Bulgarian Journal of Agricultural Science, 12 (2006), 7-12 National Centre for Agrarian Sciences

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1 7 Bulgarian Journal of Agricultural Science, 12 (2006), 7-12 National Centre for Agrarian Sciences Analysis of the Influence of Gene Environment Relations on the Variability of Correlation and Regression Indexes in Crosses between Seed and Seedless Varieties of Grape (Vitis Vinifera L.) V. ROYTCHEV Agricultural University, BG-4000 Plovdiv, Bulgaria Abstract ROYTCHEV, V., Analysis of the influence of gene environment relations on the variability of correlation and regression indexes in crosses between seed and seedless varieties of grape (Vitis vinifera L.). Bulg. J. Agric. Sci., 12: 7-12 We have studied the effect of gene-environment relations on the correlation and regression indexes of the features quantity of sugars and acids in generation parents of crosses between seed and seedless varieties of grapevine. It was established that the effect of genotype-environment relations on correlation indexes is clearly pronounced in varieties such as Armira, Hybrid and offspring in generation of the crosses Super Early Bolgar x Russalka 1 and Hybrid x Russalka. The variety Russalka 1 and the cross Armira x Russalka 1 combine high correlation index to unproved genotype-environment effect. The phenotype regression indexes vary by genotype and by year and the effects of the genotype-environment relations are not well-pronounced, which makes possible their use for forecasting the selection of elite forms without consideration to the conditions of the environment. Key words: genotype-environment, variability, correlation and regression indexes, sugars and acids, crosses, seed and seedless sorts of vine Introduction Correlation and regression indexes are widely used in the process of selection because they offer possibilities for direct and indirect selection depending on correlating quantitative features. Normally used are their phenotype values, which depend on genetic and environmental factors. This complicates the objective interpretation of correlative dependencies, which vary depending on the conditions of the environment. The sources contain examples of various statistical methods of decomposition of phenotype correlations (Rokitsky, 1978; Genchev et al., 1975; Lidanski, 1988), as well as the model of Perkins and Jinks (1968). To use efficiently the various genotypes in selection of vine it is extremely important to analyze the effect of genes in relation with the environment. The quantity of sugars and acids in the grapes

2 8 of dessert varieties of vine has an important impact on its gustatory features and their accumulation in the berries of the grape depends on many factors: variety characteristics, agricultural techniques, environment, etc (Stoev, 1983; Stoev and Zankov, 1983). The purpose of this study is to analyze the effect of genes that interact with the environment on the correlation and regression indexes between the features quantity of sugars and acids in the grapes of parent sorts and generation of hybrids between seed and seedless varieties of grapevine. Materials and Methods The study covers parent varieties P 1, and generation of crosses between seed and seedless dessert grapevine varieties Super Early Bolgar x Russalka, Armira x Russalka 1 and Hybrid x Russalka 1. The analysis of hybrids was conducted during the period at the selection field of the Viticulture Department of the Agricultural University Plovdiv. The quantity of sugars and acids in the grapes of 20 plants was read separately for each cross and each parent variety. The quantity of sugars (s, %) was measured at the stage of technological (ready to eat) maturity with an ABBE laboratory refractometer, and the total quantity of acids (a, g/dm 3 ) by titration with 0.1 n NaOH (Bulgarian Ampelography, 1990). The information about the phenotypic correlation (r k3 ) and regression (b k3 ) indexes was submitted to mathematical analysis, which includes the genes that interact with the environment as well as r and b derived from the mean values of the trait, in which this effect is absent and which in fact represent their genotypic values (Snedecor, 1957; Rokitsky, 1967). The effect of environment interacting genes is characterized by the differences between them ( d1 = r r and d = b k b ) obtained in the different 2 3 years (environments). The reliability of this effect is expressed through the criterion χ 2 by comparing the experimental data r and b k3 to the theoretical one r of 3 k and b while assessing parameters m, ej and d. To calculate χ 2 was used the formula χ = Σ d 1/ S x ( ) V. Roytchev (Mater and Jinks, 1971). To assess parameters m, e j and d, knowing that df = m-3 was used a mathematical model, which decomposes the phenotypic values of correlating traits by environments P xij = m + e j + d + gd, where parameter gd determines the genes that interact with the environment (Perkins and Jinks, 1968; Fedin et al., 1980). In cases where χ 2 = 0 or is not proved, the effect of genes interacting with the environment is missing or is weak. The phenotypic values of the correlation and regression indexes in these genotypes can be used to forecast the selection without consideration to the conditions of the environment, while in cases not proved it leads to errors. Depending on the values of the first correlating trait is established the expected significance of the other trait using the regression equation (y = a + bx) (Lakin, 1990). In the analysis are also applied the genotype values of r and b, which make possible

3 Analysis of the Influence of Gene Environment Relations... 9 the comparison between P 1, and generations best expressed through their regression lines. Results and Discussion The results of the analysis of the effect of environment interacting genes on correlation and regression indexes of the traits quantity of sugars and acids in grape by crosses are shown in Table 1. They prove that the phenotypic correlation indexes ( r ) by years vary within a wide range and, to few exceptions, they are almost always with a negative sign. High Table 1 Values of the correlation coefficients r кз and r кз Hybrid combination I Super Early Bolgar х Russalka 1 II Armira х Russalka 1 III Hybrid х Russalka P 1 P 1 P 1 r кз - by years I II III IV and proved are the values of the variety Russalka 1 and those of the offspring of generation of the crosses Armira x Russalka 1 and Hybrid x Russalka. The correlation dependency in the varieties Super early Bolgar and Rusalka and in the cross between Super Early Bolgar x Russalka 1 is relatively small and not reliable. The correlation indexes calculated from the mean values ( r ) by years also show important variability to the exception of Hybrid Reliability is typical of those of the varieties Russalka 1 and Armira and the crosses Armira x Russalka 1 and Hybrid x Russalka. From the σ (St) χ 2 r df = 1 Р 5% = 3.84 Р 1% = 6.63 Р 0.1% = rкз

4 10 V. Roytchev point of view of genetic selection, particularly interesting is the influence of the effect of genes interacting with the environment on the variability of this parameter expressed by the difference between r and b, which is best illustrated by the criterion χ 2. It offers the possibility to establish the reliability between the experimental and theoretical values of the analysed trait. Interesting for the selection are these varieties and crosses for which Р on < Р 0.1%. The specified criterion is reliable only in the varieties Armira and Hybrid and crosses Super Early Bolgar x Russalka 1 and Hybrid x Russalka. Close to reliability Р 5%, which is the limit, are the varieties Super Early Bolgar and Russalka and the cross Armira x Russalka 1, which means that the genotype-environmental effects are relatively pronounced. This is also confirmed by the Table 2 Values of the regression coefficients b кз and b кз Hybrid combination I Super Early Bolgar х Russalka 1 II Armira х Russalka 1 III Hybrid х Russalka variability of the phenotypic values of r by year expressed through the mean square deviation (σ r ). The value of this parameter is higher in Hybrid followed by the crosses Hybrid x Russalka, Super Early Bolgar, Russalka, Super Early Bolgar x Russalka 1, Armira x Russalka 1 and less in Armira and Russalka 1. In vine selection regression indexes are most used in the choice of source material for sex hybridization and to forecast the selection in heterozygote generations. The values in Table 2 prove that this trait varies within a certain range by cross as well as by year. The reliability of the differences between experimental values of the regression indexes b, which also reflect the effect of the genotype-environment interaction, and b without this effect is expressed by the criterion χ 2. The r кз - by years a кз I II III IV (St) χ 2 σ b P P P df = 1 Р 5% = 3.84 Р 1% = 6.63 Р 0.1% = b кз

5 Analysis of the Influence of Gene Environment Relations results show that it is only in Hybrid the zero-hypothesis is not proved. In all other cases χ 2 < P 5%, which means that the effect of the genotype-environment interaction varies within the tolerance range. The variability of the regression indexes in generation of the different crosses and their parents by year, expressed by the mean square deviation (σ b ), is relatively small, between and for Armira and Armira x Russalka 1, and in the cross Hybrid x Russalka and Hybrid it is in the range Acids, g/dm Fig. 1. Super Early Bolgar х Russalka 1 Acids, g/dm Fig. 2. Armira х Russalka 1 P1 P2 F1 Sugar, % P1 P2 F1 5 Sugar, % Acids, g/m P1 P2 F1 5 Sugar, % Fig. 3. Hybrid х Russalka The values of the regression indexes obtained from the mean values of the traits by year b are negative, to the exception of Hybrid The comparison by parent and by cross describes the variability of the correlation and regression dependencies. This is best illustrated by the regression lines on Figures 1-3. As one can see in these diagrams, the slope of the regression lines is reversed except for the line of Hybrid For the variety Russalka 1 and the crosses Armira x Russalka 1 and Hybrid x Russalka the slope of the regression lines is big, which means that with the quantity of sugar increasing the acids decrease quickly while in the others the variability is small. From the point of view of genetics the comparison between parents and their respective offspring in generation is of particular interest. The regression line of the cross Super Early Bolgar x Russalka 1 is similar to that of Super Early Bolgar and the line of Armira x Russalka 1 is similar to that of the variety Russalka 1 while the line of the cross Hybrid x Russalka is quite different from the parents one.

6 12 V. Roytchev Conclusions The phenotypic correlation indexes of the variety Russalka 1 and the crosses Armira x Russalka 1 and Hybrid x Russalka are proved at the 3 rd 1 st rank. Relatively high but not proved are those of the varieties Hybrid and Russalka. Similar are the indexes obtained from the mean values by year where proved are the varieties Russalka 1 and Armira as well as the crosses Armira x Russalka 1 and Hybrid x Russalka. The effect of the genotype-environment relation on the correlation indexes is pronounced in the varieties Armira and Hybrid and in offspring of generation of the crosses Super Early Bolgar x Russalka 1 and Hybrid x Russalka, while in the other varieties and crosses the effect of the genotype-environment relation are relatively less pronounced. Of particular value for the selection are the variety Russalka 1 and the cross Armira x Russalka 1, which combine high correlation index to unproved genotype-environment effect. The phenotypic regression indexes vary by genotype and by year. The effect of the genotype-environment interaction is not much pronounced while the zero-hypothesis is highly proved, which makes it possible to use them as base for forecast of the selection without consideration to the conditions of the environment. The slope of the regression lines for the separate crosses, to the exception of that for Hybrid 28-13, are reverse and this means that when the quantity of sugars in the grape increases the acids decrease. This dependence is the best pronounced in the variety Russalka 1 and in the crosses Armira x Russalka 1 and Hybrid x Russalka. Received August, 18, 2005; accepted December, 19, 2005 References Bulgarian Ampelography General Ampelography. Publ. BAS, Sofia, Vol. 1, 296 pp. (Bg). Fedin, М. А., D. Ya. Silis and A.Smiryaev, Statistical Methods of Genetic Analysis. Moscow, Kolos, 207 pp. (Ru). Genchev, G., E. Marinkov, V. Iovcheva and A. Ognjanova, Biometrical Methods in Plant Growing, Genetics and Breeding. Sofia, Zemizdat, 322 pp. (Bg). Lakin, G. F., Biometry. Moscow, Visshaya shkola, 352 pp. (Ru). Lidanski, T., Statistical Methods in Biology and Agriculture. Zemizdat, Sofia, 374 pp. (Bg). Mather, K. and J. L. Jinks, Biometrical Genetics: The study of continuous variations. - New Jork: Cornell University Press, 382 pp. Rokitsky, P. F., Biological Statistics. Minsk, Vysheishaya shkola, p. 326 (Ru). Rokitsky, P. F., Introduction to Statistical Genetics. 2 nd edition, Minsk, Vysheishaya shkola, p. 448 (Ru). Perkins, J. M. and J. L. Jinks, Environmental and genotype - environmental components of variability III. Multiple lines and crosses. Heredity, 23: Perkins, J. M. and J. L. Jinks, Environmental and genotype - environmental components of variability IV. Non-linear interactions for multiple inbred lines Heredity, 23: Stoev, K., Basic regularities of growth and maturation of grapevine berries. Grapevine physiology and principles of its cultivation. Sofia, Bulgarian Academy of Science, vol. 2: Stoev, K. D. and Z. D. Zankov, Physiology of the growth and development of seed and offspring of grape. Physiology of grape and fundaments of its reproduction, Bulgarian Academy of Science, vol. 2: Snedeco,r G.W., Statistical metods applied to experiments in agriculture and biology. The Iowa State College Press. Ames Iowa, 534 pp.