Evaluation of Gene Actions of Some Traits Contributing in Drought Tolerance in Bread Wheat Utilizing Diallel Analysis

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1 Available online at Annals of Biological Research, 2012, 3 (7): ( ISSN CODEN (USA): ABRNBW Evaluation of Gene Actions of Some Traits Contributing in Drought Tolerance in Bread Wheat Utilizing Diallel Analysis Maryam. Mohammadi Sarab Badieh 1, Ezzatollah Farshadfar 1, Reza Haghparast 2, Rahman Rajabi 2 and Leila Zarei 1 1 College of Agriculture, Razi University, Kermanshah, Iran 2 Dryland Agricultural Research Institute, Sararood Station, Kermanshah, Iran ABSTRACT In order to identify drought tolerant genotypes and study of genetic attribute and heritability of some important traits in stress and non-stress, this investigation was counducted utilizing eight-parents half-diallele cross in randomized complete block design with three replications under irrigated and rainfed s. Irrigation was done during grain filling to avoid terminal water stress. Many traits were studied. Data pertaining to the parents and F1s were subjected to Hayman methods for the genetic analysis. In this study, simultaneous additive and dominance effect of genes controlling prolin amino acid content, spike length, grain yield, relative water content (RWC) and additive effects of genes controlling plant heigh were identified. Key words: Bread wheat, Drought tolerance, diallele, Hayman method INTRODUCTION Information about morpho-physiological traits and the gene effects controlling the highly related traits to drought tolerance makes breeding programs for drought tolerance much more effective and successful. More knowledge about the genetic of these traits, more accurate and effective selection for drought tolerance and creating genetic variation for it. The most informative genetic knowledge in breeding programs are additive and dominance variance, gene effects, general and specific combining ability (GCA and SCA) are calculated by diallele methods. The objectives of this study were to estimate the heritability, GCA and SCA and the mode of gene actions controlling some related traits to drought tolerance utilizing Hayman diallele cross analysis method [5, 6, 12]. MATERIALS AND METHODS A eight-parent diallele cross excluding reciprocals using bread wheat pure lines namely Sabalan/6/shahi/KV2/5/shahi/4, T163, T189, 914 GBM, 4848 Mashhad/tui"S"IRW921D6, 72 YRRGP, Chonab, Sardari selected from the germplasm of Dryland agricultural research institute, Sararood station, Kermanshah, Iran, were made in college of agriculture, Razi University, Kermanshah, Iran during the years 2005 and Crosses were made using hand emasculation. The Parents and F1 were grown under water stress and non-stress s. The experimental material was seeded in a RCBD with three replication in one row plots, 1m in length with row space 50cm apart in the field. Twenty seeds per plot planted. To expose one set of the experiment to terminal water stress which is the most concerning biotic stress in this region, both stress and non-stress sites were under irrigation 3591

2 until flowering stage, but onward the irrigation for stress were stopped. Five randomly plants from each plots selected for measuring, plant heigh, grain yield, prolin amino acid content, spike length, relative water content(rwc) were recorded. The mean observation recorded in each replication were subjected to analysis of variance based on RCBD. Mode of gene action and combining ability analysis were carried out for both stress and non-stress s using GriffingII (1956) and Hayman method (Morley and Jones method). Hayman method was carried out on the traits with high significant differences among the genotypes. In this technique, total sum of square is partitioned into various components, namely a (additive), b (non-additive). b component is further subdivided into b1, b2, and b3. Significant amount of a and b components, show the significant additive and dominance effect of genes, respectively. Significant b1 indicates unidirectional dominance which it is in fact a comparison of F1 s mean with mid-parental value. Asymmetry of gene distribution is indicated by the component b2, whereas b3 tests that part of dominance deviation which are not attributable to b1 and b2. Further analysis of data using Vr ( variance of the crosses involving a particular parents or variance of each array) and Wr ( covariance between parents and their offspring, which Wr is the covariance of the array with non-recurring parents) were done. Array refers to the crosses in which a particular parent is involved. The relationship between Wr and Vr provides some very useful information. Therefore, the Wr value plotted against the corresponding value of Vr ( Wr,Vr - graph). The genetic parameters that were estimated by Hayman method are: D, the sign of additive variance, H1 and H2, different type of non-additive or dominance variance. Significance deviation of D from zero, indicates existence of additive effect and significant H1 and H2 shows existence of dominance effect in controlling traits and the deviation of H2 from H1 shows unequal frequency of dominance and recessive alleles in controlling traits. RESULTS Relative Water Content (RWC) The components a and b for this trait was significant, indicating significance of additive and dominance effects in controlling RWC. Moreover, components b2 was significant revealing the asymmetry of gene distribution and H1>H2. Significant b3 showed that SCA for RWC was significant (Table 1). Non-significant b1 showed nonsignificant difference between average of parents and F1s, indicating absence of heterosis. D parameter was not significant but H1 and H2 were significant, revealing the importance of non-additive effects in controlling RWC (Table 3). Over-dominance effects were indicated by amount of degree of dominance ((H1/D)^0.5 >1). H2b and h 2 n in stress and non-stress were 0.63, 0.19, 0.65 and 0.16, respectively (Table 3). Significant GCA, SCA and the ratio GCA /SCA confirmed this result (Table 2) [3]. Graphic analysis indicated the over-dominance gene action in controlling RWC. Parent 1 and 2 were the farthest parent of the origin, due to this fact that this parent contained an excess of recessive genes and parents 5 and 8 contained an excess of dominant genes for controlling RWC (Fig 1,2). Figure 1- Covariance-variance graph for RWC in stress Figure 2- Covariance-variance graph for RWC in non-stress 3592

3 Plant Heigh Significant a and non-significant b in stress random non-sterss s indicated the significant effect of additive effect of involving genes (Table 1) [2, 8]. Even, non-significant GCA/SCA showed the importance of non-additive effect of genes in controlling plant heigh (Table 2). Component b for this trait was non-significant for this reason, genetical parameters and graphical analysis were impossible(table 3). Prolin Amino Acid Content significant GCA,SCA and the ratio GCA /SCA confirmed this result. significant GCA/SCA showed the importance of additive effect of genes in controlling Prolin Amino Acid Content(Table 2). Positive GCA of parents 4,3,8 and 5 in non-stress and parents 5,3 and 4 in stress indicated the role of these parents in increasing Prolin Amino Acid Content. more amount of Prolin Amino Acid Content has positive effect in drought tolerance, thus to increase Prolin Content these parents could be use to improve drought tolerance. The components a and b for this trait was significant, indicating significance of additive and dominance effects in controlling prolin content(table 1). Moreover, components b2 was significant revealing the asymmetry of gene distribution and H1>H2. Non-significant b1 showed non-significant difference between average of parents and F1s, indicating absence of heterosis. D parameter was not significant but H1 and H2 were significant, revealing the importance of non-additive effects in controlling prolin content (Table 3). Positive F showed more frequency of dominance allele in parents. Existence of over-dominance effects were indicated by amount of degree of dominance. H 2 b and h 2 n in stress and non-stress were 0.80, 0.40, 0.71 and 0.32, respectively (Table 3). Graphic analysis indicated the over-dominance gene action in controlling for this trait (Fig 3,4). Figure 3- Covariance-variance graph for Prolin Amino Acid Content in stress Figure 4- Covariance-variance graph for Prolin Amino Acid Content in non-stress Spike Length The components a and b for this trait was significant, indicating significance of additive and dominance effects in controlling for this trait(table 1) [2]. D parameter was not significant but H1 and H2 were significant, revealing the importance of non-additive effects in controlling spike length (Table 3). Positive F showed more frequency of dominance allele in parents. Existence of over-dominance effects were indicated by amount of degree of dominance. H 2 b and h 2 n in stress and non-stress were 0.78, 0.12, 0.76 and 0.32, respectively (Table 3). significant GCA,SCA confirmed this result. Ratio GCA/SCA showed the importance of additive effect of genes in controlling spike length (Table 2). Positive GCA of parents 7, 3, 2 and 6 in non-stress and parents 7 and 3 in stress indicated the role of these parents in increasing spike length. more amount of spike length has positive effect in grain yield, thus 3593

4 to increase spike length these parents could be use to improve yield. Graphic analysis indicated the over-dominance gene action in controlling for this trait (Fig 5,6). Figure 5- Covariance-variance graph for spike length in stress Figure 6- Covariance-variance graph for spike length in non-stress Grain Yield Significant GCA, SCA and the ratio GCA /SCA confirmed this result. Positive GCA of parents 5,7,8 and 3 in stress and non-stress indicated the role of these parents in increasing grain yield, thus to these parents could be use to improve yield [1, 10, 11]. The components a and b for this trait was significant, indicating significance of additive and dominance effects in controlling yield (Table 1). D, H1 and H2 were significant indicating contribution of both additive and dominance effect in controlling for this trait (Table 3). But H1> D which indicates more contribution of the over-dominance effect in controlling this trait in compare to additive effect [7]. Positive F indicates that dominant alleles are more frequent than recessive ones [9]. Broad sense ( h 2 b) and narrow sense ( h 2 n) were estimated 94 and 55 percent in stress and 95 and 49 percent in non-stress, respectively (Table 3) [2, 3]. Graphic analysis indicated the over-dominance gene action in controlling yield. Parent 2 were the farthest parent of the origin, due to this fact that this parent contained an excess of recessive genes and Parents 1 and 6 contained an excess of dominant genes for controlling yield(fig 7,8). Figure 7- Covariance-variance graph for grain yield in stress Figure 8- Covariance-variance graph for grain yield in nonstress 3594

5 Table 1- ANOVA of Hayman method (Morley and Jones) s.o.v Df Prolin Content Spike Length RWC Plant Heigh Grain Yield N 1 S 2 N S N S N S N S a ** 1.15 ** 3.08 ** 0.93 * ** ** ns * ** ** b ** 0.52 * 1.02 ** 1.36 ** * ** ns ns ** ** b ns 0.44 ns 0.04 ns 3.21 ** ns 0.87 ns ns ns ** ** b ** 0.54 ns 0.38 ns 2.06 ** ns ** ns ns ** ** b ** 0.52 * 1.29 ** 1.02 ** * * ns ns ** ** error Non-stress 2 Stress Table 2- general and specific combining ability (GCA and SCA) S.O.V. df Prolin Content Spike Length RWC Plant Heigh Grain Yield N 1 S 2 N S N S N S N S GCA ** ** 5.08 ** 1.39 ** * ns ns ns ** ** SCA * 4.80 ** 2.88 ** 1.02 ** * ** ns ns ** ** Error GCA/SCA 2.09 ns 4.09 ** 1.76 ns 1.36 ns 1.69 ns ** 9.5 ** 1 Non-stress 2 Stress Table 3- genetical parameters Parameters Prolin Content Spike Length RWC Grain Yield N 1 S 2 N S N S N S D 0.25 ns 0.57 ns 0.37 ns 0.42 ns 6.56 ns ns ** ** H ** 7.10 ** 1.05 ** 1.83 ** * * ** ** H * 5.04 * 1.04 ** 1.27 ** * * ** ** F 0.17 ns 0.01 ns 0.01 ns 0.86 * ns ns ns ** (H 1/D)^ h 2 b h 2 n Non-stress 2 Stress DISCUSSION AND CONCLUSION According to Hayman analysis of variance a and b were significant for the testing traits, which it means the role of additive and dominance effects in controlling these traits. Significant b3 indicated more contribution of non-additive effect (dominance) of alleles in controlling these traits. The highest amount of h 2 b belonged to grain yield (94%). grain yield, relative water content, prolin amino acid content, spike length, were in both stress and non-stress s were under control of additive and dominance effect of alleles but the contribution of dominance effect was more. Hayman diallele analysis revealed that in controlling grain yield, relative water content, prolin amino acid content, spike length simultaneous additive and dominance effects are involved and in plant heigh additive effect play much more important role. REFERENCES [1] R.J. Baker, Crop Science, 1978, 18: [2] M.A. Chowdhry, M.T. Arshad, G.M. Subhani, I. Khaliq, Plant Sci, 2000, V7(3-4), [3] E. Farshadfar, M. Farshadfar, j. Sudtka, Hungarica, 1996, [4] B. Griffing, Biol Sci, 1956, 9: [5] B.I. Hayman, Genetics, 1954, 39: [6] J.L. Jinks, I. Hayman, Newsletter, 1953, 27: [7] K.A. Kheiralla, Assiut Jornal of Agricultural Sciences, 1994, 25:5, [8] S.F. Ma, Ning Xia Journal, 1989, [9] U.D. Nasir, B.F. Carver, A.C. Clatter, Eaphyca, 1992, 62: [10] A. Saarafi, R. Ecoohard, C. Planchon, Wheat, Barely and Triticale Abs, 1986, 4(5):3035. [11] S.K. Sharma, S. Iqbala, K.P. Singh, Crop Improvement, 1980, 13(1):

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