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CROP BREEDING, GENETICS & CYTOLOGY

Comparison of Phenotypic and Marker-Assisted Selection for Quantitative Traits in Sweet Corn

Dep. of Natural Resources and Environmental Sciences, Univ. of Illinois at Urbana-Champaign, 307 ERML, 1201 W. Gregory Dr., Urbana, IL 61801

* Corresponding author (j-juvik{at}uiuc.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
This investigation was designed to empirically compare the efficiency of marker-assisted selection (MAS) and phenotypic selection (PS) in enhancing economically important quantitative traits in sweet corn (Zea mays L.). Marker-assisted selection and PS were applied to three F_2:3 base populations (C₀) with either the sugary1 (su1), sugary enhancer1 (se1), or shrunken2 (sh2) endosperm mutations. One cycle of selection was applied for both single and multiple traits including seedling emergence, kernel sucrose concentration, kernel tenderness, and hedonic rating (taste panel preference). Twenty percent of the families in each of the base populations were selected and intermated to constitute MAS- and PS-based C₁ composite populations. Selection efficiencies were evaluated on the basis of gains over one cycle and estimated evaluation costs. A total of 52 paired comparisons were made between MAS and PS composite populations. In 38% of the paired comparisons, MAS resulted in significantly higher gain than PS across the three C₁ composite populations, while PS was significantly greater in only 4% of the cases. The average MAS and PS gain across all composite populations and selected traits, calculated as percent increase or decrease from the randomly selected controls, was 10.9% and 6.1%, respectively. Use of MAS is most appropriate when traits are difficult and costly to measure. However, for some traits, the higher gain from MAS can compensate for the higher costs of MAS. It was concluded that incorporating DNA markers to traditional breeding programs could expedite selection progress and be cost-effective.

Abbreviations: DAP, days after pollination • C₀, base population • C₁, composites created by intermating the selected F_2:3 families using PS or MAS • MAS, marker-assisted selection • PRS, phenotypic recurrent selection • PS, phenotypic selection • QTL, quantitative trait locus/loci • su1, se1, and sh2 are sugary1, sugary enhancer 1, and shrunken2 endosperm mutations in maize (Zea mays L.) • RFLP, restriction fragment length polymorphism

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
USING LINKAGE MAPS constructed with DNA marker loci, it is possible to estimate the location and effect of individual loci influencing the expression of quantitative traits (Breto et al., 1994; Danesh et al., 1994; Xiao et al., 1996). Statistical associations between alleles at marker loci and alleles at linked quantitative trait loci (QTL) can be used to select indirectly, but with potentially high accuracy, for favorable QTL alleles, effectively increasing the heritability of traits (Soller and Beckmann, 1983; Stuber et al., 1992). While many studies have been conducted to map QTL of important traits in maize (Bernardo, 1999), very few empirical applications of marker-assisted selection (MAS) in breeding programs have been reported. Most studies employing MAS have been theoretical, using computer simulations (Lande and Thompson, 1990; Zhang and Smith, 1992; Edwards and Page, 1994; Gimelfarb and Lande, 1994), the results of which suggest that MAS can be used to improve quantitative traits in animal- and plant-breeding programs.

In theory, MAS is proposed to be more efficient than phenotypic selection (PS) when the heritability of a trait is low, where there is tight linkage between QTL and DNA markers (Dudley, 1993; Knapp, 1998), with larger population sizes (Moreau et al., 1998), and in earlier generations of selection before recombinational erosion of marker-QTL associations (Lee, 1995). Edwards and Page (1994) reported, using computer simulation, that MAS produced rapid gains early in the selection process compared with phenotypic recurrent selection (PRS); however the rate of gain diminished greatly within three to five cycles. They proposed that the distance between markers and QTL was the factor that most limited gains from MAS. Moreau et al. (1998) reported that MAS was ineffective for traits with heritabilities below 20% due to the low power of quantitative trait loci detection and the bias caused by the selection of markers. Knapp (1998) proposed that MAS would increase the probability of selecting superior genotypes and substantially decrease the resources required in breeding for a trait with low to moderate heritability.

A limited number of empirical experiments have provided equivocal results regarding the relative efficiency of MAS and PS. Using S_4:5 maize families selected by an index based on marker effects in F₂ testcrosses or phenotypic data, MAS was found as effective as PS, but neither method identified lines better than the original hybrid (Stromberg et al., 1994). Suggested as reasons for the poor selection ability of MAS were different testing environments for the F₂ and S_4:5 testcrosses, limited coverage of the genome with markers, and small population size (20 S_4:5 families). Indices combining phenotypic and marker information were successful in selecting superior S_4:5 lines for grain yield and stalk lodging in maize compared with phenotypic performance alone (Eathington et al., 1997). However, they reported that marker information was able to improve selection gain over phenotypic data only in high-yielding environments. Comparable selection gain from MAS and PS was reported in improving flowering time of Arabidopsis, which was attributed to the high heritability of the trait (Van Berloo and Stam, 1999). Zhu et al. (1999) reported significant effects of most QTL selected using MAS in a barley-breeding program for enhanced grain yield, although progress was hindered by large QTL x environment interactions. These reports suggest that further empirical investigations are required to evaluate the merits of MAS as an adjunct to PS or as an exclusive selection procedure for rapid selection during crop-breeding programs. This is particularly true when the objective is to select for several traits simultaneously, requiring the manipulation of gene frequencies at numerous loci across the genome.

In sweet corn, breeding for improved seedling emergence and eating quality is complicated because of the relationship between these traits. High kernel sugar concentration was proposed as one of the reasons for poor seedling emergence (Douglass et al., 1993). These traits are influenced by many kernel characteristics and under the control of many genes (Azanza et al., 1996b). However, since field emergence and eating quality depend on kernel chemical and physiological characteristics, selection for improved seedling emergence without concurrent attention to the fresh quality of the harvested product will be of limited value to the sweet corn industry. In this study, we compared MAS and PS for the simultaneous improvement of seedling emergence and eating-quality characteristics.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Development of the Composite Populations (C₁)
Two sweet corn F_2:3 populations previously developed from crosses between inbreds provided the base populations and genetic information for this study. One of the populations, homozygous for sugary1 and segregating for sugary enhancer 1 (W6786su1Se1 x IL731asu1se1), consisted of 214 F_2:3 families (Azanza et al., 1996b) and the second homozygous for shrunken2 (IL451b sh2 x Ia453 sh2) of 117 F_2:3 families (Han, 1994). A near-saturated genetic linkage map of 88 RFLPs, three cDNA, and two morphological markers was constructed for the first population (Tadmor et al., 1995). In the second population, 61 polymorphic RFLP markers were used to generate a second linkage map (Han, 1994). Key alleles affecting seedling emergence, kernel eating quality, and other traits were identified and mapped in both populations (Han, 1994; Tadmor et al., 1995; Azanza et al., 1996b).

The 214 F_2:3 families in the first population were classified into three subpopulations according to segregation for se1, where families were either homozygous for Se1 (su1su1Se1Se1, sugary1), heterozygous for Se1 (su1su1Se1se1), or homozygous for se1 (su1su1se1se1, sugary enhancer 1). This classification was made to standardize the genetic background of each subpopulation regarding se1 since this gene is known to exert major epistatic influences on kernel characteristics and seedling emergence (Azanza, 1994; Wang, 1997). In this population, 38 of the F_2:3 families were homozygous for Se1 (su1su1Se1Se1) and 48 F_2:3 families homozygous for the mutant allele (su1su1se1se1). These two subpopulations were used as two base populations. In the second population, all 117 F_2:3 families were homozygous for sh2 and used as the third base population in this study. Selection using MAS and PS was applied to these three base populations separately.

Selection for single traits was applied for seedling emergence, kernel sucrose concentration at 20 d after pollination (20 DAP), and cooked kernel tenderness (20 DAP) in the su1Se1 and su1se1 populations while selection was applied only for seedling emergence in the sh2 population. Multiple trait selection using MAS and PS was applied for simultaneous improvement of (i) emergence and tenderness, (ii) emergence and hedonic rating, and (iii) emergence, sucrose, and tenderness in the su1Se1 and su1se1 populations. In the sh2 population, selection for two traits was conducted only for emergence and tenderness. The breeding scheme used to develop the C₁ composites of single and multiple traits based on PS or MAS is presented in Fig. 1.

View larger version (88K): [in this window] [in a new window]   Fig. 1. Breeding scheme for the development of the C1 MAS and PS composites in each of the three base populations (su1Se1, su1se1, and sh2).

In each population, families were sorted based on their phenotypic (PS) or genotypic values (MAS). Then 20% of the families with the highest and lowest values for each trait were chosen. Those families were used to generate C₁ composites of high and low phenotypic or genotypic performance. Q-gene software (Nelson, 1996) was used to provide a list of the three genotype class means for each marker, probability values, and estimates of gene action at each associated RFLP marker in the three base populations.

For multiple-trait selection, traits were considered of equal economic value in the selection index. For each trait, phenotypic or genotypic values of each family were standardized into Z values using the formula:

The family Z score indicates where an individual family's performance lies in relation to the population mean in units of standard deviation. The selection index was calculated by summing the standardized phenotypic or genotypic values of families for each trait resulting in a PS and MAS index, respectively. Families in each population were then sorted on the basis of their PS or MAS index scores. Twenty percent of the families with the highest and lowest index scores were selected to constitute the C₁ composites of high and low performance for multiple traits.

The number of selected F_2:3 families (20%) was 8, 10, and 23 from the su1Se1, su1se1, and sh2 base populations, respectively, and these were used to constitute C₁ composites for single and multiple traits. On average, MAS and PS paired composites were composed of 43% of the same F_2:3 families. The percentage of composite composition of shared families ranged from 25 to 60%. From each base population, 50% of the families were randomly selected to establish a control or C₀ composite from seed grown in the same environment. A list of the created composites and the marker loci used for MAS are presented in Table 1. The average beneficial allele frequencies in suSe1, su1se1, and sh2 were 51, 51, and 54% in the base populations vs. 48, 52, and 53% in the randomly selected C₀ controls, respectively. The similarity of these allelic frequencies between the base populations and the C₀ controls suggests that drift is not a major factor in the experiment results.

Evaluation of the C₁ Composites
Seedling Emergence
Replicates of 100 kernels of each of the C₀ and C₁ composites were planted in four environments; three in Illinois (1 May, 10 June, and 15 Sept. 1997) and one at the University of Wisconsin (7 June 1997). Kernels were planted using hand planters at about 4-cm soil depth in Drummer silty clay loam soil in Illinois environments and Plano silt loan soil in Wisconsin. The average daily soil and air soil temperature was above 15°C in all plantings, except in the May planting (9°C). The experimental design was a randomized complete block design (RCBD) within each environment. All composites were evaluated for seedling emergence in three replications except for the Fall 1997 planting that had eight replications. Seedling emergence was determined by direct counts at 3 to 4 wk after planting in the four environments.

Eating Quality Evaluations
Eating quality traits, which included kernel sucrose concentration, cooked kernel tenderness, and hedonic rating (taste panel preference), were evaluated on ears harvested from three replicated plots on the South Farm of the University of Illinois at Urbana-Champaign in 1997 and 1998. To minimize unintentional selection due to cold weather effects on sweet corn emergence and plant vigor, plantings were direct seeded when mean daily soil and air temperature exceeded 15°C (10 June 1997 and 15 June 1998). At anthesis, pollen was collected from 25 to 30 plants and mixed. The bulk was used to pollinate 10 random ears within the composite. Pollinated ears were harvested 20 d after pollination (DAP) for evaluation. The harvesting period did not exceed 1 wk; therefore differential accumulation of heat units was not considered an important source of variation in eating quality traits. The harvested ears were promptly placed on dry ice and transported from the field and temporarily stored in a -20°C freezer. To facilitate removing kernels from cobs and to eliminate enzymatic activity, ears were immersed in liquid nitrogen for about 2 min until hard frozen. An equal amount (50 gm) of frozen kernels from each ear within each composite were bulked, sealed in freezer bags, and stored at –80°C for subsequent chemical, physiological, and sensory analysis.

Kernel sucrose concentrations were estimated as milligrams of sucrose per gram freeze-dried tissue using an 80% ethanol extraction procedure described by Juvik and La Bonte (1988). Two samples of 25 g (100 kernels each) of frozen tissue per replication were analyzed since some variation was expected to occur within composites. Extracted corn samples were injected into a high-pressure liquid chromatography, where peak areas were recorded and used to calculate sucrose concentration. Sucrose concentrations of samples were calculated using a simple regression function with a range of eight sucrose standards of known concentrations.

Cooked kernel tenderness was evaluated using a Kramer Shear Press (Instron, Canton, MA), which measures sample texture (Azanza et al., 1996a). Two samples of 30 g per replication were cooked for 4 min in boiling water, air-dried for 1 min, and placed in the rectangular cavity of the machine. The force required to push the multibladed fixture through the cooked samples was estimated in kilograms by measuring the area under the curve recorded on attached chart paper.

Hedonic rating (taste panel preference) evaluation was conducted on the emergence/hedonic C₁ composites and the C₀ controls of both the su1Se1 and su1se1 composites, using test conditions and procedures described by Azanza et al. (1996b). Thirteen panelists were asked to mark a scale ranging from 1 (extreme dislike) to 9 (extreme like), indicating how much in general they preferred the cooked sample as an estimate of overall preference.

Selection Efficiencies of PS and MAS
Selection efficiencies of PS and MAS were compared for selection gain and comparative costs. Selection gain was calculated as percent increase in the C₁ composites of the high direction over the C₀ control, [(C₁ - C₀)/C₀] x 100. In the low direction, selection gain was estimated as percent decrease in the C₁ composites from the C₀ control, [(C₀ - C₁)/C₀] x 100.

Generalized cost comparisons between MAS and PS are difficult to achieve. In this study, the average costs required to evaluate and select a family based on MAS and PS were estimated in the mapping and C₁ populations. These estimates were based on the original su1se1 population size of 214 families with 94 markers for the 65 different traits that were evaluated in this population. An estimated cost of phenotyping and genotyping one family/composite for each trait was obtained and then added to the cost of evaluating the family/composite in the first cycle. In the case of PS, this included initial costs incurred for labor and field supplies and maintenance used in the base population and C₁ evaluation. Emergence evaluations included the initial field costs and actual germination counts. Eating quality evaluations (sucrose, tenderness, and hedonic rating) included, in addition to the initial field costs, labor and laboratory chemicals and supplies. Pay rates were estimated at $15/hour since chemical and sensory evaluations required experienced laborers while the assistance of a sensory technician was estimated at $25/hour. Assuming further selection cycles were to be conducted, the base population costs were divided by the number of cycles. With MAS, the costs included mapping the original population (phenotypic and marker analysis) and screening one family/composite for the number of the selected markers (five RFLP markers) in the C₁ of this study. The original mapping costs were divided among the number of projected cycles. The estimation of marker genotyping costs included greenhouse bench space and labor for sowing seeds, harvesting tissue, and labor for DNA extraction and RFLP analyses. Estimates were based on a pay rate of $15/hour as in PS evaluation since these analyses required experienced laboratory help.

Statistical Analysis
For statistical analysis, data of C₁ composites resulting from single-trait selection and the respective C₀ control were grouped and analyzed as separate data sets. When C₁ composites created based on multiple-trait selection included a trait (for example, emergence), data of that trait among C₁ composites and the respective control were grouped and analyzed as one data set. Each data set contained five composites: two from MAS (high and low), two from PS (high and low), and one C₀ control except with emergence-only selected composites. The number of emergence-only selected composites was seven where another replicate of two MAS composites (high and low) was created using a second set of RFLP markers. The statistical models used to analyze the data sets are presented in Table 2. Analysis of variance was conducted using PROC GLM procedures (SAS, 1991) with factors fixed except replications. Mean comparisons for traits were made on the basis of combined data over environments. The null hypothesis (H_o) was tested at P < 0.05 where composite means were compared using least significant differences (protected LSD).

View this table: [in this window] [in a new window]   Table 2. Mean squares and significance of sources of variation from ANOVA of seedling emergence, kernel sucrose and tenderness, and hedonic rating in composites selected for single or combinations of the four traits in the su1Se1, su1se1, and sh2 populations in Illinois in 1997 and 1998.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

Since the objective of this study was to compare the efficiency of two selection methods and not to evaluate composite performance across environments, the statistical analysis was performed on combined data across environments. Environmental variation in emergence, sucrose, and tenderness tends to agree with previous reports indicating that these traits are highly influenced by environmental conditions (Douglass et al., 1993; Azanza et al., 1996a). Variation associated with hedonic values indicated that a large amount of variation and error was not described by the model. This agrees with earlier work (Azanza et al., 1994) suggesting that sweet corn hedonic rating is a complicated trait controlled by the interaction of kernel sugar content, tenderness, aroma, and other attributes with relatively low heritability.

Comparative Evaluation of MAS and PS
Selection Gain in Single Traits
Seedling emergence of the C₁ composites (EPH, EPL, EMHA, EMLA, EMHB, EMLB) in the emergence-only selected composites and C₀ control in the three populations are presented in Table 3. In the high direction, mean emergence across environments and replications revealed that MAS enhanced seedling emergence significantly in two of three populations compared with PS. The C₁ composites (EMH) showed an increase in emergence over C₀ of 8.3 and 14.9% under MAS in the su1Se1 and su1se1 C₁ composites, respectively. In both su1Se1 and su1se1 C₁ composites, PS did not improve emergence over C₀ controls. Gains from the second set of selected markers for seedling emergence (EMHB and EMLB) showed the same pattern compared with PS in su1Se1, su1se1, and sh2 composites. This provides evidence for the repeatability of gains resulting from MAS when a second and different set of marker QTL was selected.

Kernel sucrose concentrations at 20 DAP in the sucrose-selected composites are presented in Table 3. Results show that both MAS and PS were effective in significantly enhancing kernel sucrose concentration in both composite populations. However, in the high direction of selection, mean sucrose comparisons among the su1Se1 and su1se1 C₁ composites revealed that MAS was superior to PS. In the su1Se1 C₁ composites, MAS improved sucrose concentration by 24.8% (SMH) while with PS gain was 9.7% (SPH). Kernel sucrose concentration was also enhanced in the su1se1 C₁ population by 26.4 and 18.2% over C₀ using MAS and PS, respectively.

Tenderness, measured as the force required to crush cooked kernels, is another important sweet corn eating quality. The less force required, the more tender and preferred the sweet corn is to the consumer (Azanza et al., 1996a). Therefore selection for tenderness was conducted using alleles associated with lower tenderness values. Composites subjected to MAS were significantly more tender (lower values) compared with those based on PS (Table 3). Phenotypic selection for tenderness was relatively ineffective in both the su1Se1 and su1se1 C₁ composites. Selection gain from MAS was 11.8 and 13.6% in su1Se1 and su1se1 in tenderness-selected composites (TMH), respectively.

Selection Gain in Multiple Traits
Selection for multiple traits is much more typical in plant-breeding programs than selection for single traits due to economic returns. In sweet corn breeding, selection for eating quality without concurrent attention to seedling emergence will be of limited commercial value. In this study, multiple-trait selection was practiced for seedling emergence and eating-quality traits to develop germplasm superior for both. Selection gains achieved with PS and MAS when incorporating more than one trait in the selection indices were not consistent across traits or composites. Negative selection gains, in which performance was in a direction opposite to expectation, were observed in a few composites.

Emergence and Tenderness
Interaction among genes in the su1Se1, su1se1, and sh2 C₁ composites, which was reported previously (Wang, 1997), and indirect selection due to a negative correlation between emergence and eating quality, appeared to affect selection gains for both PS and MAS (Table 4). In the sh2 population, with a larger population size, PS for emergence/tenderness did not enhance emergence while MAS increased emergence by 14.2% (ETMH) over the C₀. The C₁ composite (ETMH, sh2), developed through MAS and possessing improved emergence and tenderness, represents a potential source of germplasm for breeding programs designed to simultaneously improve seed and eating quality in sweet corn. In a recent study, three of the marker loci linked to beneficial alleles that enhance emergence in sh2 population were introgressed into three distinct sweet corn genetic backgrounds (Yousef, 2000). Comparable effects in emergence as observed in the F_2:3 population was observed for these alleles in this study, suggesting their effects are not background-specific.

Relative Efficiency and Cost of MAS and PS
A total of 52 paired comparisons were made between MAS and PS C₁ composites in enhancing economic traits in sweet corn (Table 7). In 38% of the paired comparisons, MAS resulted in significantly higher gains than PS across the three C₁ composite populations, while PS was significantly greater than MAS in only 4% of the comparisons. Cycle 1 MAS and PS were significantly greater than C₀ performance in 63 and 50% of the paired comparisons, respectively. MAS showed an average gain nearly twice of that of PS across all three populations. The average selection gain across the composite populations and selected traits, calculated as percent increase or decrease from the C₀, was 10.9 vs. 6.1% with MAS and PS, respectively.

The estimated cost required for phenotypic evaluation differed among the selected traits. For seedling emergence, the cost in C₁ was $56/family since only the initial field costs and germination counts were required. With eating quality traits (sucrose and tenderness), the C₁ costs were higher since they required laboratory analysis in addition to field costs. The cost to evaluate one family for sucrose or tenderness were $178/family or $158/family, respectively. The cost of hedonic evaluation was much greater ($370/family), requiring a much larger input of labor and time for taste panel preference evaluation. In contrast, with MAS the cost/family in C₁ ranged from $103 for emergence to $260 for hedonic rating. The estimated costs using MAS and PS for the selected traits in the second (C₂) and third (C₃) cycles are presented in Table 8. Phenotypic selection appeared more costly than MAS for quality traits. These costs will vary depending on the selected trait, number of evaluated traits, population size, labor cost, number of environments and replications, selection intensity, number of polymorphic markers detected, and type of DNA markers used in the breeding program. Marker systems such as RFLP are considered expensive and laborious compared with PCR-based markers. Advances in DNA technology have reduced costs associated with the application of molecular markers in breeding programs (Gu et al., 1995). It appears that making an accurate comparison between MAS and PS costs is very difficult and may not be applicable to every breeding scheme. These estimates reflect costs incurred in our program and are presented to provide a case study for comparing costs associated with PS and MAS for quantitative traits with different phenotypic evaluation costs.

View this table: [in this window] [in a new window]   Table 8. Estimates of the average evaluation costs (US$) for the selected traits using PS and MAS.

Selection is a process that increases favorable allelic frequencies. In our study, the first cycle of selection for single traits increased favorable allelic frequencies from about 50% in the suSe1, su1se1, and sh2 base populations to 73, 75, and 70% vs. 62, 66, and 56% in MAS and PS C₁ composites, respectively. The favorable allelic frequencies across all traits in the su1Se1, su1se1, and sh2 C₁ populations were increased by 45, 48, and 32% and by 24, 31, and 6% using MAS and PS, respectively. Change in allelic frequencies from C₀ to C₁ was highly correlated with selection gains (r = 0.70, P < 0.01). After one cycle of MAS, none of the beneficial alleles were fixed in any of the composites. The increase in beneficial allelic frequencies should result in most of the desirable alleles being fixed after two cycles of MAS with complete fixation by C₃. The change in allelic frequencies with PS was lower than those observed in MAS.

Comparing average selection gains of MAS (10.9%) with that of PS (6.1%) and costs associated with MAS and PS, we conclude that once beneficial alleles are identified, MAS can provide higher economic return in breeding programs. This advantage includes seedling emergence with low phenotypic evaluation costs ($56/family) compared with RFLP genotyping costs ($103/family) since the selection gain from MAS was approximately twice that of PS. However in the long run, MAS would be more advantageous, not only based on costs but also in accelerating the progress of breeding programs. Results generated from this study suggest that with traits requiring laboratory analysis and labor, MAS can provide higher economic returns in a shorter time while decreasing the probability of losing the desirable alleles. This agrees with the theoretical expectations of many authors (Dudley, 1993; Stuber, 1995; Knapp, 1998) and computer simulations (Zhang and Smith, 1992; Edwards and Page, 1994).

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The data obtained in the high direction, which is the aim of breeding programs, from this study of MAS is encouraging. Significant gains in the MAS C₁ composites compared with PS C₁ were obtained from selection based on DNA markers linked to QTL. These results are of particular importance since the population sizes used were relatively small, indicating that one may not need a large population size to achieve progress from MAS. In most cases, MAS provided simultaneous improvements for multiple traits. Many of these traits require laboratory evaluation and are difficult and expensive to characterize. Although MAS is relatively expensive, so is phenotypic-based selection. Marker-assisted selection can economically compete with PS, particularly with the advances in DNA technology and the gains resulting from the reduced size and duration of breeding programs. Therefore, incorporating DNA markers into traditional breeding programs can reduce the time and money needed to achieve breeding goals.

	ACKNOWLEDGMENTS

 
The authors acknowledge funding from grant numbers US-1709-89 and US-2242-92C from the US-Israel Binational Agricultural Research and Development, Minia University of Egypt, and the Egyptian Cultural and Educational Bureau in Washington, D.C. for support to the senior author during his study in the USA. Thanks to B. Klein, Dept. of Food Sciences, Univ. of Illinois for her guidance in the taste panel evaluation. The authors also thank William Tracy for seedling emergence evaluation. This study is a portion of a Ph.D. dissertation by the senior author at the University of Illinois, Urbana-Champaign.

Received for publication January 28, 2000.

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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