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CROP BREEDING, GENETICS & CYTOLOGY

Quantitative Trait Loci for Soybean Seed Yield in Elite and Plant Introduction Germplasm

^a Dep. of Agronomy, Iowa State Univ., Ames, IA 50011-1010
^b Dep. of Research and Product Development, Pioneer Hi-Bred International, Inc., Johnston, IA 50131

^* Corresponding author (wfehr{at}iastate.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Genetic improvement for yield in soybean [Glycine max (L.) Merr.] has been accomplished by breeding within a narrow elite gene pool. Plant introductions (PIs) may be useful for obtaining additional increases in yield if unique and desirable alleles at quantitative trait loci (QTL) can be identified. The objectives of the study were to identify QTL for yield in elite and PI germplasm and to determine if the PIs possessed favorable alleles for yield. Allele frequencies were measured with simple sequence repeat (SSR) markers in three populations, designated AP10, AP12, and AP14, that differed in their percentage of PI parentage. AP10 had 40 PI parents, AP12 had 40 PI and 40 elite parents, and AP14 had 40 elite parents. Four cycles of recurrent selection for yield had been conducted in the three populations. Allele frequencies of the highest-yielding C4 lines in the three populations were compared with the parents used to form the populations of the initial cycles. Allele flow was simulated to account for genetic drift. Fifty-four SSRs were associated with 43 yield QTL. Seven of the QTL had been identified in previous research. Sixteen favorable marker alleles were unique to the PI parents. The genes associated with the unique PI alleles merit further investigation for their potential to increase yield of soybean cultivars.

Abbreviations: AFLP, amplified fragment length polymorphism • p.d.f, probability density function • PI, plant introduction • QTL, quantitative trait loci • RAPD, random amplified polymorphic DNA • RFLP, restriction fragment length polymorphism • SSR, simple sequence repeat

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
THE ELITE SOYBEAN GENE POOL is comprised primarily of alleles from 10 plant introductions (PIs) (Delannay et al., 1983). The average relationship between any two northern or southern cultivars was estimated by Sneller (1994) to be approximately 0.25, which is equivalent to that of half-sibs. Soybean breeding has mainly employed biparental breeding populations with elite parents to improve yield (Fehr, 1987). This strategy has been successful for achieving an increase in soybean yields of 22.6 kg ha^–1 yr^–1 in the USA for the period 1924 to 1997 (Specht et al., 1999). Soybean breeders have attempted for many years to introgress PI germplasm into elite breeding populations to increase genetic variability for yield. The use of PI germplasm generally has not been as successful as selection within elite populations for developing high-yielding cultivars. Although PI germplasm probably has unique favorable alleles for yield, it has been difficult to identify and select for those alleles in a breeding population. Molecular markers may be a useful tool for identifying unique favorable alleles at quantitative trait loci (QTL) for yield in PI germplasm.

Quantitative trait loci have been identified through associations with changes in molecular marker allele frequency in recurrent selection populations. Stuber et al. (1980) found that allele frequency changes at eight isozyme loci in maize (Zea mays L.) agreed with yield increases in four recurrent selection experiments. Changes in allele frequencies of restriction fragment length polymorphisms (RFLP) in the Illinois Long Term Selection Experiment in maize corresponded to QTL for increased oil concentration identified in a F₂ mapping population (Sughroue and Rocheford, 1994). De Koeyer et al. (2001) measured allele frequency changes of RFLPs after seven cycles of recurrent selection for yield and other agronomic traits in oat (Avena sativa L.). They identified 13 QTL that had been detected previously in a recombinant inbred line population. Sebastian et al. (1995) compared allele frequencies for RFLP and random amplified polymorphic DNA (RAPD) markers of ancestral parents and elite soybean cultivars and lines. Changes in allele frequency were associated with 17 QTL for yield.

Whole genome scans for association of allele frequency with QTL would be expected to be especially effective in the identification of QTL in soybean. The relatively few ancestral parents that were the founders of the current elite gene pools and the self-fertilizing nature of the species favor the existence of extensive linkage disequilibrium (Nordborg et al., 2002; Rafalski, 2002a, 2002b).

A limited number of QTL for yield have been reported in soybean. Orf et al. (1999) used lines from ‘Minsoy’ x ‘Noir 1’, Minsoy x ‘Archer’, and Noir 1 x Archer populations to identify four QTL for yield with RFLP and simple sequence repeat (SSR) markers. Concibido et al. (2003) used SSR and amplified fragment length polymorphism (AFLP) markers and the advanced backcross method of QTL mapping described by Tanksley and Nelson (1996) to identify a yield QTL in a HS-1 (Hartz Seed, Stuttgart, AR) x PI 407305 population. Specht et al. (2001) used the genotypic data of Orf et al. (1999) from the Minsoy x Noir 1 population to identify six QTL for yield under water stress conditions. Yuan et al. (2002) used SSRs in the ‘Essex’ x ‘Forrest’ and ‘Flyer’ x ‘Hartwig’ populations to identify four yield QTL.

Recurrent selection for yield in five populations, designated AP10, AP11, AP12, AP13, and AP14, that differed in their percentages of PI germplasm began at Iowa State University in 1979. Vello et al. (1984) found the genetic variability for yield in cycle 0 (C0) of the four populations that contained PI germplasm was twice that of the population with no PI percentage. Ininda et al. (1996) reported the genetic gain for yield after three cycles of selection among F₄–derived lines in the five populations was 2.5% cycle^–1 in AP10 (100% PI), 2.0% in AP11 (75% PI), 3.1% in AP12 (50% PI), 2.8% in AP13 (25% PI), and 5.4% in AP14 (0% PI). There were no significant differences among the five populations in genetic variability among lines for yield in cycle 4 (C4) (Narvel, 1999). Changes in marker allele frequencies associated with recurrent selection for yield in the populations may be useful to identify genomic regions important for yield in diverse soybean germplasm. The objectives of this study were to identify QTL for yield in elite and PI germplasm through their association with SSR alleles that had frequency changes in the populations AP10, AP12, and AP14 and to determine if the PIs possessed favorable alleles for yield at the QTL.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Recurrent Selection
AP10 was formed with 40 PIs, AP12 was formed with 40 PIs and 40 elite cultivars and lines, and AP14 was formed with 40 elite parents (Fehr and Cianzio, 1981). The sole criterion for selection of the PI and elite parents was yield within Maturity Groups I to IV. The PI parents were chosen on the basis of yield in replicated trials in Iowa from a set of 240 accessions. The elite parents were the highest yielding lines from the Iowa Soybean Yield Test and the Uniform Regional Test, Northern States. Recurrent selection for yield was practiced among F₄–derived lines through four cycles of selection for each of the populations (Ininda et al., 1996). The 20 highest yielding lines within Maturity Groups II and III were intermated to form the populations for the next cycle of selection.

The method described by Sebastian et al. (1995) was the basis for the QTL analysis. The method involves genotyping with molecular markers the improved lines from the most advanced cycle of selection and the earliest known ancestors of the improved lines. The lines used in this study were the original PI and elite parents of the C0 populations, the 20 high-yielding lines selected as parents in AP10 and AP14 to form the cycle 1 (C1) populations, the 15 highest-yielding lines from the C4 populations of AP10 and AP14, and the 13 highest-yielding lines from the C4 population of AP12 (Fig. 1) . The number of C4 lines chosen from AP12 was limited to 13 to make it possible to analyze the samples in complete 96-well plates. One ancestor of AP14 was an elite experimental line that could not be included in the study because its seed did not germinate. The data for selecting the highest-yielding lines from the C4 populations were obtained by Narvel (1999), who tested 100 randomly chosen C4 lines of each population in two replications at three Iowa locations in 2 yr.

View larger version (34K): [in this window] [in a new window]   Fig. 1. Recurrent selection for yield in the AP10 (100% PI parents), AP12 (50% PI parents), and AP14 (0% PI parents) soybean populations. HY = highest-yielding lines in the population.

A total of 184 fluorescently labeled SSRs spaced 15 cM apart on average were chosen based on their genome distribution. The map positions were derived from the USDA–Iowa State Univ. genetic map (Cregan et al., 1999). The PCR reaction consisted of 1.0 µL GeneAmp 10x PCR Buffer II, 0.6 µL 25 mM MgCl₂, 0.2 µL 10 mM dNTP, 1.7 µL 2 µM forward/reverse primer mix, 0.06 µL AmpliTaq Gold DNA polymerase, 1.0 µL 10 ng µL^–1 DNA, and 5.44 µL HPLC H₂O (Perkin-Elmer, Foster City, CA). The PCR program was 10 min at 95°C, then 45 cycles of the following: 50 s at 95°C, 50 s at the annealing temperature, and 85 s at 72°C. A final extension step of 10 min at 72°C was used. PCR was performed individually for each marker and genotype combination.

PCR products were multiplexed by allele size and florescence color, diluted by a SciClone Liquid Handling Workstation (Zymark Corporation, Hopkinton, MA), and separated via capillary electrophoresis with an ABI Prism 3700 DNA Analyzer (Applied Biosystems, Foster City, CA). ROX 400HD was used as the internal standard to calculate allele sizes (Applied Biosystems, Foster City, CA). Data were collected with GENESCAN Prism software (Applied Biosystems, Foster City, CA) and allele sizes estimated by GENOTYPER software (Applied Biosystems, Foster City, CA). Manual verification of the allele sizes was performed.

Allele Frequency Changes
Allele frequencies in the C4 lines were compared with the parents used to form the C0 populations of AP10, AP12, and AP14. For AP10 and AP14, the allele frequencies in the C4 lines also were compared with the 20 highest-yielding C0 lines used to form the C1 populations. The probability that each improved line inherited each allele from its ancestors was calculated and averaged over improved lines to determine the expected frequency of each allele. The observed and expected allele frequencies of the improved lines were compared to determine which alleles occurred more or less frequently than expected (De Koyer et al., 2001; Sebastian et al., 1995). The comparision of allele frequencies before and after selection must account for mutation, migration, and genetic drift, which may influence the allele frequency of the population (Falconer and Mackay, 1996). The effects of mutation were considered negligible because the breeding process consisted of only four cycles of selection. The self-fertilizing nature of soybean and the care practiced during intermating likely prevented the migration of alleles into the population. Genetic drift may have had a large influence on the allele frequencies of the lines in the C0 and C4 generations, and was accounted for through two methods.

For AP10 and AP14, analyses conducted with the parents of the C0 populations as the ancestors were compared with the analyses when the 20 highest-yielding C0 lines of each population were considered the ancestors. The comparison was used to differentiate between changes in frequency of marker alleles due to an association with a selected QTL allele versus allele frequency changes due to the restriction of alleles that occurred in forming the C1 populations. The 20 C0 lines used as parents to form the C1 populations was half the number of parents used to form the C0 populations of AP10 and AP14 and one-quarter the number of parents used to form the C0 population of AP12. The reduction in the number of parents and the inbreeding of the parents used to form the C1 populations contributed to genetic drift through a restriction in the number of alleles from the parents of the C0 populations that could be expected in the C4 lines.

The flow of each marker allele was simulated 10000 times from the ancestors to the C4 lines to construct a probability density function (p.d.f.) for each recurrent selection population structure. Given the pedigree structure for the C4 lines, and the genotype of the most distant ancestral nodes, the genotype of the C4 lines was computed by a simulation that assumed random inheritance of parental alleles, with each intermediate selfed to homozygosity. The simulation fully accounted for the dependencies between the probabilities of an allele appearing in each C4 line because of the dependencies reflected in the pedigree structure. Repeating the simulation 10000 times provided an estimate of the p.d.f. for the allele frequencies in the C4 lines under the null hypothesis of no selection. Missing ancestral genotypes were handled by randomly selecting for each simulation of a genotype based on the allele frequencies in the ancestors. The area under the tail of the p.d.f. was quantified as a P value that was the measure of the number of rounds that the simulation generated an allele frequency at least as extreme as that observed in the C4 lines (Miller and Miller, 1999). The formula was P = (S_e/S_t), where S_e was the number of rounds of simulation that produced an allele frequency equal to or more extreme than that observed in the C4 lines and S_t was the total number of rounds of simulation. For example, if the expected frequency of an allele in the C4 lines was 0.15, the observed frequency was 0.30, and 500 out of 10000 rounds of simulation produced an allele frequency 0.30, then S_e = 500, S_t = 10000, and P = 0.05. A P value 0.05 was used as the significance threshold to declare that the frequency change in a marker allele was not entirely due to random genetic drift, but may be associated with selection for a linked QTL allele.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Molecular markers were identified that had significant allele frequency changes at the threshold probability level of P value 0.05 (Table 1). The marker alleles with frequency changes were inferred to be associated with alleles that have undergone selection at yield QTL. There were 27 alleles at 25 SSR markers identified as significant in AP10, 21 alleles at 20 SSRs in AP12, and 19 alleles at 18 SSRs in AP14. There were 16 alleles at 15 SSRs unique to the PIs, 9 alleles at 9 SSRs unique to the elites, and 41 alleles at 36 SSRs in both the PI and elite parents (Table 1). The difference between the number of significant marker alleles and loci indicated that more than one allele at some loci was influenced by selection for yield. Narvel et al. (2000) measured the diversity of the original parents of the C0 populations of AP10 to AP14 with SSR markers and observed a greater number of alleles in the PI parents than in the elite parents. Our results indicated that some of those unique PI alleles remained after the fourth cycle of selection and increased appreciably in frequency in the highest-yielding C4 individuals of AP10 and AP12.

View this table: [in this window] [in a new window]   Table 1. SSR marker alleles with significant frequency changes in the soybean populations AP10, AP12, and AP14.

The results obtained from AP10, AP12, and AP14 were compared with the studies of yield QTL mapping in biparental populations. There were four yield QTL reported by Orf et al. (1999), one by Concibido et al. (2003), six by Specht et al. (2001), and four by Yuan et al. (2002). We identified a total of 54 SSR markers associated with 43 yield QTL in AP10, AP12, and AP14 (Table 1). The greater number of yield QTL identified in our study than in previous research reflected the greater number of PI and elite parents used to form the C0 populations. The larger number of accessions provided the opportunity for a greater number of QTL alleles to segregate than would be possible in any biparental population. Nine of the SSR markers associated with yield QTL in our study were in seven regions where yield QTL had been identified in previous research (Table 1). A yield QTL detected with Satt066 on B2 was identified previously by Concibido et al. (2003) with the AFLP marker U3944117. The QTL region identified with the marker Satt294 on C1 also was reported by Yuan et al. (2002). On C2, Satt277 identified a yield QTL in the same region that was reported by Orf et al. (1999) with the markers Satt277 and Satt489 and by Specht et al. (2001) with the markers Satt205 and Satt489. The marker Sat_074 on F was associated with a QTL for yield in our study and in a study by Specht et al. (2001). A QTL on H was associated with Satt469 and Specht et al. (2001) used the linked marker, Satt314, to identify the same yield QTL. Two regions containing yield QTL that were identified on K also were reported by Yuan et al. (2002). They found the first QTL on K was associated with Satt337 and Satt326 and the second QTL was associated with Satt539. The markers Satt590, Satt567, and Satt540 on M identified a yield QTL that was detected by Orf et al. (1999) using Satt150 and by Specht et al. (2001) using Satt150 and Satt567.

There was a larger reduction in AP10 than in AP14 for the number of allele frequency changes when the C4 lines were compared with the original PI or elite parents than when they were compared with the 20 highest-yielding lines used to form the C1 populations. When the 15 highest-yielding C4 lines were compared with the 40 parents of the C0 population of AP10, 58 alleles were significant at P 0.10 (Table 2). When the 20 highest-yielding C0 lines of AP10 were used as the ancestors, 29 alleles were significant. In AP14 when the 15 highest-yielding C4 lines were compared with the 40 parents of the C0 population, 29 alleles were significant. When the 15 highest-yielding C4 lines were compared with the 20 C0 lines used to form the C1 population of AP14, 24 alleles were significant.

The number of alleles with significant frequency changes in AP10, AP12, and AP14 did not correspond to the genetic gain for yield that had been realized during three cycles of selection in the three populations. In our study, the number of markers with significant changes in allele frequency was greatest for AP10, intermediate for AP12, and least for AP14 (Table 2). Ininda et al. (1996) reported that the percentage yield increase from the first three cycles of selection was 2.5% cycle^–1 in AP10, 3.1% in AP12, and 5.4% in AP14. The greater genetic gain in AP14 for the initial cycles of selection may be due to genetic asymmetry. Allele frequencies near 0.5 maximize the heritability for additive traits (Falconer and Mackay, 1996). In Table 1, the expected allele frequencies of the SSR markers represent the allele frequencies that were present in the parents of the C0 and C1 populations. The observed allele frequencies indicate the allele frequencies that were present in the highest-yielding C4 lines that would be used to form the C5 populations. The percentage of alleles that had expected frequencies between 0.2 and 0.8 was 2.7% for AP10, 4.2% for AP12, and 9.5% for AP14. The percentage of alleles with observed frequencies between 0.2 and 0.8 was 71.6% for AP10, 47.9% for AP12, and 57.1% for AP14. The increase in the percentage of alleles with frequencies near 0.5 suggested the heritability and genetic gain for yield may increase in future cycles of selection for yield in AP10, AP12, and AP14. The higher percentage of QTL alleles with intermediate frequencies in AP10 compared with AP14 indicates that AP10 might have an increased rate of genetic gain for yield over AP14 in future cycles of selection for yield.

	ACKNOWLEDGMENTS

 
The authors thank M.L. Katt, D.J. Cahill, and W-C. Chu from Pioneer Hi-Bred International, Inc. for laboratory resources and advice; J.D. Lorentzen and D.F. Austin from Pioneer Hi-Bred International, Inc., and M.K. Hanafey from DuPont Crop Genetics for statistical analysis and software guidance; and J.M. Narvel and G.A. Welke of Iowa State University for yield evaluation of the cycle 4 lines and for DNA collection from the genotypes used in the study.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Project No. 3732 supported by the Hatch Act, State of Iowa, Iowa Soybean Promotion Board, Raymond F. Baker Center for Plant Breeding, and Pioneer Hi-Bred International, Inc.

Received for publication April 8, 2003.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 

• Concibido, V.C., B. La Vallee, P. Mclarid, N. Pineda, J. Meyer, L. Hummel, J. Yang, K. Wu, and X. Delannay. 2003. Introgression of a quantitative trait locus for yield from Glycine soja into commercial soybean cultivars. Theor. Appl. Genet. 106:575–582.[ISI][Medline]
• Cregan, P.B., T. Jarvik, A.L. Bush, R.C. Shoemaker, K.G. Lark, A.L. Kahler, N. Kaya, T.T. VanToai, D.G. Lohnes, J. Chung, and J.E. Specht. 1999. An integrated genetic linkage map of the soybean genome. Crop Sci. 39:1464–1490.[Abstract/Free Full Text]
• De Koeyer, D.L., R.L. Philips, and D.D. Stuthman. 2001. Allelic shifts and quantitative trait loci in a recurrent selection population of oat. Crop Sci. 41:1228–1234.[Abstract/Free Full Text]
• Delannay, X., D.M. Rogers, and R.G. Palmer. 1983. Relative genetic contribution among ancestral lines to North American soybean cultivars. Crop Sci. 23:944–949.[Abstract/Free Full Text]
• Falconer, D.S., and T.F.C. Mackay. 1996. Introduction to quantitative genetics. 4th ed. Longman Group Ltd., New York.
• Fehr, W.R. 1987. Soybean. p. 533–576. In W.R. Fehr (ed.) Principles of cultivar development: Volume II. Crop Species. Macmillan, Inc., New York.
• Fehr, W.R., and S.R. Cianzio. 1981. Registration of soybean germplasm populations AP10 to AP14. Crop Sci. 21:477–478.[Free Full Text]
• Ininda, J., W.R. Fehr, S.R. Cianzio, and S.R. Schnebly. 1996. Genetic gain in soybean populations with different plant introduction parentage. Crop Sci. 36:1470–1472.[Abstract/Free Full Text]
• Miller, I., and M. Miller. 1999. John E. Freund's mathematical statistics, 6th ed. Prentice Hall, Upper Saddle River, NJ.
• Narvel, J.M. 1999. Assessment of plant introductions for increasing the genetic variability of soybean populations. PhD. diss. (Diss. abstr. AAT9950107). Iowa State University, Ames.
• Narvel, J.M., W.R. Fehr, W.-C. Chu, D. Grant, and R.C. Shoemaker. 2000. Simple sequence repeat diversity among soybean plant introductions and elite genotypes. Crop Sci. 40:1452–1458.[Abstract/Free Full Text]
• Nordborg, M., J.O. Bergelson, C.C. Berry, J. Chory, J. Hagenblad, M. Kreitman, J.N. Maloof, T. Noyes, P.J. Oefner, E.A. Stahl, and D. Weigel. 2002. The extent of linkage disequilibrium in Arabidopsis thaliana. Nat. Genet. 30:190–193.[ISI][Medline]
• Orf, J.H., K. Chase, T. Jarvik, L.M. Mansur, P.B. Cregan, F.R. Adler, and K.G. Lark. 1999. Genetics of soybean agronomic traits: I. Comparison of three related recombinant inbred populations. Crop Sci. 39:1642–1651.[Abstract/Free Full Text]
• Rafalski, A. 2002a. Applications of single nucleotide polymorphisms in crop genetics. Curr. Opin. Plant Biol. 5:94–100.[ISI][Medline]
• Rafalski, J.A. 2002b. Novel genetic mapping tools in plants: SNPs and LD-based approaches. Plant Sci. 162:329–333.
• Sebastian, S.A., M.K. Hanafey, and S.V. Tingey. 1995. Method to identify genetic markers that are linked to agronomically important genes. U. S. Patent 5437697. Date issued: 1 Aug.
• Sneller, C.H. 1994. Pedigree analysis of elite soybean lines. Crop Sci. 34:1515–1522.[Abstract/Free Full Text]
• Specht, J.E., K. Chase, M. Macrander, G.L. Graef, J. Chung, J.P. Markwell, M. Germann, J.H. Orf, and K.G. Lark. 2001. Soybean response to water: A QTL analysis of drought tolerance. Crop Sci. 41:493–509.[Abstract/Free Full Text]
• Specht, J.E., D.J. Hume, and S.V. Kumudini. 1999. Soybean yield potential—A genetic and physiological perspective. Crop Sci. 39:1560–1570.[Abstract/Free Full Text]
• Stuber, C.W., R.H. Moll, M.M. Goodman, H.E. Schaffer, and B.S. Weir. 1980. Allozyme frequency changes associated with selection for increased grain yield in maize (Zea mays L.). Genetics 95:225–236.[Abstract/Free Full Text]
• Sughroue, J.R., and T.R. Rocheford. 1994. Restriction fragment length polymorphism differences among Illinois long-term selection oil strains. Theor. Appl. Genet. 87:916–924.
• Tanksley, S.D., and J.C. Nelson. 1996. Advanced backcross QTL analysis: A method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theor. Appl. Genet. 92:191–203.[ISI]
• Vello, N.A., W.R. Fehr, and J.B. Bahrenfus. 1984. Genetic variability and agronomic performance of soybean populations developed from plant introductions. Crop Sci. 24:511–514.[Abstract/Free Full Text]
• Yuan, J., V.N. Njiti, K. Meksem, M.J. Iqbal, K. Triwitayakorn, M.A. Kassem, G.T. Davis, M.E. Schmidt, and D.A. Lightfoot. 2002. Quantitative trait loci in two soybean recombinant inbred line populations segregating for yield and disease resistance. Crop Sci. 42:271–277.[Abstract/Free Full Text]

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This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (2) Citing Articles via Google Scholar Google Scholar Articles by Smalley, M. D. Articles by Streit, L. G. Search for Related Content PubMed Articles by Smalley, M. D. Articles by Streit, L. G. Agricola Articles by Smalley, M. D. Articles by Streit, L. G. Related Collections Cell Biology & Molecular Genetics Crop Genetics Soybean