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a Dep. of Plant Sciences, Univ. of Tennessee, 2431 Joe Johnson Drive, Knoxville, TN 37996
b Dep. of Animal Science, Univ. of Tennessee, 2505 River Dr., 208c Brehm Animal Sciences, Knoxville, TN 37996
* Corresponding author (vpantalo{at}utk.edu)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Abbreviations: CIM, composite interval mapping • cM, centimorgan • KPSF, Knoxville plant science farm • LOD, log of odds ratio • MAS, marker-assisted selection • MLG, molecular linkage group • NITS, Near-infrared transmittance spectroscopy • PAGE, polyacrylamide gel electrophoresis • PCR, polymerase chain reaction • QTL, quantitative trait loci • RAPD, randomly amplified polymorphic DNA • RCBD, randomized complete block design • RFLP, restriction fragment length polymorphism • RIL, recombinant inbred lines • SSR, simple sequence repeat
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Hardiness, brittleness, and gumminess are important physical properties, which determine the overall quality of tofu. Protein concentration, particularly glycinin, determines these properties in tofu, whereas seed size determines the quality of soy sprouts (Liu, 1997). Small seeded soybeans are generally desired for high quality soy sprouts and natto production, and combining higher protein with large seed size is desirable for tofu production. Genomic regions for seed size along with protein and oil QTL could be used in marker-assisted selection (MAS) for desired soybean types for soy food applications.
Despite moderately high heritabilities (Burton, 1987), it is difficult to improve seed traits, particularly protein and oil concentration, simultaneously. MAS might be useful in achieving specific goals if genomic regions controlling protein and oil concentration and seed size could be identified to improve selection indices more precisely.
Several researchers have paved the way toward this goal (Brummer et al., 1997; Chung et al., 2003; Diers et al., 1992; Lee et al., 2001; Lee et al., 1996c). Qiu et al. (1999) found two restriction fragment length polymorphism (RFLP) markers associated with protein QTL, and one marker associated with oil QTL in a population derived from ‘Peking’ x ‘Essex’ on molecular linkage group F and H. Since the total phenotypic variation explained by the QTL was around 30%, they assumed that there should be additional QTL determining these traits, which could not be detected because of background genetic effects of the population in their experiment. Csanadi et al. (2001) mapped QTL for protein, oil, and seed size in an early maturing soybean population developed from the cross of cultivars Maple Belle x Proto. They found four QTL for protein, three for oil, and eight for seed size. Mansur et al. (1996) mapped QTL for protein, oil, and seed size in a population developed from ‘Minsoy’ x ‘Noir 1’. They found three QTL each for protein, oil and seed size on MLG U3, U7, U14, and U20, which corresponds to current MLG G, A1, B2, L, and B1, respectively (Cregan et al., 1999). Several seed size QTL have also been identified but there are very few consistent QTL across the populations (Hoeck et al., 2003; Maughan et al., 1996; Mian et al., 1996; Orf et al., 1999). Breeder's use of such QTL is more likely only after they are confirmed in diverse populations grown in diverse environments.
Currently, there are at least 53 QTL associated with oil content, 61 with protein concentration, and 66 with seed size (Hyten et al., 2004). However, most of the QTL are not confirmed (Pantalone et al., 2004). Furthermore, the populations have been derived from common parents or close parents in many cases (Hyten et al., 2004). It is important to identify QTL in key pedigree lines and in as many populations as feasible which are derived from modern parents and grown in multiple environments to verify stable QTL. In that regard, one of the most consistent protein QTL reported to date was found on MLG I near Satt292 by numerous researchers (e.g., Chung et al., 2003; Diers et al., 1992). The objective of the present study was to detect QTL associated with seed protein and oil concentration and seed size in a soybean population targeted for protein enhancement.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Phenotypic Traits
Approximately 25 g of seed samples were analyzed as whole beans for protein and oil concentration per plot using an Infratec 1255 NIR Food and Feed Grain Analyzer (Ultra Tec Manufacturing Inc., Santa Ana, CA) at USDA-NCAUR, Peoria, IL. The Infratec analyzer is a near infrared transmittance instrument and is used for simultaneous constituents determination of whole grains. The measurements were based on absorbance of electromagnetic radiation in the near infrared region of the spectrum. Seed size was determined by weighing 100 seeds per plot.
DNA Extraction and Polymerase Chain Reaction
DNA was extracted from the RIL and parental lines using a Qiagen Plant Easy DNA Extraction Kit (Qiagen, Hilden, Germany). Polymerase chain reaction (PCR) consisted of 7.4 µL of ddH2O, 1 µL of 10x PCR Buffer, 1 µL of 2 mM dNTPs mixture (Pharmacia, Piscataway, NJ), 0.5 µL of 20 µM forward and reverse primer, 0.1 µL of 5 units/µL Klentaq (Ab Peptides Inc., St. Louis, MO) and 2 µL of 20 ng/µL template DNA. The PCR was performed in a 96-well MBS Hybaid thermocycler (Hybaid, Franklin, MA). PCR conditions were (i) 94°C for 5 min. followed by 35 cycles at 94°C for denaturation for 25 s; (ii) 47°C for annealing for 30 s; and (iii) 72°C for 25 s for extension, and one last cycle at 72°C for final extension for 5 min. Parents were screened for a total of 585 (ATT)n type of simple sequence repeat (SSR) genetic markers (Cregan et al., 1999) covering all 20 MLG. The sequence information for the markers is publicly available from Soybase (http:/soybase.org; verified 25 May 2005). A total of 138 molecular markers were found polymorphic between parents but only 94, which were scorable and polymorphic among RIL, were used in QTL analysis.
DNA Gel Electrophoresis
A 6% non-denaturing polyacrylamide gel electrophoresis (PAGE) consisting of 6% bis-acrylamide, 0.5% TBE buffer, 0.07% APS, and 0.035% TEMED (measured as 28.5 mL of 40% bis-acrylamide, 160.2 mL of 0.593x TBE buffer, 1.33 mL of 10% APS, and 66.5 µL of TEMED) was used to separate the PCR product. Two µL of loading buffer (6x) were added to the PCR products, and a 10 µL sample was loaded on the gel. The running buffer was 0.5x TBE. The gel was run at a constant 300 V for 3 h. A fan was used to circulate air to keep the glass plates cool during gel running. Ethidium bromide (50 µL, 10 mg mL–1) was added to the running buffer to visualize the bands under exposure to UV light. Bands were scored using 1 to represent P1 (homozygotes for N87-984-16), 2 to represent heterozygotes, and 3 to represent P2 (homozygotes for TN93-99) alleles for each primer locus.
Data Analysis
Phenotypic data for protein, oil, and seed size were analyzed using the MIXED procedure of SAS software to determine the genotypic differences among the RIL (SAS Institute Inc, 2002). Location and replications were considered as random blocking factors in the model. Heritability of each trait in the population was estimated on an entry mean basis (Nyquist, 1991) as follows:
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2g is genetic variance, 2ge is genotype by environment variance, 2 is error variance, r is number of replications, and e is the number of environments. REML estimation in PROC MIXED produced variance components for heritability calculations.
Phenotypic correlations were determined using the CORR procedure of SAS and genetic correlations were determined using the following formula (Falconer and Mackay, 1996; Kearsey and Pooni, 1996):
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2 is genetic variance. Cross-products between traits were generated using the MANOVA option of the GLM procedure. Candidate QTL were identified by determining associations between molecular data and least square means for protein, oil and seed size using the GLM procedure of SAS (SAS Institute Inc, 2002) for each environment and combined over six environments. All heterozygotes were excluded from the analysis because the RIL population was F6–derived, and our interest was to detect heritable alleles from pure lines. Marker order and distance were determined using Mapmaker/Exp 3.0 (Lincoln et al., 1993). Information from Mapmaker was used in QTL Cartographer (Wang et al., 2003) to verify the candidate QTL by composite interval mapping (CIM). We used the standard model Zmapqtl 6 in the CIM procedure with a 10 cM window size and a 2 cM walking speed. We performed 1000 permutations on all traits in each environment and on the combined data from six environments to establish the empirical LOD thresholds at the 5% probability level (Churchill and Doerge, 1994). Any location with a LOD score greater than the empirical LOD threshold level was considered to identify QTL significantly associated with the trait.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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There was a negative phenotypic correlation between protein and oil concentration (r = –0.59, P < 0.001) but a positive phenotypic correlation between protein and seed size (r = 0.50, P < 0.001) in this population. There was close agreement between genetic and phenotypic correlations (Table 2). A strong negative phenotypic correlation between protein and oil (r = –0.84, P < 0.001) was reported recently (Chung et al., 2003), and it was much stronger (r = –0.98, P < 0.001) in a different study (Mansur et al., 1996). On the basis of 50 yr average data, a moderately weak negative correlation between protein and oil (r = –0.39, P < 0.001) was reported and it was slightly weaker (r = –0.33, P < 0.001) in the northern states and stronger (r = –0.52, P < 0.001) in the southern states of the USA (Yaklich et al., 2002). Yaklich et al. (2002) have also reported the correlation coefficient on the basis of maturity groups 00 to VIII and showed that there was a stronger correlation in the higher maturity groups. Plant breeders prefer to have the correlation between protein and oil in soybean as weak as possible to enable flexible targeting of phenotype.
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QTL for Protein
There was one molecular marker (Satt570) located on MLG G, which was consistently associated with a protein QTL in this population (Table 3). When LOD scores from combined data were plotted, there was a significant peak upstream of Satt570 (Fig. 2), indicating that a major QTL associated with seed protein concentration resides in that genomic region. Another molecular marker (Satt274) associated with a protein QTL was significant in only one environment when the empirical LOD score was used, but it was consistent across the environments with single factor ANOVA (data not shown). When its LOD scores were plotted on MLG D1b using mean data from 2003, there was a peak above Satt274 but not up to the threshold level (Fig. 2). Significant association in one environment and a slightly lower LOD score when using combined data indicated that there may be a QTL near Satt274, which is environment specific. Such QTL can be used in MAS aimed for developing a cultivar for a specific environment. From these results, one major QTL near Satt570 appears to be stable for protein concentration in this population, whereas the QTL near Satt274 was environmentally sensitive. In fact, both of these molecular markers (Satt274 and Satt570) were found to be associated with seed nitrogen accumulation during reproductive growth stages in soybean (Panthee et al., 2004a). Nitrogen accumulated in the soybean seed is involved in storage protein accumulation.
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Recently, the genomic region near Satt459 has been reported to have a novel protein QTL (Hyten et al., 2004), which is within 2.3 cM downstream of Satt274 in the integrated linkage map (Cregan et al., 1999), where we found an environmental specific QTL in our population. Our data thus support the presence of a QTL in this genomic region controlling protein concentration in soybean. Brummer et al. (1997) found three QTL associated with protein on MLG G, and we also found a major stable QTL near Satt570 in this MLG. However, the closest QTL from the Brummer et al. (1997) study is located at least 54.8 cM distal to Satt570, indicating that we have detected a novel protein QTL from the Prolina parentage. These findings suggest that out of two protein QTL, the one on MLG D1b near Satt274 is in a genomic region already identified and verified in the present study whereas the one on MLG G near Satt570 is novel. Molecular marker Satt274 was also found to be associated with many of the soy protein amino acids (Panthee, 2005) indicating that it is also involved in determining the protein quality in soybean.
Molecular linkage group G has been reported to have a QTL associated with the Glycinin (11S) fraction of soybean protein (Panthee et al., 2004b). The same MLG has also been reported to have QTL for important diseases like SDS and SCN (Meksem et al., 1999; Njiti et al., 2002) and Phytophthora (Demirbas et al., 2001).
QTL for Oil
There were four molecular markers (Satt274, Satt317, Satt420, and Satt479) associated with oil QTL in this population (Table 3). These QTL were located on three MLG D1b, H, and O, respectively. Phenotypic variation explained by an individual QTL ranged from 9.4 to 15%. Favorable alleles for increased oil concentration in three of the four QTL (near Satt274, Satt420, and Satt479) were contributed through N87-984-16, although TN93-99 was the high parent for oil concentration. It is noted that Satt420 and Satt479 are 4.5 cM apart on MLG O indicating that both the markers may be associated with the same QTL in the genomic region associated with oil concentration in soybean.
One QTL near RFLP marker (B072_1) was found to be associated with oil concentration in a soybean population from Peking x Essex (Qiu et al., 1999), which is about 20 cM downstream of Satt317 on MLG H (Cregan et al., 1999; Song et al., 2004). Although the distance between the regions identified by Qiu et al. (1999) on MLG H and the region on the same MLG that we detected is still large, our results provide additional information on this oil controlling region in soybean. Brummer et al. (1997) found one oil QTL near RFLP marker A069_1 located on MLG H. We also found a QTL on the same MLG, but it was about 56 cM distant to A069_1, indicating an independent QTL. Another oil QTL near RFLP marker A566_2 located on MLG H was also identified by Lee et al. (1996c). It has been recently reported that there are three genomic regions (Pantalone et al., 2004) containing QTL associated with oil concentration on MLG H, all of which are at least 30 cM from the QTL detected in the present study, indicating that our population may contain novel QTL associated with oil concentration near Satt317.
There was a QTL associated with oil concentration near Satt570 on MLG G, which was significant in multiple environments (data not shown) but nonsignificant when all combined data were used (Fig. 2). This indicated that there may be an environmentally sensitive oil QTL near Satt570 on MLG G, where a protein QTL was also detected in this population. Three RFLP based QTL associated with oil concentration from MLG G were previously identified (Lee et al., 1996c). Two of them (near L154_2 and A235_1) were more than 85 cM apart from the one detected in the present population, indicating that the oil QTL we detected was independent. Similarly, oil QTL (near L002_1) detected by Lee et al. (1996c) on MLG G was 39 cM distal to Satt570, indicating that this is also an independent QTL. There are no reported QTL from MLG D1b and O associated with oil concentration (Pantalone et al., 2004). Therefore, QTL reported from the present study on those MLG are novel.
Two populations were studied for QTL analyses for oil and protein concentration by Lee et al. (1996c); however, no QTL were consistent between the populations. Recently, Fasoula et al. (2004) reported that only two of four previously reported protein QTL and two of three previously reported oil QTL could be confirmed in a population of PI97100 x ‘Coker 237’. In the same study, they used a different population of ‘Young’ x PI416937, in which they could not confirm any of the previously reported three protein QTL, and only one of three oil QTL was confirmed. Therefore, soybean geneticists should continue efforts to detect, validate, and confirm QTL to make this technology more practical for applied breeders.
It was interesting that both protein QTL detected in the present study were also associated with oil concentration (Table 3 and Fig. 2). However, one of these QTL (near Satt570) had opposite additive genetic effects for these two traits, whereas Satt274 had positive additive genetic effects on both traits (data not shown). In the present population, there was a moderate negative correlation (r = –0.59) between protein and oil concentration (Table 2). Possibly the genomic region near Satt274 is playing a role in reducing the negative correlation. To increase the protein and oil concentration simultaneously in a soybean line, the most desirable condition is to have positive effects for both traits on particular loci. Therefore, QTL near Satt274 may be useful for this purpose. Three QTL near T155, A329, and Satt006 located on MLG A1, B2, and L, respectively, in the integrated molecular linkage map were associated with protein as well as oil concentration in soybean (Mansur et al., 1996). However, all three loci had opposite effects on protein and oil concentration making it difficult to improve genetic gain in both traits simultaneously using these markers. A locus near RFLP marker A566-2 located on MLG H was found to be associated with protein and oil in a population derived from PI 97100 x Coker 237 (Lee et al., 1996c). They detected three QTL (near L154-2, A235-1, and L002-1) located on MLG G associated with oil concentration, but the closest QTL was about 38 cM downstream of the protein QTL detected near Satt570 in our population.
At least four QTL associated with plant height and one each associated with lodging and yield from MLG D1b have been reported in Soybase (Lark et al., 1995; Lee et al., 1996a; Specht et al., 2001). However, none of them are in close proximity to the QTL detected on this linkage group in our population. A QTL near K007 associated with plant height was identified in a population derived from PI 97100 x Coker 237 located on MLG H (Lee et al., 1996b), which is about 25 cM downstream of an oil QTL in the vicinity of Satt317 in our population. Kabelka et al. (2004) found Satt142 located on MLG H was associated with seed yield, which is just 3 cM from Satt317, a marker associated with oil QTL in our population. Plant height and oil QTL near Satt542 and Satt157, respectively, detected by Kabelka et al. (2004) on MLG D1b, were more than 60 cM upstream of an oil QTL near Satt274 identified in our population. Plant height QTL near Sat_096 located on MLG D1b was detected in a population derived from Noir 1 x Archer (Orf et al., 1999), which is toward the opposite end of Satt274 on the same linkage group.
QTL for Seed Size
There were three molecular markers (Satt002, Satt147, and Satt184) associated with seed size QTL in this population located on two MLG D1a and D2 (Table 3). All the QTL were consistent across the environments. Phenotypic variation explained by an individual QTL ranged from 10 to 15% (Table 3). Molecular linkage group D1a contains two seed size QTL, near Satt147 and Satt184, which are within 20 cM distance of each other. We plotted the LOD score against markers on MLG D1a, which revealed that there was a major QTL between Satt147 and Satt184 in this population (Fig. 3). A relatively flat peak indicated that there could be more than one major QTL in this genomic region. Hoeck et al. (2003) detected two QTL (near Satt002 and Satt184) associated with seed size, which are common with our findings. Fasoula et al. (2004) recently confirmed seed size QTL in independent populations and wisely mentioned that it is necessary to verify already reported QTL in independent populations, which is as important as identifying new QTL. The QTL linked to Satt002 and Satt184 are verified QTL for seed size, whereas the one near Satt147 is novel in our population.
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Interestingly, almost all the QTL detected for protein, oil, and seed size in our population had R2 
10%; thus, they are major QTL for these seed traits (Falconer and Mackay, 1996). Analyses of allelic contributions for quantitative traits at particular loci are important to improve traits through introgression of alleles from a particular breeding line. In a population developed from G. max x G. soja Siebold & Zucc. for the study of QTL associated with protein and oil, it was found that all protein-related alleles were contributed through G. soja, whereas oil-related QTL were contributed through G. max (Diers et al., 1992). However, in soybean populations such as ours, we found contribution from both parents to the target traits of interest. Similar observations were made for a number of traits by Mansur et al. (1993). Transgressive segregation may take place among the progeny, and molecular genetic detection of the underlying loci which contribute to the process is important knowledge for plant breeders.
On the basis of results from past experiments and present findings, it is important to note that all QTL associated with a trait are not stable across populations and environments. In MAS, it would be more desirable to use confirmed QTL. For this purpose, it is necessary to conduct QTL mapping studies in as many and as diverse environments and populations as feasible (Fasoula et al., 2004; Song et al., 2004). The present experiment has verified a few QTL and it has also detected a few novel QTL, which provides important information. Just as we verified a few QTL previously detected by other researchers, the novel QTL from the present study need to be verified in the future. We encourage other researchers to continue working in this direction.
Finally, our population served not only for QTL detection but also was utilized to develop and release a new improved protein germplasm, TN04-5321, by the Tennessee Agricultural Experiment Station. We hope that the QTL identified from this study and the recent germplasm line that we have released will benefit breeders in accumulating favorable alleles for improvement in soybean seed quality.
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ACKNOWLEDGMENTS |
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Received for publication December 10, 2004.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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