Introduction
Curvularia
leaf spot is a significant foliar disease of
maize that occurs in all maize cultivation regions (Liu et al. 2015a),
but especially in north and northeast China, the main maize cultivation regions
in China. This disease is caused by the fungus Curvularia lunata, and
its severity depends on whether the climatic conditions are favorable for
fungus development. The fungus can overwinter in the debris of diseased corn
plants left on the soil surface, and conidia produced in the following spring
can be spread by wind or splashing of droplets during rain events. The disease
is prevalent in areas where dewy mornings are followed by hot humid afternoons
and relatively cool nights. The disease occurs mainly during the maize
reproductive period and damages the whole plant, which decreases quality and yield.
In 1999, an outbreak of maize Curvularia leaf spot in Liaoning Province
resulted in the infection of nearly 17,500 hectares of maize, which decreased
maize production by 250 million kg (Li et
al. 2002).
The development of maize cultivars resistant to Curvularia leaf spot through conventional breeding is one way to
control the disease and ensure the security of corn production in China.
However, conventional breeding of
Curvularia leaf spot resistant cultivars has been difficult because of
the complexity of the resistance trait. Although Curvularia leaf spot resistance is a highly heritable trait, it
is controlled by many minor quantitative trait loci (QTLs) (Wang et al.
2014; Dong et al. 2015). Improvements in cultivation methods (Li and Mo
2015) and field management strategies have effectively reduced the incidence of
Curvularia leaf spot in maize (Zhao et al. 2001). Although Curvularia leaf spot is now
well contained in China, the combined effects of climate change, increasing
areas of corn monocultures, and the narrow North Chinese resistant germplasm
means that the disease still poses a serious threat to Chinese corn production.
Previous studies on the pathogenic mechanisms of C.
lunata have identified and characterized some of the important genes and
determined which pathways are involved in pathogenesis (Liu et al. 2016;
Gao et al. 2017). However, relatively few studies have focused on the
location of resistance genes in maize, and the mechanisms of maize disease
resistance. Several previous studies have identified QTLs associated with
resistance to Curvularia leaf spot, but
research on their effects has obtained inconsistent results (Liu et al. 2009; Hou et al. 2013; Liu et al. 2015b). To date, no conserved QTL
regions associated with Curvularia leaf
spot resistance under different environments have been found.
Materials and Methods
Plant materials and field trials
The maize cultivar Ma 664 was
selected by the breeder Yi-yong Ma (Jilin Agricultural University). This
cultivar is strongly resistant to C. lunata (the disease showed the
lowest grade, grade I, in a field trial in 2014). The cultivar H4074 was
selected by the breeder Shu-yan Guan (Jilin Agricultural University). This
cultivar is highly susceptible to C. lunata (disease grade 7 in the
field trial). Therefore, a mapping population of 239 F2
individuals was derived from the cross between Ma664 and H4074. In 2015, the
239 F2 individuals, the two parent lines, and the F1
generation were planted in Changchun, Jilin Province. In 2016, the F2:3 families
derived from selfed F2 individuals were planted in Gongzhuling and
Changchun, Jilin Province. Hereafter, the three groups constructed above are
abbreviated as follows: F2, Rep1, and Rep2. At each location, the field
experiment had a randomized complete block design with three replications for
each genotype. Maize was planted with row lengths of 5 m, inter-plant spacing
of 25 cm, and row spacing of 60 cm. There was a protective line around each
plot, and conventional field management was employed.
Trait evaluation
The F2 population and
two F2:3 family populations (Rep1, Rep2) at the 12–13 leaf stage
were inoculated with C. lunata (provided by the Jilin Academy of
Agricultural Sciences). The
spore concentration of the inoculum was adjusted to 10–15 spores per field of
view under 100x magnification. The inoculum was
sprayed onto both sides of whole maize leaves until the solution dripped from
the leaves. The evaluation criteria for the disease level were those specified
by Hou et al. (2013). The incidence
of different disease levels is shown in Fig. 1, and the different disease
levels are described in Table 1.
Phenotypic data analysis
Pairwise comparisons of means of
the parents’ disease grades were tested for significance with t-tests
implemented in SPSS 25 software (http://www.ibm.com/legal/copytrade.shtml). The
phenotypic data of the F2 population and populations of the two F2:3
families were tested by Descriptive Statistics of S.P.S.S. 25 for normal
distribution, where absolute values of kurtosis and skewness of less than 1
confirmed normal distribution.
Molecular data collection and
linkage map construction
The DNA was extracted from the
plant materials as described by Mu et al.
(2010). Simple sequence repeat (SSR) markers covering the entire genome were
selected from the maize genome database (http://www.maizegdb.org/) and screened
to identify those that were polymorphic between the two parents. These markers
were used to genotype the mapping population (F2 population). Marker
linkage analysis and construction of linkage maps were conducted using Ici Mapping 4.0. A limit of detection (LOD) threshold of 2.5 was
used to assign markers to the same linkage group. The observed frequencies at
each marker were tested against the expected Mendelian segregation ratio of
1:2:1 using a K2 test for goodness of fit.
QTL analysis
The DNA extracted from members of
the F2 generation was subjected to PCR amplification and capillary
electrophoresis detection using the selected markers. This procedure was used
to genotype all members of the F2 generation. The phenotypic data
for the F2:3 populations and the SSR marker molecular linkage map
information were used to identify QTLs using IciMapping 4.0. We used the
inclusive composite interval mapping (ICIM) method for single-environment QTL
mapping of traits. A
QTL was considered to be significantly correlated with resistance to Curvularia
Leaf Spot when the LOD score was greater than 2.5. The genetic effects and
phenotype contribution rates were analyzed. Each QTL was scored according to
its dominance ratio (DR; DR = | d | / | a |). Thus, when DR < 0.2, the QTL
was additive; 0.2 < DR < 0.8, the QTL was partially dominant; 0.8 < DR
< 1.2, the QTL was dominant; and DR > 1.2, the QTL was super-dominant.
Epistatic effects of QTLs were analyzed using the ICIM with epistatic
interactions (ICIM-EPI) method.
Results
Genotype
analysis and construction of maize genetic linkage map
We
tested 650 SSR primer pairs and found that 150 pairs (23.08%) were sufficiently
polymorphic between the two parents. The genotype data for the F2
population were analyzed using SPSS software. The separation of 74.7% SSR
markers in the F2 population was consistent with a 1:2:1 segregation
ratio. Of the 150 SSR polymorphic markers, 38 (25.3%) showed segregation
distortion. Of those, 20 markers (52.6%) were biased to the female parent, 11
(28.9%) were biased to the male parent, and 7 (18.4%) were biased to the F1
generation.
The
markers were unevenly distributed among linkage groups, and the distances
between markers ranged from 6.25 and 29.70 cM, with no large gaps Fig. 4. The relative order of the
markers was consistent with that on the genetic linkage map at the Maize GDB
database, which indicates that our data conformed to QTL positioning
requirements. The lengths, numbers, and distances between SSR markers are shown
in Table 2.
Table 1: Evaluation criteria of Curvularia Leaf Spot
disease grades
Disease grade |
Description of corresponding
disease grade |
Resistance level |
1 |
No lesions or only sporadic
lesions, area of lesions accounts for less than 5% of leaf area |
Highly resistant (HR) |
3 |
A few lesions, area of the
lesions accounts for 6%–10% of the leaf area. |
Resistant (R) |
5 |
More lesions, accounting for
11%–30% of the leaf area |
Middle resistant (MR) |
7 |
Many lesions, some connected,
accounting for 31%–50% of the leaf area. |
Susceptible (S) |
9 |
Whole plant is covered with
disease spots, lesions are connected and account for more than 50% of the
leaf area. Leaves die in late stages of the disease. |
Highly susceptible (HS) |
Table 2: Lengths, numbers, and distances between SSR markers
for each maize linkage group
|
Linkage groups |
Average |
Total |
|||||||||
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
|||
Chain length (CM) |
243.0 |
199.5 |
180.5 |
187.5 |
175.9 |
131.2 |
162.0 |
144.5 |
139.6 |
140.0 |
170.4 |
1703.7 |
Number of linkage groups |
15 |
13 |
10 |
11 |
13 |
10 |
11 |
10 |
10 |
9 |
11.2 |
112 |
Average spacing (CM) |
16.2 |
15.3 |
18.1 |
17.0 |
13.5 |
13.1 |
14.7 |
14.5 |
14.0 |
15.6 |
15.2 |
|
Fig. 1: Disease grades of Curvularia
Leaf Spot in maize.
Phenotype
analysis
The
incidence of resistance was significantly different between the two parental
populations, as determined by independent samples t-tests (Table 3). This
confirmed that these parents were suitable for the construction of populations
for mapping QTLs related to resistance to Curvularia leaf spot.
The distribution of disease grades
in the F2 and F2:3 families is shown in
Table 4. The disease grades were distributed evenly (1, 3, 5, 7 and 9),
indicative of a continuous distribution of the resistance phenotype. This
confirmed that the resistance of maize to Curvularia leaf spot is a
quantitative trait controlled by multiple genes.
A descriptive statistical analysis
of the F2 and two F2:3 populations was
conducted. These analyses (Table 4 and 5) showed that 90% of the F2
population had a disease level between those of the parents [average value of
5.52, skewness of 0.155, peak of 0.062, and standard deviation (SD) of 1.78].
The overall disease incidence in the population was consistent with a normal
distribution (Fig. 2A). For the F2:3 family Rep1 group, 98% of the
population had a disease level between those of the parents (average 4.85,
skewness 0.062, kurtosis 0.614, and SD 1.49). The overall disease incidence in
the population showed a normal distribution (Fig. 2B). For the F2:3
family Rep2 group, 98% of the population had a disease level between those of
the parents (average 4.90, skewness 0.143, kurtosis −0.037, and SD 1.50).
The overall disease incidence in the population showed a normal distribution
(Fig. 2C). These results showed that resistance to Curvularia Leaf Spot in
maize is a quantitative trait under polygene control, and confirmed that the
population was suitable for QTL mapping.
QTL
positioning
Table 3: T-tests of disease incidence levels between
parents
Year |
Levene’s test for equality of variances |
|
T-test for equality of means |
|
|
F |
Sig |
T |
Sig |
2015 |
0.924 |
0.345 |
-19.008 |
0.000 |
2016 |
7.338 |
0.011 |
-16.310 |
0.000 |
Table 4: Disease grade distribution in maize parents, and F1,
F2 and F2-derived populations
Year |
Generation |
Disease grade |
Total plants |
|||||
1 |
3 |
5 |
7 |
9 |
||||
2015 |
P1 |
12 |
3 |
0 |
0 |
0 |
15 |
1.40 ± 0.214 |
P2 |
0 |
0 |
3 |
13 |
0 |
15 |
6.73 ± 0.182 |
|
F1 |
0 |
5 |
10 |
0 |
0 |
15 |
4.33 ± 0.252 |
|
F2 |
5 |
32 |
122 |
56 |
24 |
239 |
5.52 ± 0.115 |
|
2016 |
P1 |
13 |
2 |
0 |
0 |
0 |
15 |
1.27 ± 0.182 |
P2 |
0 |
0 |
5 |
10 |
0 |
15 |
6.33 ± 0.252 |
|
F1 |
0 |
6 |
9 |
0 |
0 |
15 |
4.20 ± 0.262 |
|
F2:3 rep1 |
6 |
53 |
138 |
37 |
5 |
239 |
4.85 ± 0.966 |
|
F2:3 rep2 |
3 |
60 |
126 |
46 |
4 |
239 |
4.90 ± 0.969 |
Table 5: Descriptive statistics of F2
population and its two derived F2:3 families
Mean ± SE |
Standard deviation |
Skewness ± SE |
Kurtosis ± SE |
|
F2 |
5.52 ± 0.115 |
1.78 |
0.155 ± 0.157 |
0.062 ± 0.314 |
F2:3rep1 |
4.85 ± 0.966 |
1.49 |
0.062 ± 0.157 |
0.614 ± 0.314 |
F2:3rep2 |
4.90 ± 0.969 |
1.50 |
0.143 ± 0.157 |
-0.037 ± 0.314 |
Table 6: Results of QTL mapping for resistance to Curvularia lunata (ICIM
LOD>2.5)
Population |
Name |
Bin |
Position |
Distance to markers |
Marker interval |
LOD score |
PVE(%) |
Additive effect |
Dominant effect |
Gene action |
|
Left |
Right |
||||||||||
F2 |
qCLS1.10 |
1.10 |
212.00 |
0.22-11.75 |
bnlg1347a |
umc1862 |
3.65 |
4.87 |
-0.26 |
-0.68 |
OD |
qCLS3.08 |
3.08 |
130.00 |
26.86-2.84 |
umc2269 |
bnlg1108 |
8.78 |
14.00 |
-0.92 |
0.24 |
PD |
|
qCLS8.01 |
8.01/8.02 |
26.00 |
7.12-11.29 |
umc1483 |
umc1913 |
4.29 |
7.05 |
-0.67 |
-0.04 |
A |
|
qCLS10.04 |
10.04 |
75.00 |
0.15-19.77 |
mmp121 |
umc1506 |
10.48 |
14.42 |
-0.90 |
-0.40 |
PD |
|
Rep1 |
qCLS1.02 |
1.02 |
29.00 |
13.02-3.96 |
bnlg1014 |
umc1467 |
4.64 |
8.90 |
-0.58 |
0.34 |
PD |
qCLS5.07 |
5.07 |
139.00 |
0.28-10.83 |
umc1375 |
umc2013 |
4.40 |
6.69 |
-0.55 |
0.11 |
A |
|
qCLS7.01 |
7.01/7.02 |
38.00 |
5.73-10.43 |
umc1270 |
bnlg1247 |
2.51 |
4.55 |
0.45 |
-0.03 |
A |
|
qCLS10.04 |
10.04 |
75.00 |
0.15-19.77 |
mmp121 |
umc1506 |
7.53 |
11.60 |
-0.68 |
-0.29 |
PD |
|
Rep2 |
qCLS2.10 |
2.10 |
193.00 |
7.54-6.53 |
bnlg1893 |
umc2214 |
3.97 |
5.21 |
-0.48 |
-0.06 |
A |
qCLS5.03 |
5.03 |
62.00 |
0.44-15.54 |
umc1705 |
bnlg1902 |
8.56 |
10.62 |
-0.68 |
-0.19 |
PD |
|
qCLS6.05 |
6.05 |
72.00 |
2.05-16.45 |
umc2141 |
bnlg1732 |
3.68 |
4.60 |
-0.17 |
-0.58 |
OD |
|
qCLS9.01 |
9.01 |
7.00 |
7.00-3.11 |
umc1957 |
umc1867 |
4.67 |
6.03 |
-0.45 |
0.36 |
PD |
|
qCLS10.04 |
10.04 |
75.00 |
0.15-19.77 |
mmp121 |
umc1506 |
14.75 |
19.00 |
-0.90 |
-0.21 |
PD |
Note:
Bin indicates the corresponding Bin interval on the MaizeGDB
map of the QTL; Position indicates QTL position on chromosome (CM); "a-b" in the “Distance to markers” column
indicates that the distances of the QTL locus to left and right marks is a and
b respectively(CM) ;“Left” and “Right” of the Marker interval indicates the
left and right markers of the QTL interval; LOD indicates log10 of the
likelihood odds ratio; PVE(%)represents the phenotypic variance
percentage that owe to the corresponding QTL.A, D, PD and OD in the “Gene
action” column represent additive effects, dominant effects, partial dominance
effects and super-dominant effects, respective
Fig.
2: Disease grade distribution in F2
population and its F2:3 families.
Four
QTL loci were detected by genome-wide scanning of the F2 population,
based on a combination of genotypic and morbidity data. Single QTLs were
located on chromosomes 1, 3, 8, and 10 (Fig. 3). The whole genome was analyzed
by ICIM-EPI with LOD > 2.5 as the unit. Epistatic interactions among QTLs
were not detected. The names, numbers, and effects of QTLs related to leaf spot
resistance in the F2 population are shown in Table 6. The detected
QTLs accounted for 40.33% of the phenotypic variation in resistance. The QTLs
in the intervals umc2269–bnlg1108 (on chromosome 3) and mmp121–umc1506 (on chromosome
10) made the largest contributions to phenotypic variance (14.00% and 14.42%,
respectively). The QTLs associated with the bnlg1347a–umc1862 interval
accounted for 8.77% of phenotypic variation (LOD 3.65). The QTL located on umc1843–umc1913
on chromosome 8 accounted for 7.05% of phenotypic variation (LOD 4.29). The
additive effect of each QTL was negative, which indicates that all the QTLs
related to low disease incidence were from the resistant parent Ma 664. According to the DR ratio proposed by Stuber et
al. (1992), qCLS1.10 (DR = 2.62), qCLS3.08 (DR = 0.26), qCLS8.01 (DR =
0.06) and qCLS10. 04 (DR = 0.44) were super-dominant, partially dominant,
additive, and partially dominant, respectively.
Fig. 3: QTL mapping results for each maize population
Fig. 4: Maize genetic linkage map showing distribution of
QTLs for resistance to Curvularia Leaf Spot in maize
Four QTL loci (Fig. 3) located on
chromosomes 1, 5, 7, and 10 were detected in the F2:3 Rep1 population and accounted for 31.74% of the phenotypic
variation in resistance. As shown in Table 6, except for qCLS7.01 (in the
umc1270–bnlg1247 interval on chromosome 7), the other QTLs had negative
additive effects, which indicates that they were derived from the
disease-resistant parent Ma 664, while qCLS7.01 was inherited from the female
parent H4074. The QTLs qCLS10.04 (DR = 0.43) and qCLS1.02 (DR = 0.59) were
partially dominant, and accounted for 8.9% and 11.60% of the phenotypic
variation in resistance, respectively. The qCLS7.01 (DR = 0.07) locus located
at umc1270–umc2013 in the umc1375–umc2013 interval and the umc1270–bnlg1247
interval on chromosome 7 showed additive effects, and accounted for 6.69% and
4.55% of the phenotypic variation in resistance, respectively. Among the QTLs,
qCLS10.04 on chromosome 10 was detected in the F2:3 Rep1 and F2
populations, and was partially dominant. The IGEM-EPI algorithm was used to
analyze the epistatic interactions of the whole genome, and no epistatic
interactions were detected among these QTLs.
Five QTLs (on chromosomes 2, 5, 6,
9, and 10) were detected in the F2:3 Rep2 population (Fig. 3) and
accounted for 45.46% of the phenotypic variation in resistance. As shown in
Table 6, all of the QTLs had negative additive effects, which indicates that
the QTLs from the male parent reduced disease incidence and improved
resistance. The qS2.10 locus on chromosome 2 was an additive effect QTL (DR =
0.13) located at bnlg1893–umc2214 (LOD 3.97) that accounted for 5.21% of the
phenotypic variation. The qCLS6.05 locus on chromosome 6 was super-dominant (DR
= 3.41), was located at umc2141–bnlg1732 (LOD 3.68), and accounted for 4.60% of
the phenotypic variation. There are still major obstacles for using
super-dominant QTLs in crop breeding. Therefore, this QTL needs to be further
investigated. The QTLs qCLS9.01 (DR = 0.8) and qCLS10.04 (DR = 0.23) on
chromosomes 9 and 10, respectively, were partially dominant. They were located
at umc1957–umc1867 and mmp121–umc1506, respectively (LOD 4.67 and 14.75,
respectively) and accounted for 6.03% and 19.00% of the phenotypic variation,
respectively. qCLS10.04 on chromosome 10 was detected
in the F2 and F2:3 Rep1 populations and had the same
genetic effect in both populations. It had high LOD values and phenotypic
variation contribution rates; therefore, we consider that this is a stable QTL
related to resistance to Curvularia Leaf Spot in maize. This QTL can serve as
the starting point to identify candidate resistance genes through fine
positioning mapping. The IGEM-EPI algorithm was used to analyze the epistatic
interactions of the whole genome, and no epistatic interactions were detected
among the analyzed QTLs.
Discussion
A consistent environment is
required to accurately assess the potential of plant genotypes to resist the
onset and progress of Curvularia leaf
spot, and to determine the magnitude of the genetic factors that
contribute to resistance. This is because the development of Curvularia leaf spot is extremely
sensitive to environmental conditions. In this QTL mapping study, we obtained
phenotypic data for Curvularia leaf
spot resistance of maize in two years (2015 and 2016) at sites in Changchun
and Gongzhuling. In both years, the summer was humid and relatively hot. These
environmental conditions made it possible to assess the level of Curvularia leaf spot resistance in
the segregating populations.
We conducted interval mapping at the LOD threshold of 2.5, and detected 11
QTLs related to resistance to Curvularia
leaf spot. Of the three main QTLs (at Bin3.08, Bin5.03, and Bin10.04),
the QTL on chromosome 10 was consistently detected in three environments. In another study, a stable QTL for resistance to
Curvularia leaf spot was detected at
the same site (Bin10.04) in analyses of an F2:3 family
population of Shen 137×Huangzao 4 (Hou
et al. 2013). The results of that
study and our study indicate that Bin10.04 on chromosome 10 is a stable main
QTL for resistance to Curvularia leaf
spot.
The additive and dominant effects of QTLs can differ among various genetic
backgrounds and/or among the same materials in different years. The additive
and dominant effects of the consistent QTL located in Bin10.04 differed between
the two years and the three environments, but this QTL was inherited
dominantly, which is consistent with the findings of Hou et al. (2013). All of the QTLs detected in this study had different
additive and dominant effects, but were predominantly additive and partially
dominant. Zhao et al. (2002) studied the inheritance of
resistance to Curvularia leaf spot using the ADAA model and found that the
resistance of maize was mainly additive and dominant.
When we searched the Maize-GDB database, we did not find any Curvularia
leaf spot resistance-related QTLs in the marker interval corresponding to the
QTL loci located in this study. However, we found that qCLS1.10
(bnlg1347a–umc1862), located in this study, is located in a sugarcane borer resistance
QTL region (bnl8.29a–umc106a), and the umc2269–bnlg1108 interval of qCLS3.08
partially overlaps with igc3b–umc63a, a QTL associated with resistance to the
European corn borer. In addition, qCLS5.03’s umc1705–bnlg1902 interval contains
a gray leaf spot resistance-related QTL (near umc43). Thus, this area may
represent a large QTL marker interval that includes many different QTL or the
loci targeted in this study, which may be multi-effect QTL.
In this study, the F2 population and F2:3 families
were used as locating groups, and F2 was used as the mapping
population. The phenotypic values of several individuals were substituted for
the F2 representative values, thereby reducing the effects of
environmental factors on plant traits and enabling the repeated trial of
multiple points. Hou et al. (2013)
noted that self-crossing of F2 individuals yields F2:3
families with reduced heterozygous genotypes, resulting in low estimates of
QTL-associated dominant effects. Here, we compared the genetic effects of the
consistent QTL in Bin10.04 in the F2 population and two F2:3
families, and we reached the same conclusion. In
studies on maize QTLs, the F2:3 phenotypic mean has often been used
instead of the F2 phenotype to account for the shortcomings of the F2
generation (Lu et al. 2002; Park et al. 2013; Hou et al.
2015; Liu et al. 2016). Those studies identified genetic loci associated
with important agronomic traits of maize using F2:3 families as the
locating populations and successfully mapped stable QTLs. Here, we located QTLs
for Curvularia leaf spot resistance in maize that stably exists in different
environments. This confirmed the feasibility of using F2:3 families
as QTL-locating populations.
Segregation distortion skews the genotypic frequencies from their Mendelian
expectations (Lu et al. 2002). In this study, 38 (25.3%) of 150 of polymorphic markers showed segregation
distortion in the F2
population. Of these 38 markers, 20 (52.6%) were biased towards the female
parent, 11 (28.9%) were biased towards the
male parent, and seven (18.4%) were biased toward the F1. This is consistent with the results
reported by Lu et al. (2002). Since Mangelsdorf and Jones (1926) first reported segregation distortion
in maize, many researchers have detected this phenomenon when studying maize
linkage maps (Bentolila et al. 1992; Gardiner et al. 1993;
Murigneux et al. 1993; Pereira and Lee 1995). Liu and Yang (2015)
constructed a maize genetic linkage map and found that 12 (33.3%) out of 31
polymorphic markers showed segregation distortion; one (8.33%) was biased to the male parent, two (16.67%) were
biased towards the heterozygote, and five (41.67%) were unbiased. There are
many reasons for the segregation distortion of molecular markers. Lu et al. (2002)
studied the segregation of SSR molecular markers in maize and found that most
chromosomes have functional genes that cause segregation distortion of markers.
This affects the normal separation of alleles and determines the direction of
segregation distortion. The ratios of marker segregation distortion are
positively correlated with the generation of the population. This is because
there is unequal selection between male and female gametes in the process of
meiosis and combination of gametes. Molecular
markers that show segregation distortion are located in particular regions of
chromosomes. In this study, markers on all chromosomes showed segregation
distortion, and those showing segregation distortion were located in certain
hot spots on chromosomes. The segregation distortion of molecular markers leads
to inconsistencies between the marker recombination rate and the genetic
distance of the marker, which reduces the accuracy of the genetic linkage map.
Some studies have reported that segregation distortion of molecular markers has
little effect on the location of loci (Hackett and Broadfoot 2003; Zhang et
al. 2010). However, we found that the segregation distortion of markers
introduced errors into the genetic linkage map, so that the relative map
position of the molecular marker was different from that in Maize-GDB.
Therefore, when this occurred, the marker was removed from the genetic linkage
map to reduce mapping errors.
The construction of a high-density and precise genetic linkage map is a
prerequisite for the accurate detection of QTLs. In this study, we detected
stable differences between parents and the separation of F2
populations in accordance with the 1:2:1 genotypic ratio of 112 SSR markers,
which were divided into 10 linkage groups. A linkage map of maize molecular
markers was constructed. The relative sequences of the markers were consistent
with the genetic linkage map at the Maize-GDB database. The total length of the
map was 1,703.7 cM and the average density was 15.2 cM, in accordance with
QTL-locating requirements. With the application of single nucleotide (SNP)
molecular marker technology in maize gene mapping, more precise genetic linkage
maps have been constructed. These maps have made it easier to fine-map maize
QTLs (Zou and Song 2003; Pan et al. 2011; Warburton et al. 2015; Song
et al. 2017).
Molecular marker-assisted screening of stress-resistant crops is extremely
important and is a very effective breeding method. The QTL detected in Bin10.04
in this study has potential uses in marker-assisted selection in breeding, but
further research is required to identify the gene(s) responsible for
resistance. It can be risky to use QTL-linked markers for breeding when their
effects on other agronomic traits are unknown, or when the mechanism of the
genetic effect is unclear (Tanksley and Hewitt 1988). Epistatic effects can
also influence molecular marker-assisted selection. Vasal et al. (1970)
found that epistatic interactions, especially those among dominant genes, are
major genetic effects related to leaf spot resistance. Epistatic interactions
among QTLs were not detected in this study. Appropriate combinations of
populations and genetic mating design are essential to accurately detect the
presence of epistatic effects among QTLs. Therefore, further research to
analyze the genetic effects of QTL at Bin10.04 and the interaction with the
environment are of great significance for the molecular marker-assisted
selection of maize lines resistant to Curvularia leaf
spot.
Conclusion
We detected three main QTLs, one
each on chromosomes 3, 5, and 10 (Bin3.08, Bin5.03, and Bin10.04). The QTL on
chromosome 10 was detected in three environments. This locus may contain a
stable resistance gene. Further research is required to identify and
characterize this gene.
Acknowledgments
We thank Wang Piwu
(Biotechnology Center, Jilin Agricultural University) and Prof. Guan for
support and help. This research was funded by Jilin Province Science and
Technology Research Projects (no. 20170204005NY), the Special Fund Project of
Provincial Grain Production and Development in Jilin Province (no. 2015001) and
Five-Year Scientific Research Project of the Education Department of Jilin
Province. We thank Jennifer Smith, PhD, from Liwen Bianji, Edanz Group China
(www.liwenbianji.cn/ac), for editing the English text of a draft of this
manuscript.
Author Contributions
Jian-Bo Fei and Zhao-Xu Dong planted maize populations; Zhi-Bo
Liu and Dong-Liang Jin collected phenotypic data; Jing Qu,
Si-Yan Liu, Yi-yong Ma, and Shu-Yan
Guan obtained genotype data, Jian-bo Fei wrote the manuscript with contributions from Zhao-Xu.
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