VEGETOS
Vol. 24 (1) : 89-92 (2011)
Gene Action For Yield and its Components in
Soybean (Glycine max (L.) Merrill)
Shiv Datt*, S. K. Noren1, V. P. Bhadana 2 and P. R. Sharma3
IP&TM Unit, KAB-I, ICAR, Pusa, New Delhi-110012 India
1
College of Post Graduate Studies, CAU, Umiam, Barapani, Meghalay-793 103 India
2
Directorate of Rice Research, Hyderabad India
3
Central Agricultural University, Imphal, Manipur-795 004 India
Gene action, interaction and linkage relationship of genes creating continuous phenotypic
variation of various metric traits are dedicated to breeding methods. Thus both additive and
non-additive components of genetic variance, along their allied characteristics are of great
use for plant breeders under different situations. An estimate of additive variance and nonadditive variance provides a measure of how likely particular traits can be selected for or
against and that of whether hybridization or a population improvement program. Five generations viz. P1, P2, F1, F2 and F3 were evaluated in experiments under a compact family
block design to estimate gene effects for major agro-morphological traits in four soybean
single crosses (Bragg x RKS 18, JS 335x RAUS 5 and Bragg x JS 335 and RAUS 5 x Birsa Soy 1). The results showed additive gene effects that determined the inheritance of agromorphological traits viz. days to 50 percent flowering, days to maturity, plant height and
harvest index. Dominance gene action was critical in determining the yield. Duplicate epitasis was significantly important in inheritance of most traits studied. On the basis of results
obtained from the present investigation, it is suggested that these major quantitative traits
in the desirable genotypes play a major role in the improvement of high yielding varieties
of soybean through exploitation of additive and non-additive variances.
Keywords: gene action, gene effects, agro-morphological traits, soybean
INTRODUCTION
Genetic studies are conducted to determine if a
trait is heritable, how many genes are involved and the
possible genetic relationships among genes from different sources with similar phenotypes (Liu, 1997).
Once the inheritance of the gene(s) controlling a certain trait is understood, this information can be used to
further enhance the crop in terms of yield improvement, pest resistance, or many other vital characteristics. Determining the gene effects and their interaction
controlling various agro-morphological traits in soybean and their inheritance will expand the understanding of the genetics of this crop for further applications.
The objective of the present investigation is to study
the nature and magnitude of gene effects governing
the yield and components. Most of the earlier studies
conducted on nature and magnitude of genetic variation in soybean are based on diallele, partial diallele
* Corresponding author email: shivdatts@rediffmail.com
set and gca/sca analysis with the assumption that the
epistasis is negligible or absent. The results of the
studies indicated that epistasis plays a significant role
in the inheritance of yield and its component characters in soybean. Thus the assumption of absence of
epistasis may not hold true suggests some breeding
methods may not be appropriate for the genetic improvement of these characters hence, in the present
investigation, an efforts has been made to find out the
inheritance of yield and its attributes for their further
utilization in the breeding program.
MATERIAL AND METHODS
Present investigation was carried out at Central
Agricultural University, Imphal during the kharif season of 2006-2007. Five generations namely P1, P2, F1,
F2 and F3 of each of four crosses viz., Bragg x RKS
18, JS 335 x RAUS 5 and Bragg x JS 335 and RAUS
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Shiv Datt et al.
Table 1. Scaling tests and gene action for different agro-morphological traits in crosses of soybean
Sl.
N
o.
Characters
Scales
C
Genetic components
D
m
d
h
Days to 50% flowering
-18.36*
15.74**
44.23**
2.
Days to full maturity
-7.10*
-9.21**
123.05**
4.65**
3.
Plant height(cm)
80.20*
21.03
188.67**
4.
Number of pods/plant
78.22*
56.16**
180.92**
*
**
-17.63**
6.01*
Duplicate
0.25
-4.51*
3.21
Complimentary
-91.10**
21.08
-185.18**
-80.25
Duplicate
-57.12**
20.8
-138.32**
-28.63
Duplicate
4.21
Duplicate
Harvest index (%)
-6.14
12.25**
27.24**
8.97**
-5.68**
24.58**
-7.84
Complimentary
7.
Seed yield /plant
-0.24*
-0.008
0.33**
0.15**
-2.90**
0.22**
0.25
Duplicate
-0.90
-10.05**
1.98
Duplicate
-1.861
-0.461
10.98
5.42
17.61
**
6.
**
-4.12
*
-7.85
**
7.65
**
Dry matter weight/plant(g)
Oil per cent
8.86
l
5.
8.
-6.01
**
i
Bragg x RKS 18
-10.85**
-3.98**
1.
Non allelic
interaction
Bragg x JS 335
1.
Days to 50% flowering
10.28*
-1.85
47.63**
-0.94**
4.21**
0.65
-15.23**
Duplicate
**
-0.31
1.98
0.96
-0.79
Duplicate
2.
Days to full maturity
-3.0
-3.25
124.0
3.
Plant height(cm)
-6.23
-4.32
64.28**
-0.18
1.95
1.24
2.69
Duplicate
4.
Number of pods/plant
2.25
6.32*
76.21**
6.53**
-3.06
9.37**
5.18
Complimentary
5.
Dry matter weight/plant(g)
-0.02
-0.21**
0.61**
-65.35**
0.18**
0.15**
-0.32**
Duplicate
6.
Harvest index (%)
-0.52
29.63**
42.25**
0.35
-0.21**
-19.6**
39.54**
Complimentary
7.
Seed yield /plant
-0.81
2.31*
22.14**
-0.15
-0.62
-1.68*
3.95
Complimentary
8.
Oil per cent
-2.51
0.72
17.21**
0.25
0.35
-0.28
4.16*
Complimentary
1.
Days to 50% flowering
9.28*
-2.85
45.66**
-0.85**
3.68**
0.58
-16.13**
Duplicate
**
-0.42
2.03
0.94
-0.76
Duplicate
Duplicate
RAUS 5 x Birsa Soy 1
2.
Days to full maturity
-4.0
-4.55
122.0
3.
Plant height(cm)
-7.33
-5.62
66.38**
-0.21
2.95
2.24
3.69
4.
Number of pods/plant
3.25
5.22*
74.21**
5.63**
-4.06
8.37**
6.18
**
0.71
**
0.18
**
Complimentary
-0.06
-0.31
6.
Harvest index (%)
-0.62
30.63**
43.25**
0.45
-0.25**
-20.6**
38.54**
Complimentary
7.
Seed yield /plant
-0.91
3.51*
22.14**
-0.15
-0.62
-1.68**
3.95
Complimentary
8.
Oil per cent
-3.51
0.64
18.11
0.45
-0.38
1.
Days to 50% flowering
5.06
-3.86**
46.12**
0.95**
**
*
4.03**
5.14**
-3.12
-6.21
-0.27
**
Dry matter weight/plant(g)
0.30
0.28
**
5.
**
-64.25
**
3.16
*
Duplicate
Complimentary
JS 335 x RAUS 5
-11.10**
Duplicate
14.25**
Duplicate
2.
Days to full maturity
-8.02
3.45
124.1
3.
Plant height(cm)
9.65
27.42**
63.14**
7.36**
-1.28
-2.85
23.38**
Complimentary
4.
Number of pods/plant
1.63
6.45
71.20**
8.04**
7.33**
11.20**
7.45
Duplicate
3.67**
-6.12**
Duplicate
**
**
-0.26
4.02
16.97**
-13.65**
-17.35
Duplicate
0.24**
0.19**
0.15
Duplicate
-0.21
-1.09*
-0.23
Complimentary
5.
Dry matter weight/plant(g)
-0.51
-0.38
6.
Harvest index (%)
40.12
28.41**
48.20**
-0.51
7.
Seed yield /plant
-1.82
-6.45**
0.56**
-0.007
8.
Oil per cent
-0.32
-0.51
22.41
-0.95
**
17.52
**
-0.68
**
**
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Gene Action in Soybean (Glycine max)
5 x Birsa Soy 1 were evaluated in a compact family
design with three replications during the kharif 200708. Each plot had three rows of 3m length, spaced at
45 cm apart, with a plant to plant distance maintained
at 10 cm. The standard agronomic practices were followed to raise a healthy soybean crop. Ten competitive plants from generations namely P 1, P2 and F1 and
40 in F2 and 50 in F3 were randomly selected from
each replication in each plot to record observations on
major quantitative traits viz., plant height, days to 50
per cent flowering, days to maturity, number of pods
per plant, seed yield per plant, dry matter weight per
plant, harvested index and oil content. Data were subjected to individual scaling tests viz., C and D to detect the presence of epistasis following Mather (1949).
The gene effects were estimated by the five parameter
model as proposed by Haymen (1958).
RESULTS AND DISCUSSION
The significant gene interaction effects and
variance from different crosses was estimated for various agro-morphological and quantitative traits in view
of the great value of epistatic variation in inheritance.
Presence of significant and highly significant estimates of either one or two scaling tests were found for
all traits studied except oil content in cross (Bragg x
RKS 18), C scale for days to 50 per cent flowering
and D scale for most of the traits was found significant
in Bragg x JS 335. In case of JS 335 x RAUS 5, D
scale was found significant for number of traits viz.,
plant height, days to 50 per cent flowering, days to full
maturity, seed yield per plant and harvest index. Table
1 indicates the presence of epistatic variation in the
inheritance of various quantitative traits studied. Maloo and Nayer (2005) also found significant at least
one or two scales for days to 50 per cent flowering,
days to full maturity, yield per plant, harvest index,
number of pods per plant and plant height.
The estimates of mean (m) were highly significant for all the traits studied in all crosses. Highly significant value of „m‟ from generation mean analysis in
all the crosses showed that the five generations differed from each other significantly. Additive (d) component was predominantly in the inheritance of all the
traits in the intervarietal crosses Bragg x RKS 18,
Bragg x JS 335, JS 335 x RAUS 5 and RAUS 5 x Birsa Soy 1, revealed that selection in early segregating
generations would be effective for obtaining genetic
gain of these characters. The dominance gene effect
(h) was significant and greater in magnitude than the
additive effect (d) for days to 50 per cent flowering
and harvest index in Bragg x JS 335 and JS 335 x
RAUS 5 and days to 50 percent flowering, harvest
index and seed yield per plant in Bragg x RKS 18 and
RAUS 5 x Birsa Soy 1, indicating a predominant role
of dominance gene action in controlling these traits in
soybean. Similar results were found as additive and
dominance effects and variance were important in
genetic determination of seed yield and its componets
(Sharma et. al. 1993).
Among the digenic interaction effects additive
x additive was significant for all the traits in Bragg x
RKS 18 and Bragg x JS 335 and days to maturity,
number of pods per plant in JS 335 x RAUS 5 and
number of pods per plant, dry matter weight per plant,
harvest index and oil content RAUS 5 x Birsa Soy 1.
Dominance x dominance type of interaction
also showed greater effects in the present study. It was
found significant for plant height JS 335 x RAUS 5,
days to 50 per cent flowering along with oil content in
RAUS 5 x Birsa Soy 1 and 50 per cent flowering in
Bragg x RKS 18 and Bragg x JS 335. Singh et al.
(2010) also found significant interaction effects of
additive x additive and dominance x dominance for
days to 50 percent flowering, plant height, days to
maturity and number of pods per plant.
Complimentary epistasis was observed for
number of pods per plant, dry matter weight per plant,
harvest index and oil per cent in JS 335 x RAUS 5 and
plant height and oil content in RAUS 5 x Birsa Soy 1
and days to maturity, dry matter weight per plant in
Bragg x RKS 18 and Bragg x JS 335, which appeared
to be desirable and would be helpful in further improvement of these traits. Opposite and significant
signs of „h‟ and „l‟ components indicated the importance of duplicate epistasis in almost all the crosses
for various quantitative characters. Hence there is a
hindrance in selection as well as complex nature of
inheritance for improvement of these traits. In this
situation reciprocal recurrent selection is likely to be
useful for effective utilization of both types of additive
and non-additive gene effects simultaneously. The
significant complimentary epistasis was also observed
for yield other quantitative traits like number of pods
per plant, dry matter weight per plant, harvest index
and oil per cent by Singh et al. (2010).
Considering overall results, it is apparent that
most of the characters in either of the crosses were
found to be under the control of additive and nonadditive gene effects coupled with duplicate type of
epistasis. This indicated that heterosis breeding and
recurrent selection would be more effective for the
improvement of most of the characters. The duplicate
epistasis for most characters showed their complex
nature of inheritance. Therefore, breeding strategies
should be designed accordingly to get desired results.
Use of recurrent selection has been suggested to improve the traits when both additive and non-additive
gene effects are involved in expression of the traits.
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Maloo SR and Nair Sandeep (2005). Generation mean analysis for
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Shiv Datt et al.
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