm), S (short arm), or C (region including centromere).

Eighty-six RFLP loci and the color marker locus P1 are in common between the two marker maps. The order of these loci is identical except for a pair on 9L (npi209-bnl14.28) which are linked by 2 cM in the F_6:8 map and by 5 cM (in opposite order) in the F_2:3 map. The 20 RFLP loci mapped in the F_2:3 but not in the F_6:8 were placed on the map on the basis of their positions relative to the 87 common loci. Two of these loci (isu86 and isu145A) extended the distal region of 7S beyond what was mapped in the F_6:8.

Testcross Progeny Development
For both the F_2:3 and F_6:8 generation inbred populations, crosses were made to three inbred testers, B91, A632, and B73. B91 is derived from Iowa Corn Borer Synthetic #1 (BSCB1), which has some progenitor LSC lines and is considered unrelated by pedigree to Reid Yellow Dent and certainly unrelated to the other two testers. B91 was released in 1989 and has a maturity classification of AES800 (Russell, 1989). A632 and B73 are classified as Reid Yellow Dent inbreds (Gerdes et al., 1993). A632, released in 1964, was derived through three backcross generations with selection for earliness with B14 as the recurrent parent. A632 has a maturity classification of AES600. B73 was released in 1972 and has a maturity classification of AES800 (Russell, 1972). B14 and B73 were both derived Iowa Stiff Stalk Synthetic; however, they are distinct and exhibit a high level of genetic dissimilarity for elite stiff-stalk germplasm (Melchinger et al., 1991). Both A632 and B73 were widely used in commercial hybrids with Mo17 and H99 (Zuber and Darrah, 1980).

At Ames in 1991, F_2:3 testcross progeny were produced (Veldboom, 1994). The 194 F_2:3 lines were grown in paired rows with each tester. Ten plants per line were crossed to the tester with the seed produced on the F_2:3 lines for B91 and A632. Because of maturity differences, B73 was used as the female parent with each F_2:3 plant being used only once as a pollen source. For each line by tester combination, the ears were hand harvested and individually shelled. Equal amounts of seed from each ear were bulked to provide the testcross seed for field evaluations. Similarly, the 186 F_6:8 lines were grown in paired rows with each tester at Ames in 1994. Ten pollinations were made within each pair with the tester as the seed parent, with each F_6:8 plant being used once as a pollen source whenever possible. In the few instances where timing of flowering did not allow sufficient pollinations with the tester as the seed parent, the tester was used as the pollinator. For each line by tester combination, the ears from the paired rows were harvested and shelled in bulk to provide testcross seed for performance trials. The parents, Mo17 and H99, were also crossed to each tester in 1991 and 1994 to produce F₁ hybrids to include as checks.

Field Evaluations
The F_2:3 testcross populations were evaluated at four locations in Iowa in 1992 and 1993 (Veldboom, 1994). The F_6:8 testcross populations were evaluated at the same locations (Calamet, Ames, Kanawha, Nashua) in different years (1995 and 1996). Each testcross population was treated as a separate experiment, and the same experimental design (14 by 14 lattice, two replications) was utilized for each tester–location–year combination. At each location, the three experiments were adjacent to each other. For the F_2:3 generation, entries consisted of testcross progeny of the 194 lines and single entries for Mo17 and H99, whereas the F_6:8 entries consisted of the 186 lines and five entries each of Mo17 and H99 in each replication. The entries were machine planted in two-row plots which were 5.5 m long with 0.76-m spacing between rows. Planting densities were 76 500 kernels ha^-1 for the F_2:3 and 86 100 kernels ha^-1 for the F_6:8. For both generations, plots were thinned to 62 000 plants ha^-1 at the six-to-eight leaf stage. Grain yield and grain moisture for machine-harvested plots were recorded at all locations. Grain moisture (g kg^-1) was electronically measured from each plot sample on the harvesting machine. Total grain yield of each plot was corrected to 155 g kg^-1 moisture and converted to Mg ha^-1.

Trait Data Analysis
The F_2:3 and F_6:8 testcross experiments were evaluated separately using the same procedures. Adjusted entry means for each year–location–experiment combination were obtained by correcting for incomplete block effects according to Cochran and Cox (1957). A combined analysis of variance was conducted separately for each tester, and estimates of genetic (²_g) and genotypic x environment (²_ge) variance components were obtained. For each generation, the experimental design allowed the experiments to be combined across testers, and variance components were estimated for genetic (²_g), genotype x tester (²_gt), genotype x environment (²_ge), and genotype x tester x environment (²_gte). Heritabilities were calculated on an entry mean basis (Hallauer and Miranda, 1988) for each tester and across testers (MTC), and exact 90% confidence intervals were calculated according to Knapp et al. (1985). Within each generation, simple phenotypic correlations were calculated among the testers for each trait by mean performance across environments. Simple phenotypic correlations were also calculated between the F_2:3 and F_6:8 generations for each tester by mean values for the 147 lines of common descent.

QTL Detection
Assessments of QTL were made separately for each tester by the mean performance across environments for the F_2:3 and F_6:8 generations. QTL for MTC were detected by mean performance across all testers and environments. Previous studies in this population with inbred progeny at the F_2:3 (Veldboom and Lee, 1996a,b) and F_6:7 (Austin and Lee, 1998, 2000) generations have shown the mean environment to be the most representative of QTL with consistent effects across environments. Therefore, mean performance across environments within the F_2:3 and F_6:8 generations was utilized to compare consistency of QTL across testers and generations. QTL were identified by composite interval mapping (Jansen and Stam, 1994; Zeng, 1994). All computations for this method were performed with the software package PLABQTL (Utz and Melchinger, 1996) which employs interval mapping by the regression approach (Haley and Knott, 1992) with selected markers as cofactors. The underlying model for testcross progeny (Lübberstedt et al., 1997) can be written as:

where y_j is the phenotypic mean of the testcross progeny of Line j; µ_P1 is the mean phenotypic value of testcross progeny with the P1 (P1 = parent one) allele at the putative QTL; _l is the average effect of substituting a P1 allele with a P2 at the QTL in the marker interval (l, l+1); x^*_jl is the conditional expectation of the dummy variable _l given the observed genotypes at the flanking marker loci, where _l assumes values 0, 0.5, or 1 if the genotype at the putative QTL is QQ, Qq, or qq, respectively; b_k is the partial regression coefficient of phenotype y_i on the kth (selected) marker; x_jk is a dummy variable (cofactor) assuming values of 0, 0.5, or 1 if the genotype of line j at locus k is homozygous P1, heterozygous, or homozygous P2, respectively; and e_j is the residual error. Cofactors were selected by stepwise regression, and the final selection was for the model that minimized Akaike's information criterion with penalty=3.0 (Jansen, 1993). To enable comparisons across testers and generations, a LOD threshold of 2.0 was selected for QTL detection. By the chi-square approximation suggested by Zeng (1994), this corresponds to a comparisonwise type I error rate of P < 0.01 on the basis of the number of intervals being tested in the F_6:8 testcross evaluations (Utz and Melchinger, 1996). For each QTL, a one-LOD support interval was constructed as described by Lander and Botstein (1989). On a chromosome, QTL with non-overlapping one-LOD support intervals (SI) were considered as different regions. To allow comparison of QTL positions across generations, F_2:3 testcross population QTL positions were adjusted to correspond to the F_6:8 linkage map on the basis of relative position to the 87 RFLP loci common to the linkage maps constructed in both generations.

Estimates of the percentage of phenotypic variance explained by individual QTL were obtained by the square of the partial correlation coefficient between the respective QTL and the phenotypic observations, keeping all other QTL effects fixed. Estimates of the single QTL effects as well as the total LOD score and phenotypic variation explained by all QTL were obtained by simultaneously fitting a model including all QTL detected for the trait by tester combination (Utz and Melchinger, 1996).

	Results and discussion

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Analysis of Trait Data
The parental and progeny means are shown for each testcross population and across testers in Table 1 . Progeny means differed significantly among testers for grain moisture but not for grain yield. For each tester, the F_2:3 testcross progeny had lower mean grain yield and grain moisture than the F_6:8 testcross progeny. The differences were likely due to environmental factors. The two generations were evaluate in different years. Assuming no forces other than natural selection during the development of the F_6:8 lines, no change in average gene frequency would be expected in the two sets of lines (Hallauer and Lopez-Perez, 1979). Previous evaluations of the RFLP data for the F_2:3 (Veldboom et al., 1994) and F_6:7 (Austin and Lee, 1996a) generations revealed distribution of marker classes within expectations and average parental allele frequencies near 50%, indicating no evidence of unintentional selection.

Significant genetic and genotype x environment variance components were observed in both generations for all three tester populations for grain yield and grain moisture (Table 2) . Estimates of ²_g derived from the F_6:8 generation were 3.4 and 1.3 times greater than those derived from the F_2:3 generation, for grain yield and grain moisture, respectively. These differences were similar to those of Hallauer and Lopez-Perez (1979) for S₈ and S₁ testcrosses, and were consistent with the expectation that in the absence of dominance and/or with gene frequency of 0.5, the genetic variation among F_6:8 lines should be double that among F_2:3 lines (Hallauer and Miranda, 1988).

Significant phenotypic correlations between inbred progeny (Austin and Lee, 1998) and MTC values for grain yield were observed for the F_2:3 (r_p = 0.20) and F_6:8 (r_p = 0.19) generations (Table 3). These values are similar to the average correlation of 0.22 reported by Hallauer and Miranda (1988) in a summary of six studies. These low phenotypic correlations indicate that phenotypic selection for hybrid yield would not necessarily assure superior inbred per se yield and vice versa.

Comparison of QTL Detection in F_2:3 and F_6:8 Generations
The F_6:8 generation should be more efficient and powerful for QTL than the F_2:3 generation detecting because of increased homozygosity, homogeneity of progeny, and increased recombination for separation of linked QTL (for review see Austin and Lee, 1996a). In this experiment, the majority of the F_6:8 lines were direct descendants of the F_2:3 lines, and any differences in QTL detection are likely due to the advantages of using RI lines, additional marker loci in the F_6:8 linkage map, environmental effects, or sampling variation. For grain yield, more QTL were detected in the F_6:8 generation than in the F_2:3 generation for A632, B73, and MTC, whereas the same number were detected in both generations for B91 (Table 4) . Few QTL were common across generations: only one each for B73 and B91, three for A632, and four for MTC. For all three individual testers, the QTL that were common across generations were not those with the largest effects within each generation (Table 5) . For MTC, the four QTL that were common across generations were those with the largest effects in the F_6:8 generation, and three of the four had the largest effects in the F_2:3 generation. In all instances of common QTL across generations, the direction of parental contribution was the same.

Comparison of MTC and Individual Tester QTL and Their Effects
For grain yield, 24 QTL were identified with individual tester and/or MTC effects in at least one generation (Table 5). Of the 24 grain yield QTL, 14 (58%) were associated with effects for only one tester. For grain moisture, 34 QTL were identified (Table 6). Sixteen of the 34 (47%) grain moisture QTL were associated with effects for only one tester. The parental (Mo17 and H99) alleles at QTL associated with a single tester presumably have specific dominance interactions with the respective testers that do not occur with the other testers.

Ten grain yield QTL were associated with effects for more than one tester (either generation), and eight of these were also associated with MTC for at least one generation (Table 5). One region (3L, npi212-isu1-sh2) contained QTL for MTC and all three testers. The QTL in this region had the largest MTC effect in both generations, but detection of QTL for the three testers was completely different (B91 and B73 in F_6:8, A632 in F_2:3) in the two generations. QTL with the second largest effect for B91 and the largest effect for B73 were detected in this region in the F_6:8, but were not detected for these two testers in the F_2:3. Similarly, the QTL with the largest F_2:3 effect for A632 was detected in this region, but not in the F_6:8. This QTL appears to be important for grain yield for all three testers; however, the magnitude of the effect for a given tester is not consistent across generations. One region, (7L phi077-Pl1), had an F_6:8 QTL for A632 with the H99 allele conferring increased grain yield, whereas an F_2:3 QTL for B91 was detected in this region associated with Mo17. This may indicate an interaction of the parental alleles with the alleles for the two testers at this QTL; however, this is the only QTL that displayed different parental contributions at QTL for different testers.

For grain moisture, 14 QTL were associated with effects with more than one tester (either generation), and all but one were associated with MTC in at least one generation (Table 6). Eight regions (1C, 1L, 2L, 4L, 5L, 7S, 7L, 10L) contained QTL for MTC and all three testers. The region on 7S (isu145A-phi053-bnl15.40) had a major effect on grain moisture for all tester-generation combinations with the second and third largest MTC effects for the F_2:3 and F_6:8, respectively. The region also had QTL with the largest F_2:3 effects for B91 and A632, and QTL effects were among the six largest for each of the other tester x generation combinations. The region on 1L (npi236-phi039-umc37-an2.6) was also detected for all tester x generation combinations, but the effects were not among the three largest for the individual testers or MTC in either generation.

Several trends were apparent for the QTL effects across tester populations for grain yield and grain moisture. First, the interactions of QTL effects among tester populations were changes in magnitude of substitution effects, not changes in parental contribution (cross-over type interaction of the parental alleles with the testers). Another similarity is that both Mo17 and H99 contributed to increased trait values with each tester and MTC for both traits. This was not surprising since both inbreds were widely used in commercial hybrids (Zuber and Darrah, 1980). Grain yield QTL with the largest effects for each tester and MTC in both generations were associated with H99. Similarly, the QTL with the largest effects for grain moisture were contributed by H99, except for F_6:8 MTC (5L) and A632 (6L) effects which were contributed by Mo17. The number of common QTL between testers did not correspond with the relatedness of the testers for either trait. For grain yield, B73 and A632 (both Iowa Stiff Stalk Synthetic background) shared three QTL regions. However, A632 and B73 shared four and three regions, respectively, with the unrelated tester, B91. B73 and A632 did have greater phenotypic correlations for grain moisture than other pairs of testers. The results were more favorable for grain moisture with ten QTL common between B73 and A632. The number of common QTL were slightly less for the B73-B91 (7) and A632-B91 (8) pairs.

Comparison of Inbred and Hybrid QTL for Grain Yield
In a comparison of grain yield QTL detected with inbred progeny of the F_2:3 and F_6:7 generations from this population, 10 regions were identified that contained QTL in at least one inbred generation (Austin and Lee, 1998). Five of these QTL (1L, 2L, 3L, 5S, 8L) seem to be associated with testcross progeny QTL effects herein (on the basis of overlapping SI). QTL on 1L (near isu6), 2L (isu117-bnl8.44B), and 8L (npi268-umc48) were each only associated with effects for a single tester, but parental contributions were the same in both inbred and testcross progeny. The QTL on 5S (near umc72) was detected in the F_6:7 inbred generation with the smallest QTL effect, and it was not detected in the F_2:3 inbred generation. This region was detected in the hybrid evaluations for A632 and MTC; however, the parental contribution was from H99 for the hybrid and Mo17 for the inbred progeny. The QTL for the inbred progeny explaining the largest portions of the grain yield variation in the F_2:3 (31%) and F_6:7 (13%) was located on 6L (near npi280) and was not detected with testcross progeny. The region with the largest F_2:3 and F_6:8 QTL effects for MTC (3L) was detected in both inbred generations with the same parental contribution; however, the QTL effects were the second smallest detected for both inbred generations. On the basis of the theoretical expectations (Smith, 1986), previous studies (Gocken, 1993; Beavis et al., 1994; Schön et al., 1994; Groh et al., 1998), and the evidence reported herein, QTL identified for hybrid grain yield would not assure superior inbred per se performance. Thus, selection programs would need to identify and incorporate QTL for both progeny types.

General Discussion
Few studies have compared QTL detection in testcross progeny at early and late generations of inbreeding. Eathington et al. (1997) reported that overall marker effects estimated at S₁ and S_4:5 generations were in reasonable agreement for several traits; however, both generations of testcross progeny were evaluated in the same environments. Herein, testcross progeny from F_2:3 and F_6:8 generations were grown in different environments (same locations, different years) as per actual breeding programs. The resulting QTL effects were not consistent in rank or detection across generations within each of the three testcross populations. Parental contributions, however, were always consistent across generations for common QTL. MTC effects, however, appear to be much less sensitive to these factors as indicated by four grain yield and ten grain moisture MTC QTL detected in both generations. Those MTC QTL include most of the QTL with the largest effects. Grain moisture had a higher number of QTL within each generation than grain yield and displayed a greater consistency of QTL detection across generations, which is likely attributable to its higher h².

Choice of tester seems to be a complex factor in QTL studies involving hybrid progeny and has a major impact on which QTL are identified in a population. Previous studies have revealed little consistency of QTL detection across tester populations, and results appear to be trait dependent and vary with the relationship among testers (Guffy et al., 1988, 1989; Gocken, 1993; Schön et al., 1994; Ajmone-Marson et al., 1995; Lübberstedt, et al., 1997; Kerns et al., 1999). Herein, identification of QTL for one tester did not identify QTL that had effects in another tester (especially for grain yield) nor did it always identify regions with MTC effects. QTL for MTC effects, however, were usually associated with effects for two or more testers. Previous evaluations in this population across diverse environmental conditions suggested that QTL detected in the mean environment are most representative of QTL with consistent or large effects (Veldboom and Lee, 1996a,b; Austin and Lee, 1998, 2000). Therefore, selecting for QTL identified in the mean environment should then confer improved trait performance across a range of environments. Similarly, detecting QTL for MTC effects can be thought of as detecting QTL across "genetic environments." On the basis of results reported herein, selecting for QTL identified in the mean genetic background (MTC) should improve the trait across a diverse array of testers.Austin Lee 1996

	ACKNOWLEDGMENTS

 
The authors thank Paul White for planting and harvesting the plots, Wendy Woodman for assisting with the collection of RFLP data, and Lynn M. Senior for contributing the SSR data.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Journal Paper no. J-17535 of the Iowa Agriculture and Home Economics Experiment Station, Ames. Project no. 3134, and supported by Hatch Act and State of Iowa funds.

Received for publication September 15, 1997.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 

• Ajmone-Marson P., Monfredini G., Ludwig W.F., Melchinger A.E., Franceschini P., Pagnotto G., Motto M. In an elite cross of maize a major quantitative trait locus controls one-fourth of the genetic variation for yield. Theor. Appl. Genet. 1995;90:415-424.
• Austin D.F., Lee M. Comparative mapping in F_2:3 and F_6:7 generations of quantitative trait loci for grain yield and yield components in maize. Theor. Appl. Genet. 1996;92:817-826 a.[ISI]
• Austin D.F., Lee M. Genetic resolution and verification of quantitative trait loci for flowering and plant height with recombinant inbred lines of maize. Genome 1996;39:957-968 b.
• Austin D.F., Lee M. Detection of quantitative trait loci for grain yield and yield components in maize across generations in stress and nonstress environments. Crop Sci. 1998;38:1296-1308.[Abstract/Free Full Text]
• Austin D.F., Lee M. Detection of quantitative trait loci for plant height and flowering in maize across environments and generations. Theor. Appl. Genet. 2000;in press.
• Beavis W.D., Smith O.S., Grant D., Fincher R. Identification of quantitative trait loci using a small sample of topcrossed and F4 progeny from maize. Crop Sci. 1994;34:882-896.[Abstract/Free Full Text]
• Cochran W.G., Cox G.M. Experimental designs. New York: John Wiley & Sons, 1957.
• Coe, E.H., D.A. Hoisington, and M.G. Neuffer. 1990. Linkage map of corn (maize) (Zea mays L.) (2N=20). p. 6.39–6.67. In S.J. O'Brian (ed.) Genetic maps. 5th ed., Cold Spring Harbor Lab., Cold Spring Harbor, NY.
• Coe E., Davis G., McMullen M., Musket T., Polacco M. UMC maize RFLP and genetic working map. Maize Genet. Coop. Newsl. 1995;69:247-256.
• Eathington S.R., Dudley J.W., Rufener G.K. Marker effects estimated from testcrosses of early and late generations of inbreeding in maize. Crop Sci. 1997;37:1679-1685.[Abstract/Free Full Text]
• Gerdes J.T., Tracy W.F., Coors J.G., Geadleman J.L. Compilation of North American maize breeding germplasm. Madison, WI: CSSA, 1993.
• Gocken, T.L. 1993. Identification of quantitative trait loci (QTL) in maize on the basis of F₃ and testcross progeny performance. Ph.D. diss. (Diss. Abstr. 93-21150). Iowa State Univ., Ames.
• Groh S., Khairallah M.M., Gonzalez-De-Leon D., Wilcox M., Jing C., Hoisington D.A., Melchinger A.E. Comparison of QTLs mapped in RILs and their testcross progenies of tropical maize for insect resistance and agronomic traits. Plant Breeding 1998;117:193-202.
• Guffy, R.D., C.W. Stuber, and M.D. Edwards. 1989. Detecting and enhancing heterosis in corn using molecular markers. p. 99–119. Proceedings of the 25th Illinois Corn Breeders School, UIUC, Urbana–Champaign.
• Guffy, R.D., C.W. Stuber, and T. Helentjaris. 1988. Molecular markers for evaluating quantitative traits across varying genetic backgrounds and environments in maize. p. 82. In Agronomy Abstracts, Madison, WI.
• Haley C.S., Knott S.A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 1992;69:315-324.[ISI][Medline]
• Hallauer A.R., Miranda J.B. Quantitative genetics in maize breeding, 2nd ed Ames: Iowa State University Press, 1988.
• Hallauer, A.R., and E. Lopez-Perez. 1979. Comparison among testers for evaluating lines of corn. p. 57–76. In Proc. Annu. Corn and Sorghum Res. Conf., Chicago, IL. 11–13 Dec. American Seed Trade Association, Washington, DC.
• Jansen R.C. Interval mapping of multiple quantitative trait loci. Genetics 1993;135:205-211.[Abstract]
• Jansen R.C., Stam P. High resolution of quantitative traits into multiple loci via interval mapping. Genetics 1994;136:1447-1455.[Abstract]
• Kerns M.R., Dudley J.W., Rufener G.K., II. Tester and type of progeny affect QTL detection in maize. Maydica 1999;44:69-83.
• Knapp S.J., Stroup W.W., Ross W.M. Exact confidence intervals for heritability on a progeny mean basis. Crop Sci. 1985;25:192-194.[Abstract/Free Full Text]
• Lande R., Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 1990;124:743-756.[Abstract]
• Lander E.S., Botstein D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 1989;121:185-199.[Abstract/Free Full Text]
• Lübberstedt T., Melchinger A.E., Schön C.C., Utz H.F., Klein D. QTL Mapping in testcrosses of European flint lines of maize: I. Comparison of different testers for forage yield traits. Crop Sci. 1997;37:921-931.
• Matz E.C., Burr F.A., Burr B. Molecular map based on TXCM and COXTX recombinant inbred families. Maize Genet. Coop. Newsl. 1995;69:257-267.
• Melchinger A.E., Messmer M.M., Lee M., Woodman W.L., Lamkey K.R. Diversity and relationships among U.S. maize inbreds revealed by restriction fragment length polymorphisms. Crop Sci. 1991;31:669-678.[Abstract/Free Full Text]
• Russell W.A. Registration of B70 and B73 parental lines of maize. Crop Sci. 1972;12:721.[Free Full Text]
• Russell W.A. Registration of B90 and B91 parental inbred lines of maize. Crop Sci. 1989;29:1101-1102.[Free Full Text]
• Schön C.C., Melchinger A.E., Boppenmaier J., Brunklaus-Jung E., Herrmann R.G., Seitzer J.F. RFLP mapping in maize: Quantitative trait loci affecting testcross performance of elite European flint lines. Crop Sci. 1994;34:378-389.[Abstract/Free Full Text]
• Senior M.L., Chin E.C.L., Lee M., Smith J.S.C., Stuber C.W. Simple sequence repeat markers developed from maize sequences found in GENBANK Database: Map construction. Crop Sci. 1996;36:1676-1683.[Abstract/Free Full Text]
• Smith O.S. Covariance between line per se and testcross performance. Crop Sci. 1986;26:540-543.[Abstract/Free Full Text]
• Utz, H.F., and A.E. Melchinger. 1996. PLABQTL: A program for composite interval mapping of QTL. JQTL 2:1 (http://probe.nalusda.gov:8000/otherdocs/jqtl/jqtl1996-01/utz.html; verified August 10, 1999).
• Veldboom, L.R. 1994. Genetic analysis of inbred and topcross progeny of an elite, single-cross maize population. Ph.D. diss. (Diss. Abstr. 95-18451). Iowa State Univ., Ames.
• Veldboom L.R., Lee M., Woodman W.L. Molecular marker-facilitated studies in an elite maize population: I. Linkage analysis and determination of QTL for morphological traits. Theor. Appl. Genet. 1994;88:7-16.
• Veldboom L.R., Lee M. Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. Grain yield and yield components. Crop Sci. 1996;36:1310-1319 a.[Abstract/Free Full Text]
• Veldboom L.R., Lee M. Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: II. Plant height and flowering. Crop Sci. 1996;36:1320-1327 b.[Abstract/Free Full Text]
• Zeng Z.B. Precision mapping of quantitative trait loci. Genetics. 1994;136:1457-1468.[Abstract]
• Zuber, M.S., and L.L. Darrah. 1980. 1979 U.S. germplasm base. p. 234–249. In Proc. of the 30th Annu. Corn and Sorghum Ind. Res. Conf., Am. Seed Trade Assoc., Chicago. 9–11 Dec. 1980. Washington, DC.

This article has been cited by other articles:

				J. J. Wassom, J. C. Wong, E. Martinez, J. J. King, J. DeBaene, J. R. Hotchkiss, V. Mikkilineni, M. O. Bohn, and T. R. Rocheford QTL Associated with Maize Kernel Oil, Protein, and Starch Concentrations; Kernel Mass; and Grain Yield in Illinois High Oil x B73 Backcross-Derived Lines Crop Sci., January 16, 2008; 48(1): 243 - 252. [Abstract] [Full Text] [PDF]

				M. Ashikari, H. Sakakibara, S. Lin, T. Yamamoto, T. Takashi, A. Nishimura, E. R. Angeles, Q. Qian, H. Kitano, and M. Matsuoka Cytokinin Oxidase Regulates Rice Grain Production Science, July 29, 2005; 309(5735): 741 - 745. [Abstract] [Full Text] [PDF]

				R. Mihaljevic, C. C. Schon, H. F. Utz, and A. E. Melchinger Correlations and QTL Correspondence between Line Per Se and Testcross Performance for Agronomic Traits in Four Populations of European Maize Crop Sci., January 1, 2005; 45(1): 114 - 122. [Abstract] [Full Text] [PDF]

				M. D. Krakowsky, M. Lee, W. L. Woodman-Clikeman, M. J. Long, and N. Sharopova QTL Mapping of Resistance to Stalk Tunneling by the European Corn Borer in RILs of Maize Population B73 x De811 Crop Sci., January 1, 2004; 44(1): 274 - 282. [Abstract] [Full Text] [PDF]

				J. W. White and G. Hoogenboom Gene-Based Approaches to Crop Simulation: Past Experiences and Future Opportunities Agron. J., January 1, 2003; 95(1): 52 - 64. [Abstract] [Full Text] [PDF]

				Y. Fracheboud, J-M. Ribaut, M. Vargas, R. Messmer, and P. Stamp Identification of quantitative trait loci for cold-tolerance of photosynthesis in maize (Zea mays L.) J. Exp. Bot., September 1, 2002; 53(376): 1967 - 1977. [Abstract] [Full Text] [PDF]

				M. D. Krakowsky, M. J. Brinkman, W. L. Woodman-Clikeman, and M. Lee Genetic Components of Resistance to Stalk Tunneling by the European Corn Borer in Maize Crop Sci., July 1, 2002; 42(4): 1309 - 1315. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (20) Citing Articles via Google Scholar Google Scholar Articles by Austin, D. F. Articles by Hallauer, A. R. Search for Related Content PubMed Articles by Austin, D. F. Articles by Hallauer, A. R. Agricola Articles by Austin, D. F. Articles by Hallauer, A. R.