ISSN: 39-7706 Volume 4 Number (05) pp. 79-87 http://www.ijcmas.com Original Research Article Using Triple Test Cross Analysis to Estimates Genetic Components, Prediction and Genetic Correlation in Bread Wheat Marwa M. El-Nahas Crop Sci. Dep., Faculty of Agric., Minufiya University, Corresponding author A B S T R A C T K e y w o r d s Wheat, Triple test cross analysis, Epistasis, additive, Dominance, Prediction and genetic correlation Studies were conducted in 0/, 0/3 and 03/4 to detect epistasis, additive and dominance genetic effects for eight quantitative traits using triple test cross analysis and predicted a new recombinant lines and genetic correlation between this traits. Significant epistasis is presented in most traits studied. Additive additive epistatic type was found to be much larger in magnitude than additive dominance and dominance dominance (J + L) epistatic types for all traits studied. Both additive (D) and dominance (H) genetic components play an important role in the inheritance of all traits studied except number of kernels per and grain yield per. The average degree of dominance (H/D) / was in the range of partial dominance for all traits studied. Predicting revealed that it could be possible to derive reasonable proportion of new recombinants which are falling out side parental range for grain yield per, no. of kernels per, no. of s per, 000-kernels and yield. Genetic correlation revealed that additive and epistasis genetic correlations among some traits suggesting common genetic pool. Thus, selection based on additive genetic correlation indicated that indirect selection via, yield, 000-kernels and grain yield per would be effective. Introduction Triple test cross is one of the best design for detecting and estimating genetic components of variation for quantitative traits. So many wheat breeders have been used T.T.C analysis in wheat that proposed by Kearsey and Jinks (968). Information of the type of gene action involved in the inheritance of traits is helpful in deciding the breeding procedures to be followed for improvement and is necessary for efficient utilization of available germplasm in a breeding programe. Therefore, the presence of epistasis (additive additive, additive dominance, dominance dominance) should be studied precisely before deciding any breeding programe. T.T.C is considered one of the most efficient model as, it provides not only a precise test for epistasis, but also unbiased estimates of additive and dominance components if epistasis is absent (Singh and Youns, 986). 79
The aim of many selfing breeding programmes is to produce recombinant inbred lines to be used directly or in producing F hybrid or multiple cross hybrid. The best source of the genetical parameters required for predicting the properties of recombinant inbred lines is the triple test cross (Pooni and Jinks, 979). The knowledge of genetic correlation, which occurs between characters, can help the breeder to improve the efficiency of selection by using favorable combinations of traits and to minimize the retarding effect of negative correlations. The reliability of genetic components estimated from T.T.C makes computed correlations from them more reliable. In wheat, the correlation of components of genetic variance was computed using T.T.C analysis by Eissa (994c), Al-Kaddoussi (997), Menshawy (008), Morad (0) and Dawwam et al. (05). The objectives of this study were: ) Existence of epistasis and to determine the additive (D) and dominance (H) variances of quantitative traits in wheat. ) To make prediction for traits studied that help the breeders to identify the favorable combinations to improve the efficiency of selection. 3) To compute the genetic correlation among various traits and partitioning it to epistasis, additive and dominance correlations. Materials and Methods The present study was conducted at the Experimental Farm, Faculty of Agriculture, Minufiya University at Shebin El-Kom, during the three successive seasons of 0/0, 0/03 and 03/04. In the first season (0/0), two genotypes of bread wheat, differ in most of their agronomic traits namely Gemmeiza and Masr, were crossed to obtain their F progeny (Gemmeiza Masr ) to be used as three testers. The pedigree of bread wheat genotypes are illustrated in table. Fifteen wheat varieties, namely Gemmeiza 9, Gemmeiza 0, Sahel, Shandaweel, Giza 67, Giza 68, Sakha 8, Sakha 6, Sakha 69, Sakha 94, Sids 4, Sids 6, Sids 8, Sids 9 and Sids each was crossed to the three testers Gemmeiza (P), Masr (P) and their F (Gemmeiza Masr ) to generate 45 crosses i.e. 5 Li, 5 Li and L3i progeny families of a triple test cross design in 0/03 winter growing season. All materials, the forty five families (crosses), their fifteen parents and the three testers were grown in a randomized complete block design with three replicates in 03/04 winter growing season. Each progeny family was growing in 3m long row. The spacing between (row to row distance) and within ( to distance) rows were maintained at 30 and 0 cm, respectively. All the normal agronomic practices were followed as usual in the ordinary wheat field in the area of study. Data were scored on ten guarded s from each row in each replications for the eight characters, heading date (days), height, no. of s per, length, no. of kernels per, yield, 000-kernels and grain yield per. Statistical analysis The detection of epistasis was done according to the method outlined by Kearsey and Jinks (968) and is based on genetic model; L ijk = M + G ij + R k + E ijk Where, L ijk = Phenotypic value of cross between tester i and line j in k replication. 80
M = Overall mean of all single and three way crosses. G ij = Genotypic value of cross between tester i and line j. R k = Effect of k th replication. E ijk = Error. The mean squares for deviations L i + L i L 3i was used for detection of epistasis. The overall epistasis was partitioned into (i) type of epistasis (additive x additive) and (i + j) type due to additive x dominance and dominance x dominance gene interactions. The estimation of additive (D) and dominance (H) genetic components and the correlation coefficient (r) between sums L i + L i and differences L i - L i were obtained to detect the direction of dominance, according to Jinks and Perkins (970). The degree of dominance was calculated as (H/D) /. The proportion of superior inbreds, that outperform their parental range, is equal to the normal probability integral corresponding to the value [d] / D while, the range of inbred lines is m ± D wherem L and [d] = L - L (Jinks and = 3 Ponni, 976). The proportions of recombinant lines corresponding to the probability level were obtained using Fisher and Yates (963) tables. Additive (Ra), dominance (Rd) and epistasis (Ri) correlation coefficients were computed from ( L + L + L ), ( L - 3i i L )and ( i respectively. i L + i Results and Discussion i L L ), i 3i The analysis of variance for all traits studied is presented in table. Genotypes, hybrids and parents mean square estimates were found to be highly significant for all traits studied, indicating the presence of variability among hybrids and their parents. Testers mean squares were found to be highly significant for all traits except no. of kernels per. The mean performance of the two parents Gemmeiza and Masr (P vs P) were significantly different from each other in all traits except no. of kernels per. The unbiased estimates of additive and dominance gene action and unambiguous test of epistasis would only be achieved when the testers are different from each other. However, when this condition of difference between two parents is not met, the estimates are biased to an unknown extent (Kearsey and Jinks, 968; Jinks et al., 969). Test for epistasis The analysis of variance for testing the presence of epistasis in the inheritance of all traits studied is presented in table 3. The mean square for the deviations L i + L i L 3i revealed the presence of significant epistasis for all traits studied except height. Partitioning of the total epistatic effect revealed the presence of highly significant additive additive (i) type of epistasis for all traits studied. Also, estimates of additive dominance and dominance dominance, J + L types of epistasis mean square were highly significant for all traits studied except height and no. of kernels per. The additive additive epistatic type (i) was found to be much larger in magnitude than additive dominance and dominance dominance (J + L) epistatic types for all traits studied, indicating that fixable components of epistasis were more important than non-fixable one in the inheritance of these traits. The same results were reported by Eissa (994a,b), El-Nahas (005), Esmail (007) and Morad (0). 8
Detection of genetic variance components Analysis of variance for sums (L i + L i ) and difference (L i - L i ) are presented in table 4. The mean square due to sums were found to be highly significant for all traits studied, indicating the presence of additive genetic variance for these traits. The mean square due to difference were also found to be highly significant for all traits studied except no. of kernels per and grain yield per, indicating the importance of dominance genetic variance for these traits. The additive genetic variance (D) was found to be much larger in magnitudes than the dominance variance (H) for all traits studied. Consequently, it could be concluded that selection procedures based on accumulation of additive effects would be successful in improving all traits studied. However, to maximize selection advance, procedures which are known to be effective in shifting gene frequency when both additive and nonadditive genetic variance are involved would be preferred. Similar results were previously obtained by Esmail (007), El Massry (009), Koumber (0), Morad (0) and Dawwam et al. (05). The degree of dominance (H/D) / was less than unity for all traits studied suggesting the role of partial dominance in the inheritance of these traits and as certain the fact that in self pollination crops, most genes are homozygous and the over-dominance is rare. Further, the correlation coefficient between the sums (L i + L i ) and difference (L i - L i ) were found to be positive and significant for no. of s per indicating that dominance seemed to be acting in one direction. However, the correlation coefficient for the remaining traits was insignificant indicating the genes with positive and negative effects were equally distributed among the genotypes including in this study. Prediction of superior recombinants: Triple test cross is the useful sources for such information to make prediction of new recombinants line. These informations will allow predictions of the proportion of inbreds which as good as or superior to better parent of F hybrid. Prediction results given in table 5, revealed that it could be feasible to predict as early as possible for transgressive segregates and the highest proportions of recombinants which outperform parental range for grain yield per (49.0%), no. of kernels per (48.40%), no. of s per and 000- kernels (47.60%), yield (47.%), length (43.64%), height (43.63%) and heading date (4.9%). For traits studied, the range of inbred likely to exceed parental range was nearly 40%. The obtained high proportion could be explained that the studied wheat cultivars have common genetic pool, and the prevalence of additive gene effects for most traits studied refer that, selection imposed for the traits studied was to intermediate performance. Thus the breeder should give a great emphasis to the promising cross are the most frequent ones and having high values for new recombinants for yield, therefore, the breeder should pay great emphasis for considering these promising cross in wheat breeding program. In this respect, a reasonable proportion of new recombinants could be predicted for yield and its components in wheat by Eissa (994c), Al-Kaddoussi (997), Menshawy (008) and Dawwam et al. (05). Genetic correlation The kind of relationships, which may occur among characters, is important for selection breeding programs. Partitioning of the total genetic correlation to its components of 8
additive, dominance and epistasis genetic correlation illustrated in table 6. The results obtained provide evidence for positive and significant correlation between additive gene effects controlling between height and heading date, between no. of s per and heading date, between no. of kernels per, between yield and each of length and no. of kernels per, between 000-kernels with those of length, no. of kernels per and yield, between grain yield per with those of no. of kernels per, yield and 000-kernels. Table. The origin and pedigree of the studied parental bread wheat varieties Pedigree Ald S /Huac S //CMH74A.630/5X CGM4583-5GM-GM-0GM MAYA74 S /ON//60-47/3/BB/GLL/4/CHAT S /5/CROW S. NS.73/PIMA// Vee"S". SD 735-4SD-SD-SD-0SD SITE//MO/4/NAC/TH.AC//3PVN/3/MIRLQ/BUC.CMSS93B00567 S-7Y-00M-00Y-00M-OHTY-OSH Au/UP30//G/SX/Pew S /4/Mai S /May S //Pew S CM6745-C- M-Y-M-7Y-M-0Y ML/BUC//SeriCM93046-8M-0Y-0M-Y-0B INDUS 66 / NORTENO S. PK 348-6S- SW-0S Inia/RL40//7C/Yr S CM5430-5-55-0S-0S Inia/RL40//7C/Yr S CM5430-5-65-0S-0S OPATA/RAYON//KAUZ.CMBW90Y 380-0TOM-3Y-00M-00Y- 0M-05Y-0Y-0AP-0S. Maya S /Mon S / CMH74.A.59/3/CHZa57 Maya S /Mon S /CMH74.A59/3/Sakha8SD000-4sd-3sd-sd- 0sd Maya S /Mon S /CMH74.A59/3/Sakha8SD000-4sd-3sd-sd- 0sd Maya S /Mon S /4/CMH47-48/MRC//Jup/3/CMH47A-58/5/Giza- 57 BUC//7C/ALD/5/MAYA74/ON//60.47/3/BB/GLL/4/CHAT S /6/ MAYA/VUL//CMH74A.630/4SX. SD7096-4SD-SD-SD-0SD. BOW S /KVZ S //7C/SER8/3/GIZA68/SAKHA6 GM789-GM-GM-GM-GM-0GM (P ) OASIS/KAUZ//4BCN/3/PASTOR. CMSS00Y088T-050M-030Y-030M-030WGY-00M-0Y-0S (P ) (P P ) Origin Name lines Gemmeiza 9 Gemmeiza 0 Sahel Shandaweel Giza 67 Giza 68 Sakha 8 Sakha 6 Sakha 69 Sakha 94 Sids 4 Sids 6 Sids 8 Sids 9 Sids testers Gemmeiza Masr F No. 3 4 5 6 7 8 9 0 3 4 5 3 83
Table. Mean squares of the analysis of variance of (Li, Li and L3i) triple test cross hybrids for all traits studied Grain yield/.34 58.9.9 59.75.43 000- kernels 0.55 86.99 0.00 6.07 606.50 yield 0.6 3.88 4.9 3.05 0. kernels/ 45.33 700.38 76.5 559.9 965.05 length 0.7 5.6 4.98 7.98.7 s/ 0.3 7.09 5.03 9.39 78.6 33.36 96.33 3.50 673.9 8.46 3.75 0.94. 0.78 5.6 7.0 6.68 396.8 75.30.3 5.98 33.30 53.77 7.4 36.65 0.69 6.30.44 7.87 3.7 3.6 0.09.03 0.0 0.46, Significant at 0.05 and 0.0 probability levels, respectively Plant height 57.78 66.43 38.9 8.56 30.68 33.56 55.63 304.37 43.97 9.09 Heading date (days) 0.9 7.90 89.5 6.3 36.86 34.0 5.3 347.39.70 0.9 d.f 6 44 7 4 4 Sours of variance Replications Genotypes Hybrids Parents Hybrids vs parents Lines Testers Line vs tester P vs P Error Table.3 Analysis of variance for testing the presence of epistasis in a triple test cross for all traits studied Grain yield/ 9.0 9.5 8.7 3.3 6.9 7.35 000- kernels 43.66 48.8 6.9 0.87.60.48 yield.4 8.67 0.9 0.045 0.079 0.077 kernels/ 45.50 53.37 73.5 3.64 66.8 6.60 length 8.53 86.4.98 0.3 0.34 0.3 s/ 6.4 47.8 3.46 0.03..4 Plant height 53.3 56.98 45.9 79.44 7.6 3.08, Significant at 0.05 and 0.0 probability levels, respectively. (I) = additive x additive, (L) = dominance x dominance, (J) = additive x dominance Heading date (days) 0.37 754.5 54.74 0.08 0.36 0.34 d.f 5 4 3 4 45 Sours of variance Total epistasis I type epistasis j+i type epistasis I type epistasis x block j+i type epistasisx block Total epistasisx block 84
Table.4 Mean squares from analysis of variance for sums and differences and estimates of additive (D), dominance (H) and degree of dominance in triple test cross for all traits studied Grain yield/ 500.55 6.93 7.0 000- kernels 785.8.00 8.4 yield 7.65 0.6 0.73 kernels/ 3008.96 7.8 38.66 length 59.5 0..8 s/ 99.63 0.78 3.64 Plant height 85.39.0 45.00 5.69.68 0.6 4.65 0.3 0.50.6 658.6 045. 3.3 3975.70 79.9 3.80 350.9.87 7.65 0.74 8.68.7 4.9 43.77 0.05 0.08 0.8 0.06 0. 0.7 0.35-0.44-0.3 0.7 0.5 0.5 0.56-0.9, Significant at 0.05 and 0.0 probability levels, respectively. r = correlation coefficients between sums (Li + Li) and differences (Li - Li). Heading date (days) 345.9 0.3 6.0 0. 460.8 34.38 0.7 0.04 d.f 4 8 4 8 Sours of variance Sums (L+L) Error Differences (L-L) Error D H (H/D) 0.5 r Table.5 Predicting the range of inbred lines and the proportion of inbreds expected to fall outside their parental range for the traits studied Proportion of inbreds falling outside parental range% 4.9 43.63 47.60 43.64 48.40 47. 47.60 49.0 probability 0..7 0.06-0.6 0.04-0.07-0.06-0.0 Range of inbred 98.6.3 377.8 30.90 55.59 9.67 6.4 5.56 346.68 94.47 0.99.69 95.4 65.84 5.5.9 (D) 460.8 350.9 3.80 79.9 3975.70 3.3 045.0 658.6 (d) 4.74 3.05 0.69 -.49.56-0.38 -.05-0.76 (m) 55.3 340.36 3.63 43.35 0.58.34 30.49 74. parameters Traits Heading date Plant height s/ length kernels/ yield 000-kernels Grain yield/ 85
Table.6 Additive (Ra), dominance (Rd) and epistasis (Ri) correlation coefficients among eight traits in the triple test cross 000- kernels yield kernels/ length s/ Plant height Heading date Typ e Traits 0.67 Ra -0. Rd Plant height -0.367 Ri 0.380 0.660 Ra s/ -0.0-0.4 Rd -0.00-0.90 Ri -0.495 0.09-0.57 Ra -0.334-0.05-0.04 Rd length -0.68 0.308-0.40 Ri 0.666-0.8-0.38-0.50 Ra -0.39 0.048 0.44 0.050 Rd kernels/ -0.04 0.68-0.4 0.04 Ri 0.743 0.873-0.57-0.058-0.45 Ra -0.093 0.78-0.7 0.357-0.06 Rd yield 0.3 0.34 0.093-0.389-0.63 Ri 0.88 0.95 0.800-0.56-0.50-0.50 Ra 000-kernels -0.080-0.38 0.3 0.09-0.76-0.09 Rd -0.60-0.50-0.003-0. 0.40 0.33 Ri 0.766 0.637 0.7 0.467-0.005-0.35-0.45 Ra Grain yield/ -0.43-0.03 0.6-0.069 0.38 0.64 0.36 Rd 0.30-0.96 0.06-0.4 0.546-0.098 0.78 Ri, Significant at 0.05 and 0.0 probability levels, respectively. Concerning the dominance genetic correlations, the results didn t have any positive and significant additive correlation between the traits in this investigation. Regarding epistasis genetic correlation the results indicated positive and significant correlation only between grain yield per and no. of per. From the results of genetic correlation, most of the characters were not associated with each other and confirmed that the T.T.C matting system was useful in breaking up undesirable linkage groups to obtain new recombinant lines. In this regard, Eissa (994c), Menshawy (008), Morad (0) and Dawwam et al. (05) reported the efficiency of triple test cross for obtaining new recombinant lines in wheat. These investigation was designed to use triple test cross analysis to obtain additional information about type of gene actions, genetic correlation and predicting the likely performance of new recombinants that could be derived after series of selfing generations. Thus, this study helps breeder for rightful decision about effective breeding method to be applied for improving yield and its contributing traits. Reference Al-Kaddoussi, A.R. 997. Testing for epistasis, predication and genetic correlation using North Carolina Design III. Biometrical approach for ian bread wheat (Triticum aestivum L.). Zagazig J. Agirc. Res., 86
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