Vol. 13(1), pp. 14-22, January-March 2021
DOI: 10.5897/JPBCS2020.0938
Article Number: B3EB80B66163
ISSN 2006-9758
Copyright ©2021
Author(s) retain the copyright of this article
http://www.academicjournals.org/JPBCS
Journal of Plant Breeding and Crop
Science
Full Length Research Paper
QTL mapping for resistance to Cercospora sojina in
‘Essex’ × ‘Forrest’ soybean (Glycine max L.) lines
Kelsey R. McAllister, Yi-Chen Lee and Stella K. Kantartzi*
Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL, USA.
Received 7 December, 2020; Accepted 5 February, 2021
Frogeye leafspot (FLS), caused by Cercospora sojina, is observed as red-brown lesions on leaves that
can coalesce and decrease the photosynthetic ability of soybeans. The average yield loss due to
Frogeye Leaf Spot is estimated at approximately 40% in established fields, whereas 100% incidence
was previously recorded. QoI inhibitor fungicides were considered an effective control method, but the
pathogen quickly evolved an ability to thrive post-application. This trait quickly spread across North
America. Therefore, genetic host resistance is likely the most effective method to prevent the disease.
To achieve this goal, we aimed to screen 91 recombinant inbred lines (RILs) of ‘Essex’ × ‘Forrest’ under
greenhouse conditions for FLS resistance and used single nucleotide polymorphism (SNP) markers to
identify associated quantitative trait loci (QTL). Two QTL were mapped in this study. One QTL reported
on Chr. 13 coincides with the QTL previously reported, and the QTL on Chr. 19 was novel. Overall, this
study will help to better understand the underlying mechanisms of soybean resistance to C. sojina as
well as to develop soybean varieties with resistance to FLS using marker assisted selection.
Key words: Cercospora sojina, quantitative trait loci, Frogeye Leaf Spot, Essex × Forrest, disease resistance,
genotypic and phenotypic traits.
INTRODUCTION
Frogeye leaf spot (FLS), caused by the pathogen
Cercospora sojina, is a foliar disease indicated by watersoaked lesions on the leaves of soybeans. The lesions
begin as small brown spots and develop a dark, redbrown border, whereas in severe cases, they can also
form on the stems, pods, and seeds. When lesions
appear on seeds, the fungus spreads to new seedlings
the following year (Malvick, 2018). Yearly soybean losses
to FLS in the United States have been measured at 106.3
thousand metric tons, with the most losses in the
southern states (Wrather et al., 2001). In heavily infected
fields, FLS can reduce soybean yield by 40% in conducive
environmental conditions (Byamukama et al., 2019).
Together, these characteristics create a cycle of reduced
yield and reduced profits for infected fields.
The first verified case of FLS in the United States of
America was recorded in 1925 (Lehman, 1928). The
disease was particularly problematic in the southern
states for many years, with cases first recorded in the
Midwest in the late 1940s (Philips and Boerma, 1981).
For many years, chemical control, mostly using
QoIinhibitor fungicides (also known as FRAC Group 11)
was the most effective method for disease management.
FLS resistance to QoI inhibitors was detected in North
*Corresponding author. E-mail: ksmith2015@siu.edu. Tel: (618) 542-7306.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
McAllister et al.
America by 2010 (Zhang, 2012), making genetic host
resistance to FLS more crucial to high-yielding soybean
production.
Single nucleotide polymorphisms (SNPs) for disease
resistance in soybean are usually centralized on
chromosomes (Chr.) 7, 13, and 18. Chr. 13, in particular,
is known to be a rich area of disease resistance, as it
harbors the resistance gene rich Satt114 marker and the
Rsp8 gene in linkage group F on Chr. 13. This area is
associated with resistance to two races of Phytophthora
sojae, the causal agent of Phytophthora root rot (Gordon
et al., 2006). Satt114 is also commonly used as a flag
marker for other disease resistance studies (Pham et al.,
2015). However, resistance genes are not restricted to
these areas and can be scattered across the genome.
For example, SNPs that are significant to Soybean cyst
nematode resistance can be found on Chr 3, 4, 7, 9, 10,
11, 13, 14, 15, 18, 19, and 20 (Chang et al., 2016).
Currently, there are 12 known races of C. sojina and
three main genes conferring resistance. These genes are
Rcs1, which codes for resistance to race 1; Rcs2, which
provides resistance to race 2; and Rcs3, which confers
resistance to all other known races of C. sojina (Mian et
al., 2008). In 2012, two additional dominant resistance
alleles were identified as Rcs (PI 594891) and Rcs (PI
594774) (Pham et al., 2015). More research is needed in
this area to understand specific QTLs that are associated
with each resistance gene to make their implementation
more feasible for breeders.
The Essex × Forrest (E × F) cross was made in 1983 at
Southern Illinois University Carbondale (Lightfoot et al.,
2005). Essex was chosen for its partial resistance to FLS,
whereas Forrest for its partial susceptibility (Sharma and
Lightfoot, 2017). Forrest has been extensively studied
and mapped alongside Williams 82, making it an ideal
candidate line for QTL identification. Essex and Forrest
share a common germplasm pool with Forrest that
accounts for 25% of their genomes (Lightfoot, 2008).
From the initial cross, approximately 4,500 F2 plants
were advanced to F5 using single-pod descent. After
harvest, 150 F5 plants were randomly selected and
planted into progeny rows. Of these, 100 recombinant
inbred lines (RILs) were kept for various phenotypic
assays. In total, 94 RILs were used to construct a
mapping population for quantitative trait loci (QTL)
discovery and also released for research purposes
(Lightfoot et al., 2005). The plant material that used in
this study was consisted of 91 F5:8 selected RILs.
Markers closely linked to QTL can be used to screen
hundreds of lines at once for the genes of interest. For
the purpose of developing resistant cultivars, the use of
marker assisted selection is an efficient and accurate way
to identify resistant lines as opposed to large phenotypic
surveys (Yousef and Juvik, 2001). Phenotypic assays
require more labor, take longer to complete, and are less
precise compared to genotypic methods. Two major
QTLs for FLS resistance were detected in the Essex ×
15
Forrest population (E×F) for C. sojina race 2 on Chr. 7
near Satt319 and on Chr. 8 near Satt632 as well as 13
minor QTL across various chromosomes (Sharma and
Lightfoot, 2017). However, this study used simple
sequence repeat (SSR) to find regions of interest. The
use of SNP markers is more precise than SSR and is the
preferred method in genetic diversity studies (Singh et al.,
2013). For this reason, SNP was used in this study.
Having a precise location in the genome for FLS
resistance allows for simpler implementation in
commercial lines. The objectives of this study are to
analyze the phenotypic variation of FLS resistance in E×F
in a greenhouse setting, create a genetic linkage map for
the population, and identify candidate QTLs that code for
resistance to C. sojina race 15 using SNPs.
MATERIALS AND METHODS
Greenhouse assay
Greenhouse assays were conducted by planting the 91 E × F RILs
and their parental lines in six-inch plastic nursery pots filled with
Berger BM1 growing medium. Plants were watered according to
environmental needs, generally twice a week. No fertilization was
used in this experiment. Pots were arranged in a randomized
complete block design with two blocks per replication. Each block
contained one pot of each line, with the lines „Blackhawk‟ and
„Lincoln‟ placed in each block as checks. This model was replicated
twice in time, once in March 2019 and once in October 2019
comprising the EF_1 experiment. The EF_2 experiment also
consisted of two blocks per repetition, with one repetition in March
2018 and one repetition in October 2018. Seven seeds were
planted in each pot. One treatment, the application of C. sojina
spores, was applied to all blocks. Shortly after emergence, thinning
was performed to a density of one plant per pot. Plants were
inoculated for the first time with C. sojina solution at V2–V5 stages.
Plants were then inoculated a second and third time with a week
between inoculations.
Race 15 of C. sojina was cultured in petri dishes filled with
clarified V8 solid medium (Salas et al., 2007). After two weeks in a
growth chamber at 25°C, the Petri dishes were flooded with a 0.1%
Tween 20 solution and spores were knocked into the solution using
a sterilized metal spatula. Approximately eight Petri dishes of seven
colonies were used to make 300 ml of solution. The solution was
mixed thoroughly on a stirring plate for 5 min, and then was filtered
through a cheese cloth to remove mycelium. Final spore
concentration was approximately 6 × 104 conidia/ml. This final
product was poured into a spray bottle and immediately used for
inoculation.
All lines were sprayed to dripping with the fungal solution and
covered using a gallon-sized plastic bag to create a highly humid
microenvironment. Gallon-sized bags were left on for 72 h. For the
rest of the experiment, the plants were left under a humidity tent
using plastic sheeting and a humidifier. Relative humidity was
maintained at 80-90% and temperature was maintained at 28-30°C
until the end of the experimental period. Two weeks after the first
inoculation, plants were rated for disease severity using the
Newman Scale. This method allowed for characterization of disease
development over time. Plants were rated on a scale of 1-10; rating
of 1 indicates 0-10% of the leaf surface showing disease
symptoms, whereas a rating of 10 indicates 90-100% of the leaf
showing symptoms. Defoliation due to disease presence was also
counted as a 10 (Sinclair, 1982). In total, six ratings were taken
16
J. Plant Breed. Crop Sci.
within 2 wks.
DNA isolation
For DNA isolation, all lines screened in the greenhouse were
planted in six-pack trays and allowed to grow in a dark room to
minimize cuticle growth and chloroplastic DNA expression. When
plants reached the V1 stage (first trifoliate emergence), 50 mg of
tissue from the first trifoliate was collected and stored in a -20°C
freezer until isolation. Upon collection of all tissues, samples were
thawed, flash frozen with liquid nitrogen, and crushed. DNA
isolation was performed using the DNEasy 96 Plant Kit (Qiagen,
Hilden, Germany), following the manufacturer‟s instructions. DNA
purity was tested using a gel electrophoresis visualized with a 1%
EtBr stained agarose gel, and DNA quantification was carried out
with NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA). SNP
genotyping was conducted at the Soybean Genomics and
Improvement Laboratory, USDA-ARS, Beltsville, MD, using the
BARCSoySNP6K BeadChip array.
Phenotypic variation
To compare FLS resistance across the population, the sixth and
final greenhouse rating for each line was used to run a distribution
analysis. Lines with a lower FLS score than the susceptible parent
were labelled “susceptible lines” and lines with higher FLS scores
than the resistant parent were labelled “resistant lines.”
Genetic map and QTL analysis
The genetic map and QTL analysis were done with the r/QTL
package (Broman et al., 2003; Broman and Sen, 2009). The final
rating for each line was used to measure the overall FLS
resistance. Frogeye leaf spot scores were used to find phenotypic
and genotypic differences between the parental lines and the RILs.
Single marker analysis and interval mapping were used to identify
the chromosome of interests (data not shown), the Cim() function
was subsequently used for composite interval mapping (CIM). The
Fitqtl() function was used to estimate the variance of QTL of
interest, and a 1,000 permutation test was run to determine
approximate logarithm of odds (LOD) thresholds of significance
using operm.ag. The LOD thresholds, 4.44 and 4.38 was used for
95% confidence.
Gene ontology and kyto encyclopedia of genes and genomes
pathways
The SoyBase database (Wm.82 version 2) was utilized to analyze
the gene ontology (GO) and kyto encyclopedia of genes and
genomes (KEGG) pathway of the candidate QTL and identify which
proteins are coded for in the CIM interval, (Grant et al., 2010). The
UniProt Consortium database was then used to understand what
these proteins then do within the plant so that overall gene function
can be understood.
RESULTS
distribution was 0.004 and the skewness was 0.31.
Overall, the average of the FLS score was 3.23 ± 1.32,
and the scores ranged from 1 to 7.25. Five lines were
identified as more resistant than Essex (average score,
1.50 ± 0.50), whereas two lines were more susceptible
than Forrest (average score, 5.75 ± 2.49) (Figure 1).
Lines more resistant than Essex were noted as E × F 2, E
× F 9, E × F 10, E × F 11, and E × F 54 (average score,
1.0 ± 0). The lines more susceptible than Forrest were E
x F 29 (average score, 7.25 ± 1.79) and E × F 63
(average score, 6.0 ± 2.0). The distribution of the second
experiment (EF_2) was normal (P= 0.644), the kurtosis
was -0.460 and the skewness was 0.385. The average
FLS score was 3.05 ± 1.13 and the scores ranged from 1
to 5.75 (Figure 2). The FLS score for the parental lines
„Essex‟ and „Forrest‟ was 2 and 4.5. A total of 13 lines
were more resistant than „Essex‟ (average score, 1.48 ±
0.18) and 9 lines were more susceptible than „Forrest‟
(average score, 5.17 ± 0.22).
Construction of genetic linkage map
A genetic map was created with a total of 1,959 markers
across 20 chromosomes (Figure 3). The total map length
was 2121.01 cM with an average distance between
markers of 1.08 cM (Table 1). The average chromosome
length was 105.05 cM with 97.95 markers on each
chromosome. The largest chromosome was Chr. 19 with
a length of 133.66 cM and 95 markers, while the shortest
was Chr. 16 with a length of 84.27 cM and 55 markers.
The most genetically dense chromosome was Chr. 3, with
1.17 markers/cM. The gaps of < 5 cM were at a rate of
99.97%.
Identification of QTL
In EF_1, the ss715614578–ss715615158 interval
(Position: 61.81-69.27 cM) was identified to underlie FLS
resistance on chromosome 13 (LG F). A single peak was
observed at the ss715614724 marker (Position: 64.04
cM) with a LOD score of 6.36; the variation of the
phenotype explained by the QTL was 14.33%. The
beneficial allele was derived from Forrest. In EF_2, the
interval ss715634685-ss715634842 (Position: 86.7190.21 cM) was identified to underlie FLS resistance on
chromosome 19 (LG L). A single peak was observed at
the ss715634723 marker (position: 87.50 cM) with LOD
score of 6.64; the variation of the phenotype explained by
this QTL was 14.72%. The beneficial allele was derived
from Essex (Table 2).
Phenotypic variation
Resistance
The distribution of FLS scores across the first experiment
(EF_1) was normal (P = 0.158), the kurtosis of the
The genotypes of RILs that were more resistant than
McAllister et al.
17
Figure 1. Histogram depicting the frequency of FLS scores across the first experiment (EF_1).
Figure 2. Histogram depicting the frequency of FLS scores across the second experiment (EF_2).
Essex were found to have a Forrest-like genotype at
ss715614724 (Table 3), whereas those that were more
susceptible than Forrest to have Essex-like alleles at the
same location. These results suggested that Forrest was
the parent contributing to the QTL of resistance. To
confirm this hypothesis, one-way ANOVA was conducted
comparing FLS scores of all RILs (n=81). This test
compared lines with Forrest-like alleles, Essex-like
alleles, and recombinant genotypes (Figure 4). The
ANOVA test was statistically significant to 95% confidence
(F2,80 = 7.64, P < 0.0009). Lines with Forrest- like alleles
had mean FLS ratings 1.15 smaller, which equates to
18
J. Plant Breed. Crop Sci.
Figure 3. Genetic linkage map of E × F population.
Table 1. Characteristics of genetic map of E × F population
Chromosome
Number of
markers
Genetic distance
(cM)
Average distance
between markers (cM)
Gaps ≤ 5 (%)
Maximum gap
(cM)
76
81
128
109
94
128
90
98
76
91
87
80
163
85
113
55
84
150
76
95
1959
126.05
115.46
108.79
98.64
94.61
114.33
101.99
99.97
96.45
114.06
89.45
91.25
94.47
110.02
114.66
84.27
94.97
133.35
133.66
104.56
2121.01
1.68
1.44
0.85
0.91
1.01
0.90
1.14
1.03
1.28
1.26
1.04
1.15
0.58
1.30
1.02
1.56
1.14
0.89
1.51
1.11
1.08
90.78
97.53
98.43
99.08
96.80
98.43
96.66
96.93
96.05
95.60
95.49
97.50
99.38
95.29
99.11
98.18
95.23
98.66
94.73
94.73
99.97
26.33
30.13
16.48
9.90
53.42
41.76
19.76
45.04
24.39
22.35
13.28
33.12
6.07
18.32
71.47
55.73
24.60
13.95
42.00
17.67
71.47
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Total
Table 2. Location of the QTL mapped in this study.
Interval
LG/Chr
Position of
interval (cM)
Position (cM)
LOD
ss71561457–ss715615158
ss71563468–ss715634842
F/13
L/19
61.81-69.27
86.71-90.21
64.04(ss715614724)
87.50(ss715634723)
6.64
6.36
R2
(%)
14.33
14.72
FLS mean
Essex
Forrest
1.50 ± 0.50
5.75 ± 2.49
2.50 ± 0.21
3.53 ±0.18
McAllister et al.
19
Table 3. Genotyping results at marker of interest.
Line
E×F2
E×F9
E × F 10
E × F 11
E × F 29
E × F 54
E × F 63
Essex
Forrest
FLS score
1
1
1
1
7.25
1
6
1.5
5.75
Level
Essex
Recombinant
Forrest
Genotype at ss715614724
C
C
C
C
T
C
T
T
C
Grouping
T
TC
C
Mean
3.90
3.25
2.75
Figure 4. One-way ANOVA comparing genotypes at ss715614724 (F2,80=7.64, P<0.0009).
approximately 11.5% less foliar damage, compared to
Essex-like alleles. Heterozygous lines were not
statistically different from either Forrest-like or Essex-like
lines.
GO and KEGG pathways
Within ss715614578–ss715615158 (Chr.13),
a wide
variety of genes has been published and identified (Table
4) (Nelson et al., 2010). The nearest gene to the peak at
ss715614724 are the BT089187.1 and M31024.1 genes,
both of which code for ribosomal protein S11. This
protein resides within the cytosolic small ribosomal
subunit and plays a major role in rRNA binding and
overall ribosomal structure. A total of 9 genes have been
published and identified within the ss715634685–
ss715634842 interval, an NBS-LRR disease resistance
20
J. Plant Breed. Crop Sci.
Table 4. All published genes in the ss715614578-ss715615158 interval on chromosome 13.
Gene
BT096972.1
FJ014823.1
BT093809.1
FJ014792.1
GQ422779.1
BT089187.1
M31024.1
BT097035.1
BT097614.1
BT094321.1
DQ468343.1
CYP93C1v2
ifs2
CYP93C1
FJ014793.1
KC876033.1
BT089855.1
AK244336.1
BT099462.1
DQ857259.1
BT096749.1
BT094501.1
BT097216.1
BT098969.1
AK285956.1
BT094395.1
BT095720.1
Protein
ABC transporter/family member 1-like
protein kinase
calmodulin-like protein 5-like
calmodulin-binding receptor-like cytoplasmic kinase
bifunctional purple acid phosphatase 26-like
ribosomal protein S11
ribosomal protein S11
pre-rRNA-processing protein TSR2 homolog
CASP-like protein N24-like
formate dehydrogenase 1, mitochondrial-like
SNI1
cytochrome P450 monooxygenase CYP3C1v2p
isoflavone synthase 2
isoflavone synthase 2
receptor-like protein kinase HSL1-like
Drought-induced family protein
17.5 kDa class I heat shock protein-like
mediator-associated protein 2-like
mediator-associated protein 2-like
Dof9
40S ribosomal protein S6-like
probable RNA 3'-terminal phosphate cyclase-like protein-like
epoxide hydrolase 2-like
monoglyceride lipase-like
secretory carrier-associated membrane protein-like
secretory carrier-associated membrane protein-like
putative 12-oxophytodienoate reductase 11-like
protein (Table 5) (UniProt Consortium, 2020).
DISCUSSION
The parents of the E×F population were scored for FLS
resistance. Forrest received an FLS score 2.3-fold higher
than Essex in EF_1 and 2.3-fold higher 2.3-fold higher
than Essex in EF_2, confirming that Forrest is more
susceptible against C. sojina race 15. These results
aligned with those presented in prior studies on
resistance to race 2 (Sharma and Lightfoot, 2017). Since
our histogram fit the normal distribution, the skewness
was near zero, suggesting that the segregation equally
contributed to high and low FLS scores.
A single QTL associated with FLS resistance was
identified on Chr. 13 at the ss715614578–ss715615158
interval, which coincides with the region of SNP41647
that is known for Rcs (PI594891) in linkage group F
(Pham et al., 2015). PI594891 is a Chinese plant
introduction, and its resistance pathway is not yet well
documented (Hoskins, 2011). Our QTL could be allelic to
Rcs (PI594891). It is believed that this resistance gene is
conditioned by Rcs3, but it likely carries different
resistance alleles from one or two other genes (Pham et
al., 2015). Another QTL associated with FLS resistance
was identified on Chr. 19 at the ss715634685–
ss715634842 interval; this QTL has not been reported.
In the present study, Forrest contributed the resistance
allele in EF_1 whereas Essex contributed the resistance
allele in EF_2. The results in EF_1 is contradictory to
prior studies on race 2, in which Essex donated the
resistance allele (Sharma and Lightfoot, 2017). Since
Rcs2 generally confers resistance to race 2, we assumed
the existence of a different resistance mechanism for
race 15. Although it seems counterintuitive for Forrest to
donate the resistant allele, it might be possible since
Forrest was only partially susceptible. The use of only
Race 15 of C. sojina may have also played a role in this
finding. More research should be conducted on which
specific races Forrest is susceptible to. It is possible
Race 15 is one that Forrest holds resistance for. Many
priorly conducted resistance tests use mixed races, which
can skew results when individual races are used.
McAllister et al.
21
Table 5. All published genes in the ss715634685-ss715634842 interval on chromosome 19.
GenBank ID
X62303.1
DQ822926.1
BT096531.1
KC344383.1
DQ787047.1
X16352.1
BT093250.1
BT099160.1
EU888329.1
Protein
mitotic cyclin
MYB transcription factor MYB142
10 kDa chaperonin-like
auxin efflux carrier component 1-like
bZIP transcription factor bZIP96
pyrroline-5-carboxylate reductase (AA 1–274)
ganglioside-induced differentiation-associated protein 2-like
two-component response regulator ARR9-like
NBS-LRR disease resistance protein
In this study, the suggested QTL was minor, contributing
14.33 and 14.72% of variance, probably due to the low
disease pressure across the experiments. Therefore,
differences among genes of small effect might not have
been identified. Future research is needed under field
conditions with relatively high disease pressure to confirm
the presence of the QTL and identify any interaction with
the environment. Besides, the use of mixed races or
other individual races of C. sojina would be also
beneficial to better understand the underlying mechanism
of resistance and the role of the QTL. Marker
ss715614724 could be used in future breeding projects to
fine-tune marker-assisted selection for resistance to FLS.
The QTL in EF_1 was found to be associated with
ribosomal S11. In soybeans, it was found that ribosomal
S11 was significantly elevated when immature plants
were treated with 2,4 D (Gantt and Key, 1985). Since this
study, the presence of S11 has been associated with
cellular proliferation. It is abundant in meristematic tissue
and allows the plant to produce new cells efficiently
(Lenvik et al., 1994). To this end, we can hypothesize
that the found SNP alters the amount of S11 produced in
the plant and allows it to overcome damage from C.
sojina. An NBS-LRR disease resistance protein was
identified within the ss715634685–ss715634842 interval
on Chr. 19; these proteins serve as a protein interaction
platform and may lead to cell death (Belkhadir et al.,
2004). This protein may contribute to the FLS resistance
in soybean.
According to SoyBase, the nearest published gene to
the ss715634723 marker noted in EF_2 is associated
with the CYP98A2 and AK287176.1 genes, both of which
code for cytochrome P450-98A2. Its function is in metal
binding and it performs oxidoreductase activities (The
UniProt Consortium, 2020). Cytochrome P450 enzymes
are a large class of monooxygenases that aid in various
plant functions from biosynthesis of pigments to plant
hormone production. Most famously, cytochrome P450
degrades herbicides, insecticides, and pollutants
whenever introduced to the plant (Guttikonda et al., 2010).
Further research should be done to formally conclude
how this gene could be functioning in a way to provide
protection from C. sojina.
Conclusions
In summary, we report a QTL that is related to Rcs
(PI594891) and production of the S11 ribosomal protein
that aids in cell proliferation and a novel QTL on chr. 19
associated with Cytochrome P450-98A2. The associated
marker ss715614724 and ss715634723 could be used in
future projects to stack resistance genes for FLS.
Environment played a large part in our experiments, and
future studies should be conducted with higher and more
consistent disease pressure to determine if the identified
QTL could confer a higher percentage of resistance.
Overall, Forrest and its derivatives are a good source for
the advancement of FLS resistance in soybean.
CONFLICT OF INTERESTS
The authors have not declared any conflict of interests.
ACKNOWLEDGMENT
This study was fully funded by the United Soybean Board.
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