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Published online 22 January 2007
Published in Crop Sci 47:45-57 (2007)
© 2007 Crop Science Society of America
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CROP BREEDING & GENETICS

Genetic Analysis of Corn Kernel Chemical Composition in the Random Mated 7 Generation of the Cross of Generations 70 of IHP x ILP

J. W. Dudleya,*, Darryl Clarkb, Torbert R. Rocheforda and John R. LeDeauxc

a Dep. of Crop Sci., Univ. of Illinois, Urbana, IL 61801
b Emprevita Corp., Lawrence, KS 66046
c Monsanto Co., St. Louis, MO 63167

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


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 APPENDIX
 REFERENCES
 
To identify and characterize quantitative trait loci (QTL) affecting kernel weight and concentrations of protein, oil, and starch in the corn (Zea mays L.) kernel, plants from generations 70 of the Illinois High Protein (IHP) and Illinois Low Protein (ILP) strains, previously developed by divergent selection for kernel protein concentration, were crossed. The cross was random mated (RM) seven generations and selfed twice to develop 500 F1RM7S2 lines. The lines per se were evaluated at three locations with two replications for 2 yr and testcrosses were evaluated at three locations in 1 yr. Genotypes were evaluated using 499 SNP markers on DNA from a bulk of leaf tissue from each line. As the parent plants used to make the original cross were not available for genotyping, previously reported multivariable and modified simple interval mapping (SIM) procedures were used. SIM identified more significant regions for all traits than did single marker analysis. Correlations and signs of QTL effects suggest development of high protein–high starch lines would be difficult but that it should be possible to develop high protein–high oil lines with minimal effects on kernel weight. The identification of a large number of QTL (at least 40 each for oil, protein, starch, and kernel weight) with small effects agrees in general with earlier estimates based on quantitative genetic theory and has implications for breeding strategies for improved corn kernel quality traits.

Abbreviations: IHO, Illinois High Oil • IHP, Illinois High Protein • ILO, Illinois Low Oil • ILP, Illinois Low Protein • QTL, quantitative trait loci • RM, random mated • SIM, simple interval mapping • SMA, single marker analysis


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 APPENDIX
 REFERENCES
 
THIS MANUSCRIPT is one of a series (Dudley et al., 2004; Laurie et al., 2004; Clark et al., 2006; Willmot et al., 2006) examining the genetic architecture of protein, oil, and starch concentration and kernel weight in the corn kernel. Progenies studied were derived from crosses between strains divergently selected for either protein (IHP and ILP) or oil (IHO and ILO) concentration in the corn kernel. Uniquely, in this series of papers random-mating has been used as a tool to identify molecular markers closely linked to QTL controlling chemical composition. Previous work utilizing a Design III mating design demonstrated that random-mating reduced additive genetic variance for oil in the cross of IHO x ILO (Moreno-Gonzalez et al., 1975) and for protein in the cross of IHP x ILP (Dudley, 1994) in accordance with quantitative genetic theory which suggests that for alleles linked in coupling phase disequilibrium random-mating will reduce additive genetic variance (Comstock and Robinson, 1948). A reduction in number of significant marker associated effects for protein as a result of random mating for four generations in the IHP x ILP cross was demonstrated by Dudley et al. (2004). Similar results were obtained for oil in the IHO x ILO cross by Willmot et al. (2006). These results support the concept that divergent selection built up coupling phase linkages for alleles which impart high protein in the IHP strain and high oil in IHO and coupling phase linkages for low protein in ILP and low oil in ILO. The papers by Laurie et al. (2004) and Clark et al. (2006) both dealt with the RM10 generation of the cross of IHO x ILO using a large number of markers and a large number of families to identify QTL. Their results suggest that at least 40 to 50 QTL control oil, protein, or starch concentration in the IHO x ILO cross. Interestingly, nearly as many QTL were identified for starch or protein in the IHO x ILO cross as were identified for oil even though selection had been only for divergence in oil concentration in the parents.

In addition to basic genetic information, more precise identification of QTL for chemical composition should allow for more efficient development of high oil, high protein, or high starch hybrids. The more precise identification of QTL will also be useful in positional cloning efforts for genes underlying quantitative control of kernel composition, using the forthcoming maize genome sequence.

High oil hybrids have value for improved feeding quality for swine (Lambert, 1994), high protein hybrids offer potential as improved feed for ruminant animals, while high starch hybrids may be useful for increased efficiency of ethanol production.

The objectives of this paper are to present results from a study using 499 SNP markers and 500 lines from the RM7S2 generation of the cross between generations 70 of IHP and ILP; to determine the genetic control of kernel weight and concentrations of protein, oil, and starch in this cross; to determine the genetic relationships among these traits; to compare the results from this cross with those of Clark et al. (2006) from the IHO x ILO cross; and to discuss the implications of these results to breeding for enhanced chemical composition.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 APPENDIX
 REFERENCES
 
Genetic Materials
In 1971, a reciprocal cross was made between cycle 70 of IHP and ILP using a sample of five to seven plants from each strain. Progeny were subsequently intermated at random for seven generations with a breeding population size of approximately 200 (100 male and 100 female parents). About 1500 random individuals were then self-mated two generations without selection to produce 500 lines per se, each represented by an S2 ear. About 50 kernels from each S2 line were planted adjacent to a commercial inbred tester (Monsanto 7051) and two types of crosses made. In one, silks of the tester were fertilized with pollen from the S2 plants to produce 500 testcross progeny. In the other, the S2 plants were sib-mated within a row to produce seed required for S2 line per se evaluation. Thus two types of progeny were developed; the lines per se (represented by a mating structure of F1RM7S2), and corresponding testcrosses.

Molecular Marker Data
For each S2 line DNA was extracted from seedling tissue germinated from a bulk of about 50 seeds and SNP genotypes obtained using procedures described by Laurie et al. (2004). This provided genotypes of the preceding progenies (F1RM7S1). All SNP markers were biallelic. Markers were chosen largely on the basis of allele frequency difference between IHP and ILP (henceforth IHP and ILP refer to cycle 70), as estimated by genotyping a random sample of 24 individuals each from IHP and ILP in the same manner as described by Laurie et al. (2004) for IHO and ILO. Loci were coded uniformly so as to assign the same designation (e.g., "A" as opposed to "a") to alleles with higher frequency in IHP. Marker order was assumed to be identical with results observed from a composite map involving these SNPs constructed proprietarily by Monsanto. Data from 499 segregating SNP markers genotyped on 500 lines per se were used for the analysis reported herein.

Phenotypic Data
Kernel phenotypes were measured on both lines per se and testcrosses. The lines per se were grown for 2 yr (2002 and 2003) at three locations with two replications each. The testcrosses were grown only in 2003 at three locations with two replications each. The locations were Monmouth, IL, Urbana, IL, and Ames, IA, for both lines per se and testcrosses. Generalized lattice [{alpha} (0,1)] experimental designs (Patterson and Williams, 1976) were used for both lines per se and testcrosses. Plots consisted of 15 plants in a row 5.3 m long with 0.76 m between rows. A replicate consisted of 50 blocks of 10 rows per block, with one row per line. Because protein concentration is controlled by the sporophyte (Letchworth and Lambert, 1998) and was the trait of primary interest, plants were allowed to open-pollinate. Approximately 10 ears per row were harvested and kernels from each ear bulked for analysis. Kernel weights were measured on kernels dried to uniform moisture and are reported as 100 kernel weights.

An Infratec Grain Analyzer was used by Monsanto Company to analyze whole kernel samples for oil, protein, and starch concentration by near infrared transmittance. The phenotypic data were analyzed using the PROC MIXED algorithm of SAS software (version 9.00, SAS Institute, Inc., Cary, NC, www.sas.com) to estimate components of variance and best linear unbiased predictors for each line. The lines and all environmental factors were considered random. Using variance component estimates, heritabilities and exact confidence intervals were calculated (Nyquist, 1991; Knapp et al., 1985).

Estimates of phenotypic and genotypic correlations were used to measure relationships among traits A method described by Holland (2006) was used to calculate both phenotypic and genotypic correlations and standard errors. In this method trait data is analyzed as a repeated measures experiment using SAS (PROC MIXED), with one measure being one trait and a second measure being a second trait.

Single Marker Analysis
Single marker analysis (SMA) was performed for the kernel quality traits and the results used for comparison with SIM and with results from Clark et al. (2006). SMA consisted of linear regression of marker genotypes on marker class phenotypic means on a per marker basis using SAS (Kearsey and Hyne, 1994). Markers with a P value ≤ 0.01 and a minimum of 20 individuals in each homozygous marker class were considered significant.

Simple Interval Mapping
Because IHP and ILP are heterogeneous strains and because allelic frequencies in the parents used to make the original cross were unknown the multivariate optimization procedure described by Clark et al. (2006) was used to estimate allelic frequencies in IHP and ILP as well as gametic linkage disequilibrium and recombination frequency among adjacent loci for each parent strain. In this procedure, an iterative process is used to estimate the above parameters and to obtain a linkage map. This map was then used to do SIM using an extension of SIM theory (Lander and Botstein, 1989) as described by Clark et al. (2006). Permutation analysis (Churchill and Doerge, 1994) was used to ascertain significance of QTL. For each locus, the estimates characterizing the bilocus model (parental allele frequencies, linkage disequilibrium and recombination fraction between markers) corresponding to the maximum observed LOD score were used. Phenotypic data were randomized and a new LOD score calculated. The process was repeated to generate a distribution of 1001 such random LOD scores. An observed LOD score was considered significant if it exceeded the 1000th highest value in its corresponding random distribution. This corresponds to an alpha value of 0.001. Where a series of adjacent significant loci with effects having the same sign was observed, a single QTL was assumed to be present at the point in the group for which the observed LOD score exceeded its permutation threshold LOD score the most.

For each locus tested, the parameter set giving the highest LOD score was used to estimate additive and dominance effects in the lines per se and additive effects for the testcrosses. For lines per se, the QTL additive effect was taken to be half the phenotypic difference between QTL homozygote values. A dominance effect was measured as the deviation of the QTL heterozygote value from the mean of the QTL homozygote values. For testcross progenies, the additive effect was measured as the phenotypic difference between QTL homozygote values.

Comparisons among Methods of Analysis
Results from SIM were compared to results from SMA. For comparative purposes, regions were identified by SIM where there was a gap of at least 10 cM between significant intervals. Likewise, regions were identified by SMA where there was a gap of at least 10 cM (using the Monsanto composite map) between significant markers. When a marker identified by SMA as significant was included in a SIM region the region was considered as identified by both SIM and SMA. Results are reported as number of SIM regions, number of SMA regions and number of regions significant by both SIM and SMA analysis. In some cases, two or more adjacent markers declared significant by SMA were included in the same SIM region. In such cases, only one region was considered as having been identified by both SIM and SMA.

Comparisons between Traits and Types of Families
Regions controlling pairs of traits were identified using the results of the SIM analysis at the 0.001 probability level. As for comparisons between SMA and SIM, regions were identified as having significant QTL for pairs of traits and the number of regions containing QTL affecting a pair of traits determined. In a similar manner, the number of regions having significant QTL in both per se and testcross progenies was determined.

Comparison of IHP x ILP Results with IHO x ILO Results
Significant SMA effects found in the IHP x ILP cross in this study were compared with the results from the IHO x ILO cross reported by Clark et al. (2006). For this comparison, significant marker-associated effects found in the IHP x ILP cross were assigned to regions where there was at least a 10-cM gap between significant markers based on the Monsanto composite map. Markers significant in the IHO x ILO cross were similarly assigned to regions and the number of regions significant in each cross and in both crosses determined. Results of the SIM analyses were not compared because of the difficulty of correlating the IHO x ILO and IHP x ILP maps used in the SIM analysis.


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 APPENDIX
 REFERENCES
 
Means, Genetic Variances, and Heritabilities
In agreement with results from the RM1 and RM5 of this same cross (Dudley et al., 2004), mean protein concentration of the lines per se was significantly higher then the mean for the testcrosses (Table 1). In addition, the range of means for lines per se was approximately three times that for the testcrosses. Mean oil concentration was similar for lines and testcrosses while starch concentration and kernel weight were greater for the testcrosses. For all traits, the range for lines per se was greater than for the testcrosses. Coefficients of variation were generally low, particularly for starch, and slightly higher for lines per se than for testcrosses for all traits (Table 1).


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  		Table 1. Means (± 1 SE), ranges, and coefficients of variation (CV) for protein, oil, starch, and 100 kernel weight in per se (PS) and testcross (TC) progenies.

 
Genetic variances (the line component in Table 2) were 6.8 times as high for lines per se as for testcrosses for protein, 3.3 times as high for oil, 4.9 times as high for starch and 1.9 times as high for kernel weight. The increased genetic variance for lines per se relative to testcrosses agrees with the RM1 and RM5 generation results from IHP x ILP (Dudley et al., 2004). Although estimates are not directly comparable because of differing numbers of progenies and environments, the genetic variance estimates for protein, as expected based on theory (Moreno-Gonzalez et al., 1975; Dudley, 1994; Dudley et al., 2004), from the RM7 in this study were less than those from the RM1 reported by Dudley et al. (2004).


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  		Table 2. Estimated components of variance for protein, oil, starch, and 100 kernel weight in per se (PS) and testcross (TC) progenies.

 
Estimates of heritability on a line mean basis (Table 3) were relatively high for all traits suggesting the data were adequate for QTL identification. For all traits, the upper limit of the confidence interval for the testcross heritability was below the lower limit for the per se heritabilities even though CVs for the testcross data (Table 1) were generally slightly lower than for the per se data. This result may have been due to the availability of only three environments for the testcross data as opposed to six for the per se data and to the lower estimates of genetic variance in the testcrosses.


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  		Table 3. Heritability estimates, on a line mean basis, and 95% confidence intervals for protein, oil, starch, and 100 kernel weight in per se (PS) and testcross (TC) progenies.

 
QTL Analysis Results
Mapping Results for SIM
The molecular markers used were chosen based on a large frequency difference between a sample of IHP and ILP plants. The observed mean interstrain marker frequency difference was 0.89. By contrast the mean frequency difference estimated by the optimization procedure was 0.54. The lower estimate from the optimization procedure agrees with results of Clark et al. (2006) for the cross of IHO x ILO and is likely a result of the fact that the sample of plants used to estimate marker frequency was not the same as the plants used to make the original cross. In addition, inadvertent selection or drift during random-mating or selfing may have played a role. However, a sample of approximately 200 plants was used during the random-mating generations and 500 lines were developed by selfing. Thus drift should have been minimal.

The correlation between interval distances for the composite map and the estimated map was 0.53 which is significant at the 0.001 level indicating relatively good agreement between the Monsanto composite map and the map developed from the optimization procedure. However the total map lengths differ. As in the IHO x ILO cross reported by Clark et al. (2006) the estimated map length of 4804 cM was much greater than the map length (1533 cM) determined from the Monsanto composite map. Clark et al. (2006) listed a number of factors which might increase the estimated map length in a random mated population. These factors included a much larger number of families in the RM7S2 (500 as opposed to approximately 160 in the composite map) which would lead to a higher probability of detecting recombination between closely linked loci. The seven generations of random mating (as opposed to no random mating in the composite map) would also allow for a higher probability of detecting recombinants. In addition, even a very small error rate in genotyping of samples for so many markers could lead to a large increase in map distance (Lincoln and Lander, 1992). An error in marker order would also lead to inflation of erroneous intervals and therefore the overall map. All these factors could lead to an increased estimate of map length.

Interval Mapping Results
Appendix tables A1GoGo through A4 contain a detailed summary of SIM results for protein, oil, starch, and kernel weight. QTL locations (points) listed are those at which the LOD+ values (the amount an observed LOD score exceeds the permutation generated LOD value at the 0.001 probability level) are positive for either per se or testcross results and which are maximum if located in an adjacent set of points which all had positive LOD+ values. Because not all points are significant for both progeny types, the tables include some negative LOD+ values. Where these occur, the points were included because they are significant for the other progeny type.


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  		Table A1. Protein quantitative trait loci (QTL) found to be significant in per se and/or testcross lines by simple interval mapping. "Marker no." indicates the marker nearest the QTL, and "Marker position" gives a marker's genetic map position in centimorgans. "QTL distance" is the QTL distance relative to the nearest marker in centimorgans. A negative value indicates the QTL is toward the beginning of the chromosome. "LOD+" refers to the amount an observed LOD score exceeds the permutation generated LOD threshold value. "a" and "d" are the estimated additive and dominance effects, respectively.

 

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  		Table A2. Oil quantitative trait loci (QTL) found to be significant in per se and/or testcross lines by simple interval mapping. "Marker no." indicates the marker nearest the QTL, and "Marker position" gives a marker's genetic map position in centimorgans. "QTL distance" is the QTL distance relative to the nearest marker in centimorgans. A negative value indicates the QTL is toward the beginning of the chromosome. "LOD+" refers to the amount an observed LOD score exceeds the permutation generated LOD threshold value. "a" and "d" are the estimated additive and dominance effects, respectively.

 

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  		Table A3. Starch quantitative trait loci (QTL) found to be significant in per se and/or testcross lines by simple interval mapping. "Marker no." indicates the marker nearest the QTL, and "Marker position" gives a marker's genetic map position in centimorgans. "QTL distance" is the QTL distance relative to the nearest marker in centimorgans. A negative value indicates the QTL is toward the beginning of the chromosome. "LOD+" refers to the amount an observed LOD score exceeds the permutation generated LOD threshold value. "a" and "d" are the estimated additive and dominance effects, respectively.

 

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  		Table A4. Kernel weight quantitative trait loci (QTL) found to be significant in per se and/or testcross lines by simple interval mapping. "Marker no." indicates the marker nearest the QTL, and "Marker position" gives a marker's genetic map position in centimorgans. "QTL distance" is the QTL distance relative to the nearest marker in centimorgans. A negative value indicates the QTL is toward the beginning of the chromosome. "LOD+" refers to the amount an observed LOD score exceeds the permutation generated LOD threshold value. "a" and "d" are the estimated additive and dominance effects, respectively.

 
Because in several cases the points listed were very closely linked, the significant points were grouped into regions by considering all significant points not separated by at least 10 cM (on our map) as being in one region. Thus while for protein in the per se progenies there are 96 significant points (Table A1), these were grouped into 50 significant regions (Table 4). Similarly for protein in the testcross progenies 88 significant points were grouped into 41 regions.


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  		Table 4. Number of regions identified by SIM for per se and testcross progenies and for both.

 
Except for kernel weight, the number of regions identified as significant in the per se progenies was slightly higher than the number identified in the testcross progenies (Table 4). The number of regions identified for kernel weight in both per se and testcross progenies was similar to the number of regions identified for protein in the testcross progenies and slightly lower than the numbers for the three quality traits in the per se progenies.

For all traits, the number of regions identified by both the per se and testcross progenies was approximately half the number identified in the per se progenies (Table 4). Thus the choice of per se or testcross progenies for identifying QTL depends on the objective of identifying QTL. If interest is in hybrid performance, then use of testcross progenies is indicated. This would have the further advantage of allowing for meaningful yield tests and identifying those QTL alleles favorable for both quality traits and grain yield.

The minimum number of regions identified as significant for protein, oil, or starch was 40 and significant regions were identified on all 10 chromosomes for all three quality traits. Thus these results agree with those of Laurie et al. (2004) and Clark et al. (2006) in demonstrating the quantitative nature of genetic control of protein, oil, and starch in the corn kernel. The large number of QTL identified is likely a result of the relatively large number of lines tested. Schon et al. (2004) showed that as the number of progeny tested increased the number of QTL identified increased. The estimates of number of QTL in this study and those of Laurie et al. (2004) and Clark et al. (2006) also agree reasonably well with the estimates, based on quantitative genetic theory, of number of effective factors controlling oil and protein reported by Dudley (1977). These numbers should be considered minimal estimates of the number of regions differentiating IHP and ILP because of the small number of parents (5–7) sampled from each strain.

SMA vs. SIM Analysis
Results of the SMA analysis are shown in Fig. 1 . For oil, starch, and kernel weight in the per se progenies, and for kernel weight in the testcross progenies, at least three-fourths of the regions identified by SMA were also identified by SIM analysis (Table 5). Approximately two-thirds of the regions identified as significant for protein, oil, and starch in the testcross analysis and for oil in the per se analysis were also significant in the SIM analysis. In all cases, SIM analysis identified more significant regions than the SMA analysis suggesting, in agreement with Clark et al. (2006) that SIM analysis was more powerful than SMA analysis.


Figure 1
Figure 1
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  		Fig. 1. A genetic map with markers found significant by single marker analysis for protein, oil, and starch concentration and kernel weight. Markers are indicated by number. Capital letters indicate the trait(s) significantly associated with each marker (P≤0.01). Letters to the left of each marker indicate significance in per se lines whereas letters to the right indicate significance in testcross lines.

 

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  		Table 5. Number of regions identified by simple interval mapping (SIM) and single marker analysis (SMA) for per se and testcross progenies.

 
Even though the parent strains were divergently selected for protein concentration and thus might be expected to differ by more QTL for protein than the other traits measured, SIM analysis identified more significant points and regions for oil and starch in both per se and testcross progenies than for protein (Tables A1, A2, A3, and 4). These results differed from those of the SMA where approximately three-fourths as many regions were significant at the 0.01 level for oil as for protein or starch in the per se progenies and approximately two-thirds as many regions were significant for the testcross progenies (Table 5).

Relationships among Traits
The genetic relationships between pairs of traits were measured by genotypic correlations and by the number of SIM regions containing significant QTL for pairs of traits, Genotypic correlations of protein with starch were –0.92 for the per se and –0.91 for the testcross progenies (Table 6). In addition, 40 of 50 regions significant for protein contained significant QTL for starch in the per se progenies and 36 of 41 regions significant for protein in the testcross progenies contained significant QTL for starch (Tables 4 and 7). Thus, more than 80% of the significant regions for protein were also significant for starch. In contrast, the genotypic correlation of protein with oil was not significant for the lines per se but was significant, although small (0.41 ± 0.05) for the testcross progenies (Table 6). Only about one-half of the regions having significant oil QTL had significant QTL for protein (Tables 4 and 7). Genetic correlations of starch with oil were negative and intermediate to those of protein with starch and protein with oil. Genetic correlations of kernel weight with the three chemical constituents were small and their signs were inconsistent between the per se and testcross progenies (Table 6). Less than half the regions showing significant QTL for oil, starch, or protein contained significant QTL for kernel weight (Tables 4 and 7).


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  		Table 6. Genotypic (above diagonal) and phenotypic (below diagonal) correlations (±1 SE) among kernel quality traits in per se (PS) and testcross (TC) progenies.

 

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  		Table 7. Number of SIM regions in common between traits. Per se above diagonal, testcross below diagonal.

 
The results of the genetic correlation analysis and the grouping of significant QTL for starch, oil, or kernel weight into regions containing significant QTL for protein suggest that selection for protein in the IHP x ILP population would lead to reduced starch, little change in oil, and little change in kernel weight.

Comparison of the IHP x ILP Results with the IHO x ILO Results
Previously we reported results of evaluation of the cross of generations 70 of IHO and ILO (Clark et al., 2006). In that study, 479 SNP markers and 499 families were used. The major difference between the studies is that the parents in this study were the result of 70 generations of divergent selection for protein whereas the parents of the previous study were the result of 70 generations of divergent selection for oil.

As expected, the genetic variance for oil in the IHO x ILO cross was much greater than in the IHP x ILP cross (Table 8) for both per se and testcross progenies. Likewise, the genetic variance for protein in the IHP x ILP cross was much greater than in the IHO x ILO cross for both types of progenies. The genetic variance for starch was greater in the IHO x ILO cross. The mean oil values for IHO and ILO in generation 70 were 166.4 and 4.0 g kg–1 (data not shown), respectively, while the mean protein values were 142 and 118 g kg–1. In contrast, the mean protein values for IHP and ILP were 266 and 44 g kg–1 and mean oil values were 48.2 and 31.0 g kg–1. In general, the differences in genetic variance between the IHO x ILO cross and the IHP x ILP cross for both per se and testcross progenies parallel the differences in oil and protein between the parent strains of the two crosses. In both crosses and for all traits, the genetic variance in the testcrosses was drastically less than in the lines per se as expected by theory (see Bernardo, 2002: p. 92, 104) for a discussion of population and testcross genetic variances) and in agreement with Dudley et al. (2004) and Willmot et al. (2006).


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  		Table 8. Comparison of IHP x ILP data with IHO x ILO data.

 
For protein, the number of significant regions was greater for the IHP x ILP cross (Table 9) than for the IHO x ILO cross for both the per se and testcross progenies. Surprisingly, for oil the number of significant regions found in the IHP x ILP cross was similar to that for the IHO x ILO cross. Because the parents of the IHO x ILO cross were selected for divergence in oil and the IHP x ILP parents were selected for divergence in protein, more significant oil QTL were expected in the IHO x ILO cross.


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  		Table 9. Number of significant SMA regions from the IHO x ILO cross, the IHP x ILP cross, and regions in common for traits measured on the per se and testcross families.

 
At first glance, the similar numbers of significant regions for oil in the IHP x ILP cross as in the IHO x ILO cross is surprising, The genetic variance for oil in the IHP x ILP cross is only about 1/10 that in the IHO x ILO cross but the average absolute value of significant marker associated effects for oil is much lower in the IHP x ILP cross (Table 8). Thus the IHP x ILP data allowed detection of QTL with smaller average effects than the IHO x ILO data. The CV for oil in the IHP x ILP cross was not much different than for the IHO x ILO cross (Table 8). However, the average error for testing the significance of a marker associated effect (the among families within genotypic classes mean square) is much lower for both the per se and testcross data in the IHP x ILP cross than in IHO x ILO (Table 8). The among families within genotypic classes mean square includes the genetic variance not accounted for by the marker associated effect. Because the genetic variance for oil is much smaller in the IHP x ILP cross, the error mean square for testing significant effects for oil is smaller than in the IHO x ILO cross. Thus it was possible to detect smaller effects for oil in IHP x ILP than in IHO x ILO. For protein, the same type of result was observed in that the average error mean square for markers in IHO x ILO was smaller than in IHP x ILP and the average effect for significant markers in IHO x ILO was approximately half that in IHP x ILP. However, the number of significant regions (Table 9) was higher in IHP x ILP because the genetic variance in IHO x ILO was not reduced as much relatively as it was for oil in IHP x ILP.

The larger effects for oil in the IHO x ILO cross and the larger effects for protein in the IHP x ILP cross may have resulted from the build up of tight coupling phase linkages, which were not broken up by random mating, as a result of selection in the parental strains.

Approximately one third of the regions significant for protein in the IHP x ILP cross were also identified in the IHO x ILO cross (Table 9). Similarly, over one third of the regions identified as significant for oil in the IHO x ILO cross were identified as significant in the IHP x ILP cross. Thus divergent selection for protein allowed identification of significant regions not identified in the IHO x ILO cross. For starch, a much higher percentage of regions significant in the IHO x ILO cross were also significant in the IHP x ILP cross.


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
APPENDIX
 REFERENCES
 
A major finding of this study is that kernel weight and concentration of protein, oil, and starch in the corn kernel are controlled by a relative large number of QTL. This finding is in agreement with the QTL results of Laurie et al. (2004) and Clark et al. (2006) and with earlier quantitative genetic estimates of number of effective factors (Dudley, 1977). This result suggests that altering these components by transgenic approaches may be a major challenge. The presence of large numbers of QTL controlling a trait may be considered a barrier to marker assisted selection (MAS) approaches. However, the crosses of IHO x ILO and IHP x ILP are crosses in which the maximum numbers of QTL are expected to be segregating. In a practical breeding program, the number of QTL for which the parents differ will be limited and concentration on those QTL should make MAS selection a potentially useful approach.

The finding of a similar number of significant regions for oil in the IHO x ILO cross, where selection was for oil and marker associated effects were large, as in the IHP x ILP cross where selection was for protein and marker associated effects were small, resulted from reduced genetic variance in the IHP x ILP cross. This suggests that small marker associated effects may be found significant in segregating populations where genetic variance is relatively small. This provides hope for use of MAS approaches in crosses where genetic variance is limited.


   APPENDIX
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 APPENDIX
REFERENCES
 


   ACKNOWLEDGMENTS
 
We gratefully acknowledge the assistance of Cathy Laurie, Scott Chasalow, and Lyle Crossland for their input during the development and execution of this project. We thank Donald Roberts and Randy Rich for valuable technical assistance.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 APPENDIX
 REFERENCES
 
This research was supported by the Illinois AES and a grant from Renessen, LLC.

Received for publication March 31, 2006.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 APPENDIX
 REFERENCES
 




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J. W. Dudley
Epistatic Interactions in Crosses of Illinois High Oil x Illinois Low Oil and of Illinois High Protein x Illinois Low Protein Corn Strains
Crop Sci., January 16, 2008; 48(1): 59 - 68.
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J.W. Dudley
From Means to QTL: The Illinois Long-Term Selection Experiment as a Case Study in Quantitative Genetics
Crop Sci., December 18, 2007; 47(Supplement_3): S-20 - S-31.
[Abstract] [Full Text] [PDF]

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