Generation Mean Analysis for quantitative traits in the population of AKDRMS 21-54 x YH3 cross of rice (Oryza sativa L.)

Abstract

Aim: To study the genetic components of inheritance for 12 quantitative traits which leads into genetic improvement by identification of gene actions for yield and yield related traits. Methodology: Twelve quantitative traits for yield and yield attributing traits were studied at Agricultural College, Bapatla during Rabi 2021-22 deploying Hayman's five parameter model generation mean analysis in estimating the gene effects in the population (P1, P2, F1, F2 and F3) generated from the cross AKDRMS 21-54 x YH3. Estimated mean effects affirm the existence of significant variation in the traits studied. Significance of either one or both, scales C or D inferred the existence of epistatic interaction for the traits studied. Digenic non-allelic interaction model explained the gene action of the traits studied in the present investigation. Results: The generation mean for all the traits showed the importance of both additive and dominance type of gene effects. Among the epistatic gene effects, the additive x additive gene interaction was reported predominant for shoot length, plant height, flag leaf length, spikelet fertility and flag test weight. The gene interaction is associated with homozygosity, therefore, pedigree method of breeding may be adopted for isolation of desirable lines, while for all other traits, along with grain yield per plant, dominance x dominance gene interaction was found to be pre-dominant. The gene interaction is not fixable, hence, population improvement approaches, would be effective. Interpretation: Dominance x dominance gene interaction was predominant for grain yield per plant and majority of yield component traits studied coupled with duplicate epistasis, infers to takeup population improvement approaches, such as bi-parental mating and recurrent selection, followed by isolation of purelines in later generations for competent improvement. Key words: Epistasis, Five parameter model, Generation Mean Analysis, Quantitative traits, Rice