Introduction

Rice (Oryza sativa L.) is one of the major cereal food crops of the world, contributing nearly 73% of the total calorie intake of the population. It covers around a 20% of the total land area covered under cereals as reported by Gaballah et al., 2020. It is the most important cereal crop and primary energy source for two thirds of world’s population (Khan et al., 2015).  Twenty one percent of global human per capita energy and 15% of per capita protein is provided by rice. Asia cultivates and consumes around 90% of the world’s rice is cultivated and around a half of the population depends on rice for food reported by Tenorio et al., 2013. Self-sufficiency in rice production was made possible by the development of high yielding varieties. Grain yield is a complex polygenic trait in rice that is affected by the large numbers of its component traits (Liu et al., 2011). These components traits are dependent on their expression on several morphological and developmental traits, which are interrelated with each other. So, the selection and genetic improvement of these characters is the principal way of enhancing yield. In order to make the genetic improvement of rice, the breeding method adopted mainly depends on the nature of gene action involved in the expression of the polygenic trait. Gene action is nothing but additive and dominant effects and their interactions which are reported to be associated with a breeding value (Falconer, 1989). In such manner, generation mean analysis is an extremely valuable strategy used to estimate gene action for a polygenic trait (Kearsey and Pooni, 2004). It is a simple yet useful technique used to estimate gene effects for a polygenic trait (Kearsey and Pooni, 2004). Its greatest positive is its ability to estimate epistatic gene effects, such as additive×additive, dominance×dominance and additive× dominance effects (Kearsey and Pooni, 2004; Viana, 2008). Using this, the gene effects of quantitative traits has been studied for various crops like  in rice (Rao, 2017), Wheat (Said, (2014) pearl millet (Gaoh et al., 2020), safflower (Golkar, 2018), corn (Azizi et al., 2006), cotton (Srinivas et al., 2015, Giri et al., 2020 and Naghera et al., 2021), castor (Patel  et al., 2021) and  peanut (Ajay et al., 2018). Genetic analysis using generation mean analysis (GMA) has been utilized to evaluate the gene actions controlling the quantitative traits, and it gives the information on additive, dominance, and epistatic effects. Mather’s individual scaling tests (Scale A, B, C, and D) and Cavalli’s joint scaling test were used to detect the presence or absence of the epistatic gene interactions i.e. non–allelic gene interactions. Therefore the present investigation was carried out to estimates the gene actions, i.e., additive, dominance and epistatic effects, that control quantitative traits in rice by using five parameter model  (generation mean analysis) i.e,  parent 1 (P₁), parent 2 (P₂), first filial (F₁), second filial (F₂) and third filial (F₃) generations in two rice crosses.

Materials and Methods

2.1.  Experimental material and procedure

The experiment was carried out at the Instructional Farm, Bidhan Chandra Krishi Viswavidyalaya, Jaguli, Nadia West Bengal, India located at 22.9452^o N Latitude and 88.5336^o E Longitude at an altitude of 9.5 m above mean sea level during June to October month of Kharif 2016 and kharif 2017. The experimental materials consisted of three rice varieties, i.e., Mahsuri, Bhutmuri, IR36 and F₁,F₂,and F₃ populations of Mahsuri×Bhutmuri (Cross I) and IR36×Bhutmuri (Cross II). The five populations i.e., P₁, P₂, P₃, F₁ and F₂ of the two crosses were grown in two separate experiments in a randomized complete blocks design with two replicates for each one. Plants were grown in 1, 2, and 3 rows each of 3 m length at a spacing of 30×15 cm² respectively. Observations were recorded on 10 randomly selected plants in case of parents and F₁, 30 plants of F₂ and 60 plants in F₃ per replication against 18 quantitative different characters such as days to 50% flowering, days to maturity, plant height (cm), number of tillers plant^-1, number of panicles plant^-1 (cm), panicle length, number of primary branches panicle^-1, number of secondary branches panicle^-1, number of grains panicle^-1, number of filled grains panicle^-1, fertility percentage (%), 1000 grain weight (g), grain length (mm), grain breadth (mm), grain L/B ratio, straw weight (g), harvest index (%) and grain yield plant^-1 (g).

2.2.  Statistical analysis

The analysis of variance was worked out to test the differences among genotypes by F-test. It was done by the technique of Randomized Block Design for each character according to approach upheld by Panse and Sukhatme (1967). The Scaling test was done as described by Mather (1949) and Hayman and Mather (1955) The joint scaling test as proposed by Cavalli (1952) was also applied to test the adequacy of the additive-dominance model. The different components of generation mean m, d, h, i, and l were calculated following Hayman (1958) and their test of significance were calculated following the appropriate t-test.

Results and Discussion

3.1.  ANOVA

Analysis of variance revealed significant differences among the parents, F₁ hybrids and F₂   generation against all characters studied (Table 1  and Table 2). Among the yield component in cross I, the number of secondary branches per panicle exhibited the highest significant difference. Among the yield component in cross II, the number of grains panicle^-1 exhibited the highest significant difference and grain length/breadth (L/B) exhibited the lowest significant difference in both cross I and II. It shows the presence of variation among rice genotypes used in this study.

Table 1 : Analysis of variance for different characters in Cross I (Mahsuri×Bhutmuri)

Table 2: Analysis of variance for different characters in Cross II ( IR36×Bhutmuri)

Figure 2: Continue...

3.2.  Scaling test, Joint scaling test and gene effects

Generation mean analysis is a very useful technique for estimating gene effects for a polygenic character (Hayman, 1958) and determines the presence and absence of non-allelic gene interactions. Generation mean analysis can be use for the estimation of epistatic gene effects namely additive×additive (i), additive×dominance (j), and dominance×dominance (l). The variation among the means of different generations in all the eighteen characters studied suggesting the usefulness of the estimation of additive, dominance and epistatic interaction. The C and D scaling test for all the characters in the two crosses showed that at least one or both were found significant indicating the presence of non-allelic interaction. Both the crosses exhibiting non-allelic interaction for the inheritance of almost all the traits studied. However, the characters like number of grains panicle^-1 and harvest index (%) in cross I and 1000 grains weight (g) in cross II showed non-significant values for both C and D scales (from table 2) indicating the non-interacting mode of inheritance. Similar finding were reported by Yadav et al. (2013) for number of grains per panicle and Jondhale et al. (2018) for harvest index (%). The present study shows that significant additive and epistatic effects exist in this population. Although their presence may vary from the Cross to Cross, the estimated values of joint scaling test and the five-parameter model are presented in Table 3.

Table 3: Different scales for yield components and component of generation means for different characters in cross I and cross II as suggested by Hayman (1958)

Table 3: Continue...

Table 3: Continue...

3.2.  Scaling test, Joint scaling test and gene effects

Generation mean analysis is a very useful technique for estimating gene effects for a polygenic character (Hayman, 1958) and determines the presence and absence of non-allelic gene interactions. Generation mean analysis can be use for the estimation of epistatic gene effects namely additive×additive (i), additive×dominance (j), and dominance×dominance (l). The variation among the means of different generations in all the eighteen characters studied suggesting the usefulness of the estimation of additive, dominance and epistatic interaction. The C and D scaling test for all the characters in the two crosses showed that at least one or both were found significant indicating the presence of non-allelic interaction. Both the crosses exhibiting non-allelic interaction for the inheritance of almost all the traits studied. However, the characters like number of grains panicle^-1 and harvest index (%) in cross I and 1000 grains weight (g) in cross II showed non-significant values for both C and D scales (from table 2) indicating the non-interacting mode of inheritance. Similar finding were reported by Yadav et al. (2013) for number of grains per panicle and Jondhale et al. (2018) for harvest index (%). The present study shows that significant additive and epistatic effects exist in this population. Although their presence may vary from the Cross to Cross, the estimated values of joint scaling test and the five-parameter model are presented in Table 3.

Table 3: Different scales for yield components and component of generation means for different characters in cross I and cross II as suggested by Hayman (1958)

Table 3: Continue...

Table 3: Continue...

3.2.  Scaling test, Joint scaling test and gene effects

Generation mean analysis is a very useful technique for estimating gene effects for a polygenic character (Hayman, 1958) and determines the presence and absence of non-allelic gene interactions. Generation mean analysis can be use for the estimation of epistatic gene effects namely additive×additive (i), additive×dominance (j), and dominance×dominance (l). The variation among the means of different generations in all the eighteen characters studied suggesting the usefulness of the estimation of additive, dominance and epistatic interaction. The C and D scaling test for all the characters in the two crosses showed that at least one or both were found significant indicating the presence of non-allelic interaction. Both the crosses exhibiting non-allelic interaction for the inheritance of almost all the traits studied. However, the characters like number of grains panicle^-1 and harvest index (%) in cross I and 1000 grains weight (g) in cross II showed non-significant values for both C and D scales (from table 2) indicating the non-interacting mode of inheritance. Similar finding were reported by Yadav et al. (2013) for number of grains per panicle and Jondhale et al. (2018) for harvest index (%). The present study shows that significant additive and epistatic effects exist in this population. Although their presence may vary from the Cross to Cross, the estimated values of joint scaling test and the five-parameter model are presented in Table 3.

Table 3: Different scales for yield components and component of generation means for different characters in cross I and cross II as suggested by Hayman (1958)

Table 3: Continue...

Table 3: Continue...

3.2.  Scaling test, Joint scaling test and gene effects

Generation mean analysis is a very useful technique for estimating gene effects for a polygenic character (Hayman, 1958) and determines the presence and absence of non-allelic gene interactions. Generation mean analysis can be use for the estimation of epistatic gene effects namely additive×additive (i), additive×dominance (j), and dominance×dominance (l). The variation among the means of different generations in all the eighteen characters studied suggesting the usefulness of the estimation of additive, dominance and epistatic interaction. The C and D scaling test for all the characters in the two crosses showed that at least one or both were found significant indicating the presence of non-allelic interaction. Both the crosses exhibiting non-allelic interaction for the inheritance of almost all the traits studied. However, the characters like number of grains panicle^-1 and harvest index (%) in cross I and 1000 grains weight (g) in cross II showed non-significant values for both C and D scales (from table 2) indicating the non-interacting mode of inheritance. Similar finding were reported by Yadav et al. (2013) for number of grains per panicle and Jondhale et al. (2018) for harvest index (%). The present study shows that significant additive and epistatic effects exist in this population. Although their presence may vary from the Cross to Cross, the estimated values of joint scaling test and the five-parameter model are presented in Table 3.

Table 3: Different scales for yield components and component of generation means for different characters in cross I and cross II as suggested by Hayman (1958)

Table 3: Continue...

Table 3: Continue...

number of primary branches panicle^-1 and number of secondary branches panicle^-1 were significant except dominance gene effect in days to 50% flowering and number of panicles plant^-1.

In Cross I all gene effects (Mean, additive dominance, additive×additice and domonnce×dominance) for days to maturity, number of primary branches panicle^-1 and number of secondary branches panicle^-1 were significant except dominance gene effect in days to 50% flowering and number of panicle plant^-1. Roy and senapati (2001) also reported that the dominance effect was not significant for the dyas to 50% flowering. In the case of the adequacy of the somple additive-dominance model, only additive gene effect were significant on panicle length, frain L/B ratio, straw weight (g) and grain yield plant^-1 (g). Similarly, only the dominance gene effect was significant in grain breadth (mm). In plant height (cm), number of grain grains pancle^-1, number of filled grains panicle^-1 and harvest index (%) bth additive and dominance gene effect were significant. An additive effect for panicle length and grain length/breath ratio was reported by Roy and senapati (2012). Aldo the importance of additive gene effects were reported by Roy and Senapati (2011), Kachabhai (2014) and Sultana et al. (2016). In Cross II all gene effects (mean, additive, dominance, additive×additive and monimance×dominance) for plant height, number of tiller plant^-1, number for panicle plant^-1, number of filled grains panicle^-1 and fertility percentage were significant except donimnace×dominance gene effect on the number of grains panicle^-1. Similar dominance results were earlier reported for the number of panicles plant^-1 (Denary et al., 2012), number of tillers plant^-1 (Kumar et al., 2007) and L/B ratio (Rani et al., 2015). Chaturvedi et al. (2010 and Rao et., 2017 reported that days to 50% flowering to maturity, plant height, panicle length, grain filling percentage and length-breadth ratio were controlled by both additive and dominance gene action. In the case of the adequacy of the simple addivtive dominance model, only additive gene effect was significant in grain breadth (mm), straw weight (g) and gain yield plant^-1 (g). Similarly, only dominance gene effect was significant in, days to maturity and number of primary brances panicle^-1. In a number of secondary brances panicle^-1, both dominance and additive gene effects were significant. Mather and Jinks (1971) showed that the classification of interactions on the basis of the related magnitudes and signs of the estimates of the five parameters largely depends of the magnitude and signs of the estimats of h and I. The opposite sign of dominance (h) and dominance×dominance (I) sign indicated a prevalence of duplicate epistasis whereas, in Cross II plant height, number of filled grains panicle^-1, number of grains panicle^-1 and fertility % showed the duplicate type of epistasis. The duplicate effect of these traits would tent to hinder progress at an increased level of manifestation. Similar results were reported for a number spikelet panicle^-1 (Liu and Hong, 2005), for a number for filled grains panicle^-1 (Kumar et al., 2007), for all the yield and quality related traits like plant height (Roy and Senapati, 2011) and a number of grains panicle^-1 (Subbulakshmi et al., 2016). In cross 1 the traits like days to 50% flowering and number of panicles plant^-1 showed the presence of a complementary type of epistasis whereas in cross II the complementary type of epistasis was recorded for the numbers of tillers plant^-1 and number of panicles plant^-1. Subbulakshmi et al., 2016 reported the presence of a complementary type of epistasis for days to 50% flowering. The complementary effect of therse traits will produce new recombinants capable of improving yield.

Conclusion

An additive, dominance, additive×additive and dominance×dominance interaction effects were present along with either duplicate dominant epistasis or complementary recessive epistasis for most of its contributing traits in Cross I and Cross II. Hence, selection in the early segregating generations might not give desirable recombinants. Pureline selection, heterosis breeding, and reciprocal recurrent selection might be profitable in exploiting additive, dominance, both additive and non-additive gene action to obtain desirable recombinants.

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