Introduction
As a
predominant source of protein and oil, soybean
(Glycine max Merr.) is an indispensable crop for people's life, but the
yield level and nutritional quality standard of soybean in China are often
difficult to meet the rapidly increasing human population, resulting in an
increase in the import of soybean purchases abroad (Wilson 2008;
Ray et al. 2013). Therefore, it is
extremely important to overcome the bottlenecks confronting low yield level and
quality components of soybean in China.
Stay-green refers to that chlorophyll is not or
insignificantly degraded during leaf senescence, especially in the later stage
of plant growth and development, and it is an important character to improve
grain number (Jagadish et al. 2015; Zhang et al. 2019). The leaves can remain green for a longer time,
maintain an active leaf area for photosynthesis, improve photosynthetic
activity and net photosynthetic efficiency and continue to fill their grains
normally under stress (Liu et al.
2019). The stay-green mutants can be divided
into two groups due to their different stay-green traits and mechanisms such as
mutants and non-functional mutants (Thomas and Howarth 2000). Functional mutants can produce much more total dry
matter than the plants without a stay-green trait because of their slow leaf senescence and long
photosynthesis time. Cha et al. (2002) found that chlorophyll concentration in
leaves of stay-green mutant decreased slowly than that of wild type during
grain filling. Stay-green mutants have been reported in Arabidopsis (Armstead et al. 2007), Lycopersicon
esculentum
(Hu et al. 2011), Zea mays L.
(Asakura et al. 2004). Furthermore,
stay-green genes of some stay-green mutants have been analyzed. For example,
the stay-green gene CL of the Capsicum
annuum L. stay-green mutant was caused by mutation of homologous genes on
chromosome 1 (Efrati et al.
2005). Ma and Gan have found that the yield of
stay-green mutant varieties increased significantly in maize and Nicotiana tabacum
research (Gan and Amasino 1995; Ma and Dwyer
1998). Studies have further shown that there are
two recessive stay-green genes D1 and D2 controlling cotyledons and seed coats
respectively in soybean (Fang et
al. 2014; Nakano et al. 2014).
In
addition, to the improved agronomic traits mentioned above, the stay-green
mutants obtained by radiation mutagenesis have many advantages. First, compared with traditional natural
variation and hybrid breeding, mutation breeding and molecular breeding have shorter breeding
years and higher success rate, and it has been performed in a number of crop
species such as Oryza sativa L. (Yao et al. 2018) and
tomatoes (Chaudhary et al. 2019). Second, radiation mutagenesis refers to a breeding
method that uses some physical factors (x-ray, γ-ray, β-ray, etc.) to irradiate the plant
and breed new varieties by altering plant genetic material. So it can not only
create new germplasm resources and research materials, but also has no food
safety issues similar to genetically
modified (GM) crops (Wang and Hu 2002). Third, radiation mutagenesis has been widely used in breeding, since
the late 1950s and radiation mutation breeding in China has played a great role
in promoting mutation breeding and the mutant varieties have a leading
advantage in quantity and planting area (Liu et al. 2009). People have used
radiation mutation to breed new varieties of tobacco (Zhou et al. 2008), wheat (Xue et al.
2014) and rice (Sun et al. 2017). With the development of radioactive cobalt source 60Co-γ rays are widely used and the breeding effects are
remarkable (Zhao and Liu 2017). Fourth, radiation breeding is also
environmental friendly. For instance, Japanese
scientists have bred rice varieties with low cadmium accumulation by high energy heavy ion mutagenesis (Ishikawa
et al. 2012). Scientists in United States, Canada and other countries have created a number of environmental friendly, low
phytic acid mutants of maize, soybean and barley using mutation breeding
approach (Sparvoli
and Cominelli 2015).
Radiation mutation breeding has the advantages of high breeding
efficiency, large variation range and mutagenesis progeny are safe (Wang and Hu
2002). Combining with radiation mutagenesis, stay-green mutants can be bred
quickly to meet the requirements of new soybean varieties and the needs of
actual agricultural production. In this study, soybean stay-green mutants were
used as mutagenic materials and treated with 60Co radiation to
analyze the genetic variation of phenotypic traits and molecular markers of
stay-green mutants induced by radiation. This study provided theoretical
support for the application of stay-green mutants in radiation mutation, and
combined with phenotypic data analysis. In addition, molecular experiments were
done to selected new high-quality stay-green mutants, which can provide a
practical basis for the innovation and development of soybean germplasm
resources.
Materials
and Methods
Table 1: Biological characteristics
of stay-green soybean and mutagenesis progeny
Number of lines |
Leaf shape |
Flower color |
Pubescence color |
Maturity type |
1 (ck) |
Narrow |
purple |
brown |
normal |
2 |
Narrow |
purple |
brown |
mid-late maturity |
3 |
broad |
purple |
gray |
normal |
4 |
broad |
white |
gray |
mid-late maturity |
5 |
narrow |
purple |
brown |
normal |
6 |
narrow |
purple |
brown |
normal |
7 |
narrow |
purple |
brown |
normal |
8 |
narrow |
purple |
brown |
normal |
9 |
narrow |
purple |
gray |
normal |
10 |
broad |
white |
gray |
normal |
11 |
narrow |
purple |
brown |
mid-late maturity |
12 |
narrow |
purple |
brown |
normal |
13 |
narrow |
purple |
gray |
early maturity |
14 |
narrow |
purple |
brown |
normal |
15 |
narrow |
purple |
gray |
normal |
16 |
narrow |
purple |
brown |
normal |
17 |
narrow |
purple |
gray |
normal |
18 |
narrow |
purple |
brown |
early maturity |
19 |
narrow |
purple |
brown |
mid-late maturity |
20 |
narrow |
purple |
brown |
normal |
21 |
narrow |
purple |
brown |
normal |
22 |
narrow |
purple |
brown |
normal |
23 |
narrow |
purple |
gray |
mid-early maturity |
24 |
narrow |
purple |
gray |
normal |
25 |
narrow |
purple |
gray |
normal |
26 |
narrow |
purple |
gray |
mid-late maturity |
27 |
narrow |
purple |
brown |
normal |
28 |
broad |
white |
gray |
normal |
29 |
narrow |
purple |
gray |
normal |
30 |
narrow |
purple |
gray |
normal |
31 |
narrow |
purple |
brown |
normal |
32 |
narrow |
purple |
brown |
normal |
33 |
narrow |
purple |
brown |
mid-late maturity |
34 |
narrow |
purple |
brown |
normal |
35 |
narrow |
purple |
brown |
normal |
36 |
narrow |
purple |
brown |
normal |
37 |
narrow |
white |
gray |
late maturity |
38 |
narrow |
purple |
brown |
mid-early maturity |
39 |
narrow |
purple |
brown |
normal |
40 |
broad |
white |
gray |
mid-late maturity |
41 |
narrow |
purple |
brown |
normal |
42 |
broad |
white |
gray |
normal |
43 |
narrow |
purple |
brown |
normal |
44 |
broad |
purple |
gray |
mid-late maturity |
45 |
broad |
white |
brown |
normal |
46 |
narrow |
purple |
brown |
early maturity |
47 |
narrow |
purple |
brown |
normal |
48 |
narrow |
purple |
brown |
mid-late maturity |
49 |
narrow |
purple |
brown |
normal |
50 |
narrow |
purple |
brown |
mid-early maturity |
51 |
narrow |
purple |
brown |
normal |
52 |
narrow |
purple |
brown |
late maturity |
53 |
narrow |
purple |
brown |
normal |
54 |
narrow |
purple |
brown |
normal |
55 |
narrow |
purple |
brown |
normal |
56 |
narrow |
purple |
brown |
mid-late maturity |
57 |
narrow |
purple |
brown |
normal |
58 |
narrow |
purple |
brown |
normal |
59 |
narrow |
purple |
brown |
normal |
60 |
narrow |
purple |
brown |
normal |
61 |
broad |
white |
brown |
normal |
62 |
narrow |
purple |
brown |
mid-late maturity |
63 |
narrow |
purple |
brown |
normal |
64 |
narrow |
purple |
brown |
normal |
65 |
narrow |
purple |
brown |
mid-early maturity |
Plants material
Selected a soybean stay-green mutant and its progenies M3 and
M4 (89 materials in total) were used to analyze the character
variation of the progeny of stay-green mutant. The mutant with the
characteristics of stay-green is produced under natural conditions. The leaves
and seeds are green, plant type is compact, flower color is purple,
semi-determinate, pubescence color is brown, and with few branches
(generally 0~2), the growth period is generally 115~120 days (Table 1).
Table 1: Continue
66 |
narrow |
purple |
Brown |
mid-late maturity |
67 |
narrow |
purple |
Brown |
Normal |
68 |
narrow |
purple |
brown |
Normal |
69 |
narrow |
purple |
brown |
mid-late maturity |
70 |
broad |
white |
gray |
Normal |
71 |
narrow |
purple |
brown |
Normal |
72 |
narrow |
purple |
brown |
Normal |
73 |
narrow |
purple |
brown |
Normal |
74 |
narrow |
purple |
brown |
Normal |
75 |
narrow |
purple |
brown |
late maturity |
76 |
broad |
purple |
brown |
mid-early maturity |
77 |
narrow |
purple |
brown |
normal |
78 |
narrow |
purple |
brown |
normal |
79 |
narrow |
purple |
brown |
normal |
80 |
broad |
purple |
gray |
normal |
81 |
narrow |
purple |
brown |
mid-late maturity |
82 |
broad |
white |
gray |
normal |
83 |
broad |
purple |
gray |
early maturity |
84 |
narrow |
purple |
brown |
normal |
85 |
narrow |
purple |
brown |
mid-late maturity |
86 |
narrow |
purple |
brown |
early maturity |
87 |
narrow |
purple |
brown |
normal |
88 |
narrow |
purple |
brown |
normal |
89 |
narrow |
purple |
brown |
mid-early maturity |
Selection of mutations
and their mutant progenies and the extraction of DNA
First, M1
generation was obtained by air-dried mutant seeds of soybean with stay-green
and irradiated with 60Co 100R/min radiation mutation. Then the M2 generation was obtained by
planting, the excellent mutant plants and special mutant individuals were
selected from M2 generation for cultivation to get M3 and
M4 generations of the selected plants. Using the method of randomized block design, M3 generation was
planted with a row spacing of 0.5 m, row length of 0.5 m and plant spacing of
0.25 m, repeated three times.
To analyze of SSR genetic diversity of stay-green mutant progeny, stay-green mutant
progeny M5 was planted in 2015, when the first alternate compound leaves emerged
from the seedling Genomic DNA of stay-green mutants and its progeny M5
were extracted based on the SDS method (Guan et al. 2003) and the oxidative reaction of (DNA) was prevented by adding β-mercaptoethanol in the
experiment DNA was extracted twice and then
dissolved and preserved in (TE) buffer. The extracted DNA
was of high purity and moderate
concentration, which was suitable for subsequent
molecular experiments. In subsequent molecular experiments, the genetic
diversity of mutant progeny was analyzed by SSR markers.
Character analysis
The SPAD values in leaves of mutant progeny at the seedling stage,
blooming stage and seed filling stage was measured by portable
chlorophyll meter SPAD-502 (Minolta Camera Co., Japan). After soybean matured, 10 individual plants were randomly selected to
measure 17 agronomic traits. Measured plant height (cm) and pod height (cm)
with ruler, and measured stem diameter (cm) with vernier caliper. Electronic
balance was used to measure plant weight (g), 100-seed weight (g) and seed
weight per plant (g). Then count main stem node number, branch number,
main stem pod number, branch pod number, number of one seed per pod, number of
two seed per pod, number of three seed per pod, number of four seed per pod, number of blighted pods, total
pod number, insect herbivory number. After that, the protein content (%) and
fat content (%) were measured by InfratecTM
1241 Grain Analyzer V5.00.
Statistical analysis
Mutagenic progeny M4 were seeded in 2015, with the same
planting season and method as before. SPSS19.0 software was used to collate the
data of phenotypic traits, correlation analysis, principal component analysis
(PCA) and cluster analysis were carried out.
Fig. 1: Percentage of mutation progeny according to the
different growth period
Fig. 2: Percentage of mutation progeny according to the
different pods number per plant
The data of SSR were processed by Popgene Version
1.32 (Yeh et al. 1999) and the allele
variance, genetic distance and Shannon index were obtained:
The pij in
the formula denotes the probability of the occurrence of the j allele of marker i,
Shannon
index (H′) = -∑pilnpi (Duan et al. 2003),
The pi in the formula is the
probability of the occurrence of the I allele variation, and the ln is the
natural logarithm. We used the STRUCTURE software
2.3.4 to analyze the genetic structure
of stay-green mutants and their mutant progenies (Evanno et al.
2005). The optimum number of main groups was determined by the value of Ln P
(D) obtained by the software. When the software runs, the K value was
set to 1–15 and each K value was run 15 times. And used SAS 8.0
(SAS Institute Inc., Cary, NC, USA) to statistic the mean, standard deviation
(SD) and coefficient of variation, etc. (Zondervan and Cardon 2004).
Results
Analysis of trait variation in
the progenies of stay-green mutants induced by radiation mutagenesis
Trait difference analysis: The growth period of the contrast
material was 118 days, while the change range of the growth period of mutagenic
progenies was 96~142 days, which was mainly around 120 days, accounting for
67.0% of the mutant progenies. Early maturity accounted for 5.7%, middle-early
maturity accounted for 8.0, 15.9
Fig. 3: Percentage of mutation progeny
according to the 100-seed weight
Fig. 4: Percentage of mutation progeny according to the
different seed weight per plant
Fig. 5: Percentage of mutation progeny
according to the different protein content
and 3.4% of them were middle-late and late maturity (Fig.
1).
The pods number per plant of
contrast material was 53, and the pods number per plant of the mutant progenies
was mainly happened between 56 and 65. Less than 45 of them accounted
for 10.2% of the progeny population and more than 95 of them accounted for
7.9%, mutagenesis results in a higher variable rate of the pods number per
plant (Fig. 2). One hundred-seed weight of contrast material was 22.08 g, the
variation range of 100-seed weight of mutant progenies was 14.70~26.16 g,
mainly distributed between 21.22~22.24 g, which accounted for
21.6% of the mutant progenies. Less than 16.22 g accounted for 2.2% of the
mutant progenies and 5.7% of the progenies were above 12.11 g (Fig. 3). The
seed weight per plant of the control material was 32.67 g, the seed weight per
plant of mutant progenies was mainly between 25.55 and 35.55 g, accounting for
26.1% and there were 11.4% of the mutant progenies that seed weight per plant
exceeded 65.55 g (Fig. 4).
In this study, the
protein content of control material was 44.6% and
the protein content of mutant progenies
Fig. 6: Percentage of mutation progeny according to the
different fat content
Fig. 7: Percentage of mutation progeny according to the
different seeding SPAD value
Fig. 8: Percentage of mutation progeny
according to the different SPAD value on the full bloom stage
Fig. 9: Percentage of mutation progeny according to the
different SPAD value in the seed filling period
was mainly between 44.4 and
45.4%, which accounted for 68.2% of the mutant progeny population. The
progenies whose protein content exceeded 45.4% accounted for 10.2% of the population,
whilst the protein content of 5.7% mutant progenies was lower than 42.4% (Fig.
5). The fat content of the control material was 21.6%,
the fat content of mutant progenies was mainly
varied between 21.3% and 21.7%, which accounted for 68.2% of the
mutant progeny population. After mutagenesis, the progenies whose fat
content exceeded 21.7% accounted for 20.4% of the population and less than
21.3% accounted for 11.4% (Fig. 6).
The SPAD values in the leaves of
the control materials at the seedling stage, full bloom stage and filling stage
were 38.2, 45.7 and 49.2, respectively. The SPAD values
in the leaves of the mutant progenies at the seedling stage were mainly between 38.0 and 39.0 (Fig. 7). The SPAD values of the mutant progenies at
flowering stage and seed filling stage remained in the range of 45.0–46.0 and 45.5–47.5, respectively. The SPAD values
of mutant progenies at seed filling stage were generally lower than that of
soybean stay-green mutants and 21.6% of progenies had higher SPAD value than that
of control materials. This indicated that mutation treatment caused changes in
stay-green trait of some progeny populations, and mutation can easily weaken
the stay-green property of soybean stay-green mutants (Fig. 8 and 9).
At the same time, it cannot be
denied that mutation can also destroy some gene functions of the stay-green
mutants, resulting in some dwarf plants, semi-sterile plants, and sterile
plants. Because only one kind of soybean stay-green mutants was selected in
this experiment, the results of this study cannot represent all the stay-green
mutants of soybean and other varieties, but it also has very rich reference
significance.
Analysis of genetic variation of phenotypic traits: Through
statistical analysis of agronomic traits data of mutant progenies, the genetic
variation degree of different materials on traits was compared by the
coefficient of variation. It was noted that greater the coefficient of variation of a
trait, greater was difference of this trait in the later generations. M3
and M4 are significantly higher than the control in plant weight
(g), plant height (cm), protein content (%) and fat content (%) after
mutagenesis, and these germplasms have great potential to become high-quality
and high yield varieties (Table 2). In the analysis of
coefficients of variation, in the M3 generation, maximum coefficient
of variation of number of four seed per pod was r=1.42 and the minimum
coefficient of variation of protein content and fat content were r=0.02 and
r=0.01, respectively. From these data, we noted that the phenotypic traits of M3
and M4 generations changed to some extent. The varying degree of
number of four seed per pod, branch number and branch pod number and other
traits were higher, but the difference of protein content (%) and fat content
(%) of mutant progenies were not obvious, while the variations in the quality
traits were lower. Moreover, the coefficient of variation of most traits in M4
generation was slightly smaller than in M3 generation, indicating
that the traits of mutant progeny tended to be stable (Table 2).
Correlation analysis of the agronomic traits: Through
correlation analysis of plant weight (g), plant height (cm) and other agronomic
traits data of mutant progeny, the correlation between agronomic traits of progeny
can be understood. Seven pairs of mutant progenies reached a significant level,
48 pairs reached an extremely significant level, accounting for 35.95% of the
total (Table 3). Pearson’s correlation revealed that 26 pairs were negative
correlated, accounting for 17.0% of the total. Data showed that higher was the
pod height, lower was the yield per plant. Therefore, the appropriate pod
height should be selected when breeding varieties. In breeding programs, it is
Table 2: The average of different agronomic traits and
coefficient of variation in the mutants
Trait |
CK |
Max. |
Min. |
Mean |
SD |
CV |
||||||
M3 |
M4 |
M3 |
M4 |
M3 |
M4 |
M3 |
M4 |
M3 |
M4 |
M3 |
M4 |
|
Plant weight (g) |
50.84 |
58.96 |
118.47 |
122.87 |
18.74 |
25.13 |
55.21 |
62.47 |
21.87 |
21.61 |
0.40 |
0.35 |
Plant height (cm) |
77.60 |
81.70 |
110.60 |
116.20 |
32.40 |
42.50 |
76.20 |
76.80 |
14.68 |
14.23 |
0.19 |
0.19 |
Pod height (cm) |
11.80 |
14.10 |
37.20 |
21.80 |
2.30 |
2.30 |
10.40 |
9.30 |
7.48 |
3.78 |
0.72 |
0.41 |
Stem diameter
(cm) |
1.11 |
0.89 |
1.47 |
1.64 |
0.35 |
0.52 |
0.96 |
0.91 |
0.23 |
0.21 |
0.24 |
0.23 |
Main stem node number |
17 |
21 |
20 |
24 |
11 |
10 |
19 |
19 |
3.44 |
3.09 |
0.18 |
0.16 |
Branch number |
0 |
0 |
4 |
4 |
0 |
0 |
1 |
2 |
1.26 |
1.22 |
1.26 |
0.76 |
Main stem pod number |
41 |
69 |
88 |
122 |
17 |
32 |
42 |
54 |
13.61 |
14.57 |
0.32 |
0.27 |
Branch pod number |
0 |
0 |
72 |
90 |
0 |
0 |
12 |
25 |
14.98 |
22.49 |
1.25 |
0.90 |
No. of one seed per pod |
2 |
5 |
36 |
34 |
1 |
2 |
9 |
9 |
6.93 |
5.76 |
0.77 |
0.66 |
8 |
12 |
51 |
86 |
3 |
6 |
18 |
22 |
8.94 |
13.24 |
0.50 |
0.59 |
|
No. of three seeds per pod |
17 |
33 |
38 |
103 |
2 |
3 |
19 |
34 |
12.04 |
18.63 |
0.63 |
0.54 |
No. of four seeds per pod |
10 |
12 |
38 |
29 |
0 |
0 |
4 |
6 |
5.70 |
6.42 |
1.42 |
1.00 |
4 |
7 |
14 |
19 |
0 |
1 |
4 |
7 |
3.21 |
3.27 |
0.80 |
0.45 |
|
Total pod number |
41 |
69 |
142 |
168 |
19 |
38 |
55 |
79 |
22.42 |
28.06 |
0.41 |
0.35 |
Insect herbivory number |
8 |
8 |
27 |
15 |
1 |
2 |
7 |
7 |
4.91 |
3.06 |
0.70 |
0.43 |
100-seed weight (g) |
22.52 |
22.44 |
26.54 |
26.40 |
16.22 |
14.52 |
22.14 |
19.90 |
2.22 |
2.48 |
0.10 |
0.12 |
Seed weight per plant (g) |
20.13 |
39.47 |
71.26 |
79.33 |
5.29 |
11.92 |
24.11 |
36.20 |
11.34 |
15.07 |
0.47 |
0.42 |
Protein content (%) |
44.20 |
44.30 |
46.10 |
46.50 |
40.50 |
40.90 |
44.10 |
44.00 |
0.88 |
0.95 |
0.02 |
0.02 |
Fat content (%) |
21.50 |
21.60 |
22.00 |
22.00 |
20.50 |
20.70 |
21.40 |
21.50 |
0.35 |
0.20 |
0.02 |
0.01 |
Table 3: The simple correlation coefficient for agronomic
traits of mutation progeny
Characteristics |
Plant weight |
Plant height |
Pod height |
Stem diameter |
Main stem node number |
Branch number |
Main stem pod number |
Branch pod number |
No. of one seeded pods |
No. of two seeded pods |
No. of three seeded pods |
No. of four seeded pods |
No. of blight affected pods |
Total pod number per plant |
Insect herbivory number |
100-seed weight |
|
Plant height |
0.121 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pod height |
-0.038 |
0.376** |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Stem diameter |
0.607** |
-0.05 |
0.063 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Main stem node number |
0.107 |
0.573** |
0.096 |
0.196 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Branch number |
0.511** |
0.025 |
-0.111 |
0.218* |
0.105 |
|
|
|
|
|
|
|
|
|
|
|
|
Main stem pod number |
0.583** |
0.09 |
0.059 |
0.325** |
0.1 |
-0.145 |
|
|
|
|
|
|
|
|
|
|
|
Branch pod number |
0.762** |
-0.107 |
-0.204 |
0.416** |
-0.004 |
0.788** |
0.105 |
|
|
|
|
|
|
|
|
|
|
No. of one seed per pod |
0.269* |
0.019 |
-0.129 |
0.299** |
0.184 |
0.320** |
0.125 |
0.305** |
|
|
|
|
|
|
|
|
|
No. of two seeds per pod |
0.521** |
0.053 |
-0.076 |
0.298** |
-0.05 |
0.329** |
0.349** |
0.483** |
0.249* |
|
|
|
|
|
|
|
|
No. of three seeds per pod |
0.668** |
-0.16 |
-0.111 |
0.299** |
-0.027 |
0.396** |
0.441** |
0.625** |
-0.03 |
-0.046 |
|
|
|
|
|
|
|
No. of four seeds per pod |
0.481** |
0.135 |
0.088 |
0.278** |
0.187 |
0.201 |
0.297** |
0.419** |
-0.08 |
0.115 |
0.277** |
|
|
|
|
|
|
No. of blighted pods |
0.301 |
0.061 |
-0.114 |
0.318 |
0.121 |
0.243* |
0.449** |
0.471 |
0.267* |
0.392 |
0.283 |
0.321 |
|
|
|
|
|
Total pod number |
0.914** |
-0.039 |
-0.133 |
0.502** |
0.049 |
0.557** |
0.604** |
0.856** |
0.310** |
0.568** |
0.730** |
0.490** |
0.611** |
|
|
|
|
Insect herbivory number |
0.214 |
0.016 |
-0.195 |
0.262* |
0.172 |
0.283 |
0.182 |
0.334 |
0.28 |
0.078 |
0.321 |
0.103 |
0.343 |
0.363 |
|
|
|
100-seed weight |
0.14 |
0.105 |
0.024 |
0.308** |
0.003 |
0.008 |
-0.055 |
-0.049 |
0.244* |
0.11 |
-0.194 |
-0.229* |
0.115 |
-0.068 |
-0.06 |
|
|
Seed weight per plant |
0.932** |
0.022 |
-0.049 |
0.505** |
0.03 |
0.524** |
0.532** |
0.804** |
0.174 |
0.454** |
0.736** |
0.524** |
0.452 |
0.921** |
0.216 |
0.103 |
|
Note: * and ** indicate significant
difference and extremely significant difference, respectively
easier to breed new soybean varieties with high quality
and yield by considering the mutual restriction and correlation of agronomic
traits (Table 3).
PCA and cluster analysis: We used PCA to
analyze the phenotypic traits of yield in order to select excellent varieties
with the high efficiency as mentioned by Evanno et al. (2005). Five
principal components were obtained from the dimensionality reduction of 17
agronomic traits by PCA. The first principal component was crop yield,
including total pod number, plant weight, seed weight per plant, branch pod number;
The second principal component was the plant type, including plant height, main
stem node number and pod height, which was related to plant type; The third
principal component was determinate nature of plants, including the number of one
seed per pod, the 100-seed weight and the number of two seed per pod, which is
called ‘pod factor’; The fourth principal component was
insect herbivory, which exhibited the most
significant correlation with the insect herbivory number,
including the insect herbivory number, branch number and the main stem node
number. The fifth principal component includes the main stem node number, pod
height and stem diameter, namely plant stem type (Table 4).
Cluster analysis was used to
classify the stay-green mutants and their progenies into four groups. The first
group had higher pod height, lower plant weight and yield per plant, the
stay-green mutant of the control material was found in this group. The plant
weight and yield per plant of the second group and its progeny maintained a
medium level, while the number of two seed per pod was higher and the insect
herbivory number were fewest. Table 4: Component matrix table
Trait |
Components |
||||
|
1 |
2 |
3 |
4 |
5 |
Plant weight |
0.941 |
0.09 |
-0.053 |
-0.109 |
0.112 |
Plant height |
0.026 |
0.821 |
-0.092 |
0.281 |
0.104 |
Pod height |
-0.12 |
0.539 |
-0.306 |
-0.07 |
0.423 |
Stem diameter |
0.602 |
0.249 |
0.189 |
-0.213 |
0.372 |
Main stem node number |
0.128 |
0.694 |
-0.002 |
0.464 |
0.431 |
Branch number |
0.608 |
-0.18 |
0.287 |
0.541 |
0.343 |
Main stem pod number |
0.548 |
0.243 |
-0.348 |
-0.525 |
-0.351 |
Branch pod number |
0.864 |
-0.269 |
0.119 |
0.271 |
0.238 |
No. of one seed per pod |
0.348 |
0.154 |
0.655 |
0.069 |
-0.225 |
No. of two seeds per pod |
0.541 |
0.076 |
0.301 |
-0.306 |
0.273 |
No. of three seeds per pod |
0.691 |
-0.309 |
-0.387 |
0.101 |
-0.123 |
No. of four seeds per pod |
0.514 |
0.154 |
-0.49 |
0.137 |
0.126 |
No. of blighted pods |
0.649 |
0.134 |
0.09 |
-0.17 |
-0.243 |
Total pod number |
0.977 |
-0.09 |
-0.085 |
-0.055 |
0.009 |
Insect herbivory number |
0.44 |
-0.028 |
0.149 |
0.604 |
-0.59 |
100-seed weight |
0.042 |
0.295 |
0.61 |
-0.365 |
0.172 |
Seed weight per plant |
0.938 |
-0.034 |
-0.131 |
-0.078 |
0.125 |
Table 5: The average of phenotypic traits
Plant traits |
Plant weight |
Plant height |
Pod height |
Stem diameter |
Main stem Node number |
Branch number |
Main stem pod number |
Branch pod number |
First
kind |
45.05 |
72.45 |
10.10 |
0.83 |
19 |
1 |
49 |
5 |
Second
kind |
69.29 |
75.10 |
8.90 |
0.96 |
19 |
2 |
57 |
33 |
Third
kind |
108.62 |
81.80 |
8.30 |
1.15 |
20 |
2 |
97 |
37 |
Fourth
kind |
105.32 |
83.30 |
9.40 |
1.02 |
21 |
3 |
62 |
69 |
Table 6: The average of
phenotypic traits
Plant traits |
No. of one seed per pod |
No. of two seeds per pod |
No. of three seeds per pod |
No. of four seeds per pod |
No. of blight affected pods |
Total pod number |
Insect herbivory number |
100-seed weight |
Seed weight per plant |
First kind |
5 |
14 |
25 |
4 |
5 |
54 |
8 |
19.66 |
24.62 |
Second kind |
11 |
31 |
35 |
5 |
8 |
90 |
5 |
19.38 |
36.68 |
Third kind |
13 |
24 |
70 |
15 |
12 |
134 |
9 |
19.10 |
62.63 |
Fourth kind |
11 |
45 |
48 |
16 |
11 |
127 |
9 |
20.42 |
60.67 |
The third group had better
plant type, more branches, higher plant weight and yield. The fourth group had
the strongest stem, medium height, good plant growth, the
highest plant weight and yield, which had high yield ability (Table 5–7).
Analysis of genetic diversity of SSR in mutant progenies of
stay-green mutants
Polymorphism analysis and cluster analysis of
SSR markers in mutant progenies of
stay-green mutants: In this study, 70 pairs of SSR
primers were selected to amplify 89 materials of stay-green mutants and their
mutant progenies and 34 pairs of primers with rich polymorphism were selected
for genetic diversity analysis: a total of 96 allele variations were detected. The
range of allele variations of each primer ranged from 2 to 5, with an average
of 2.8 and the maximum allelic variation detected by primers
Sat_385 and Sat_333 was 5, while the minimum number
of allele variances was only 2. The variation range of polymorphic information
quantity (PIC) was 0.049–0.693, and the
average was 0.362. Among primers, Sat_385 has the largest polymorphic
information. The range of the Shannon index was 0.1157–1.4128, with an
average of 0.6998. Among them, the Shannon index of primer Sat_385 was the
largest. According to the calculation of genetic distance, 89 materials were
classified into 6 categories, including 1 material, 10 materials, 12 materials,
12 materials, 53 materials and 1 material,
respectively. The genetic variation of No. 50 material of category 6 was
relatively large, and it showed excellent performance in stay-green and
phenotypic trait. It belonged to the third category in the clustering results
of agronomic traits and had the potential to cultivate new varieties of high
quality. And Fig. 10 showed the annular clustering of stay-green soybean and
mutation progenies.
Analysis of genetic structure of
mutant progeny: LnP (D) values derived from software processing
results according to Evanno et al. (2005):
Table 7: Information of 18 SSR locus and diversity statistics
Number |
Primers |
Allele number |
PIC |
Shannon Index (Hˊ) |
1 |
Satt235 |
3 |
0.364 |
0.7081 |
2 |
Satt400 |
2 |
0.287 |
0.5232 |
3 |
Satt406 |
3 |
0.457 |
0.8455 |
4 |
Satt248 |
3 |
0.388 |
0.7156 |
5 |
Satt165 |
3 |
0.562 |
0.9825 |
6 |
Sat_385 |
5 |
0.693 |
1.4128 |
7 |
Satt321 |
2 |
0.103 |
0.2661 |
8 |
Sat_332 |
2 |
0.361 |
0.6714 |
9 |
Sat_201 |
3 |
0.513 |
0.9753 |
10 |
Satt450 |
3 |
0.265 |
0.4852 |
11 |
Sat_153 |
3 |
0.352 |
0.6254 |
12 |
Satt322 |
4 |
0.652 |
1.3465 |
13 |
Satt361 |
2 |
0.211 |
0.3562 |
14 |
Satt195 |
3 |
0.436 |
0.9768 |
15 |
Satt257 |
2 |
0.312 |
0.5763 |
16 |
Sat_272 |
2 |
0.107 |
0.1956 |
17 |
Sat_149 |
3 |
0.244 |
0.4015 |
18 |
Satt453 |
4 |
0.621 |
1.2237 |
19 |
Satt355 |
2 |
0.121 |
0.2681 |
20 |
Satt326 |
2 |
0.338 |
0.6028 |
21 |
Satt247 |
2 |
0.154 |
0.3627 |
22 |
Sat_091 |
3 |
0.445 |
0.8819 |
23 |
Satt624 |
3 |
0.563 |
1.0552 |
24 |
Satt514 |
4 |
0.517 |
0.9553 |
25 |
Sat_333 |
5 |
0.673 |
1.3624 |
26 |
Satt413 |
3 |
0.248 |
0.5126 |
27 |
Satt412 |
3 |
0.224 |
0.4528 |
28 |
Satt469 |
2 |
0.206 |
0.4124 |
29 |
Sat_200 |
2 |
0.146 |
0.3575 |
30 |
Satt243 |
3 |
0.522 |
0.9826 |
31 |
Sat_331 |
3 |
0.411 |
0.7784 |
32 |
Sat_108 |
2 |
0.378 |
0.6963 |
33 |
Satt156 |
3 |
0.347 |
0.6682 |
34 |
Satt723 |
2 |
0.049 |
0.1157 |
Average |
|
2.8 |
0.362 |
0.6998 |
Total |
|
96 |
12.3 |
23.7922 |
△K =
m(|L(K+1)-2L(K)+L(K-1)|)/s[L(K)]
The △K line chart is
drawn (Fig. 11). Eighty eight mutant progeny
materials were divided into 5 groups by population genetic structure analysis,
including 22, 15, 13, 17 and 21 materials, which facilitated further analysis
of the population genetic structure of mutant progenies with stay-green
mutants (Fig. 12).
Discussion
It was found that radiation
mutagenesis could change many traits of soybean such as the oil content of
soybean can be increased after 60Co
mutagenesis (Guo et al. 2005). The stay-green mutant can maintain
carbon assimilation over an extended period and maintain
grain weight, quality and nutrient efficiency (Jagadish et
al. 2015; Rebetzke et al. 2016; Shi et al. 2016). In
this study, 60Co radiation mutagenesis was used to obtain stay-green
mutants. Through analysis of 19 agronomic traits, we found that
the yield per plant of mutant progeny was significantly increased, mainly due
to the increase of effective total pod number, which was similar to Han’s
research (Han et al. 2008). The protein and fat content also changed.
But in the aspect of stay-green trait, mutagenesis could decrease the
stay-green trait of soybean stay-green mutant, which showed that SPAD values in
leaves of most mutant materials was less than that of control materials without
radiation treatment at seed filling stage.
Correlation
analysis showed that there was a strong correlation between agronomic traits, which acted
synergistically or antagonistically and affected plant growth. The results
showed that stem diameter, plant weight, branch number, main stem pod number,
branch pod number and total pod number were significantly positively
Fig. 10: Annular clustering figure of stay-green
soybean and mutation progeny
Fig. 11: △K determine the mutagenesis
best group manager for several generations
Fig.
12:
Mutagenesis progeny population genetic structure
correlated
with yield per plant, and negatively correlated with pod height. Li (2018)
showed that the breeding high-yield
vegetable soybeans should consider all agronomic traits comprehensively,
instead of pursuing plant height carelessly. This study also indicates that
when selecting a good progeny in mutant breeding, the material with low pod
height should be given priority, which makes it easier to breed new varieties.
The PCA has been widely used in the
character evaluation and the comprehensive evaluation of germplasm resources in many soybeans and vegetable soybeans
(Wu and Chen 2007; Li 2018). This study used PCA transformed several variables
into a few important variables. Similarly, 17 agronomic traits of 88 materials
were dimensionally reduced to simplify the analysis method and reduce the data
variables. Xin et al. (2019) have used this method to reduce the
dimensionality of the quality evaluation indexes of wax gourd wine. After
that, it would be easier to analyze the yield components of mutant progeny.
Then the factors affecting the growth of mutant progeny were divided into five
main components such as yield factor, stem type factor, pod number factor,
insect pest factor and plant type factor. These factors can be considered
comprehensively in the selection of progeny materials to facilitate the
selection of new varieties with high quality.
Finally,
the data were processed by the systematic clustering method. Clustering
analysis can not only reveal the genetic differences and relationships among
populations, but also can revealed the genetic similarities among varieties
within populations (Xue et al. 2019). By using cluster analysis and PCA,
the genetic characters of main soybean varieties in different regions were
analyzed, and the difference of genetic distance between different varieties
was found out, which provided theoretical basis for soybean cross breeding and
parent selection (Hu 2004; Kang et al. 2009; Zhao et al. 2017).
In this experiment, the stay green mutants and their mutant progenies were
divided into four categories, the first category belongs to low yield
materials, the second category belongs to middle yield materials, the third
category belongs to high yield materials and the fourth category belongs to
extremely high yield materials. The hybrid combination of different categories
of soybean is beneficial to the breeding of soybean varieties with good
comprehensive characters. The stay-green mutants belong to the first category,
and their yield is relatively low. After mutagenesis, the yield per plant of
progeny increased significantly, and some extremely
high yield materials appeared. This indicated that radiation mutagenesis could
change the agronomic traits of the mutants with low yield and increase its
ability to yield. After the clustering analysis, 89 materials were divided into
6 groups with different phenotypic traits. Among them, the
genetic variation of No. 50 material in the sixth group was great, the
stay-green trait was obvious, and the yield per plant was high. Therefore, it
should be taken as the key research object in the next study.
Conclusion
The yield per plant of soybean
stay-green mutant was negatively correlated with its stay-green trait after radiation mutagenesis and
genetic variation of different traits occurred in varying degrees. Seventeen agronomic traits of mutant progenies
can be divided into five main components: yield factor, pod factor, stem type
factor, plant type factor and pest factor; and after systematic
clustering, it can be divided into four groups: low
yield, middle yield, high yield and extremely high yield. In
this study, 96 allelic variations were detected by SSR genetic diversity
analysis. The range of allelic variation of
each primer ranged from 2 to 5, with an average of 2.8. Variation ranges
of PIC were 0.049–0.693, with an
average of 0.362. The Shannon's index of SSR markers ranged
from 0.1157 to 1.4128, with an average of 0.6998. At last, according to the calculation of genetic
distance, 89 materials were clustered into six categories.
The work was partly supported by Research Fund for Young Scientists of
BUA (Project No. SXQN201805); Beijing outstanding talent training for young
backbone individual projects (Project No. 2016000020124G049) and Beijing
Municipal Education Commission (Project No. KM201610020006).
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