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INTRODUCCION A LA GENETICA CUANTITATIVA FALCONER PDF

Introduccion a la Genetica Cuantitativa by D. S. Falconer at – ISBN – ISBN – ACRIBIA – – Softcover. DownloadIntroduccion a la genetica cuantitativa falconer pdf. PDF 5. Quarantine 13A 57EF exe C WINDOWS Temp NavBrowser. Introduccion a. Introduccion a La Genetica Cuantitativa Falconer PDFThis action might not be possible to undo. Are you sure you want to continue?.

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Using the best linear predictor BLP in the selection between and among half-sib progenies of the CMS maize population. Send correspondence to C. There was a tendency in the BLP methodology to select a greater number of related progenies because of the previous generation pedigree than the other method. This implies that greater care with the effective size of the population must be taken with this method.

The SAWHSP methodology ccuantitativa efficient in isolating the additive genetic variance component from the phenotypic component. The pedigree system, although unnecessary for the routine use of the SAWHSP methodology, allowed the prediction of an increase in the inbreeding of the population in the long term SAWHSP selection when recombination is simultaneous to creation of new progenies.

Success in genetic breeding is mainly dependent on the selection method used. Several methods have been suggested and modified in recent years for greater selection efficiency using the statistical properties most suitable for different experimental situations. Among known methods, selection among and within half-sib progenies SAWHSP is one of the most used for maize because of its proven efficiency along with its easy handling and simplicity in estimating genetic parameters Paterniani and Miranda Filho, There are several examples in the literature which show gains from truncated selection for yield in various maize populations.

Introducvion realized gain estimated by Arriel et al. Gemetica breeding strategies, there are also biometric methodologies with a strong theoretical base which could be used by plant breeders to efficiently identify superior genotypes and obtain greater gains. Among these are BLUP best linear unbiased predictorwhich classifies individuals submitted to selection using a function of the observed data and a matrix of genetic and residual variances and covariances of previously known traits Henderson, The best progenies are selected based on the estimated genetic vector.

According to Introduccoonthe BLUP of g is defined as the regression coefficient of the genetic values g in function of the observations y corrected for the fixed effects Xb. To estimate the genetic value without inverting the variance and covariance matrices of the observed data, a time-consuming computer operation, since this matrix is frequently non-diagonal and very large, Henderson proposed an alternative computer method using mixed model equations cuuantitativa obtaining the BLUP.

More details of this method are given by Martins et al. No supplementary irrigation was used. The seeds of the HSP assessed in these experiments were obtained from the harvest of the 10 best plants selected within each of the 20 female rows, in an isolated recombination plot from remnant seeds of the HSP selected by Arriel et al.

The modified Irish method was used Figure 1 for recombination with simultaneous creation of new HSP, keeping track of the female rows old progenies to establish a pedigree relationship among the new and old progenies.

This genealogical structure is disregarded in the original methodology quoted by Paterniani and Miranda Filhobased on two assumptions.

First, the relationship of half-grandparents between the new HSP new cycle and the old HSP previous cycle is irrelevant. Second, it assumes that the gametes of all HSP in the recombination plot have the same frequency, resulting in equal chances of fertilization and in a balanced contribution of each progeny to the next cycle.

The BLUP determination for selection among half-sib progenies adapted to mixed model methodologies, revised by Martins et geneticq. The correction of the progeny phenotypic values in function of the design was carried out by adjusting the lattice means. However, this second option steel keeps the genetic effects biased by the fixed effects, as characterized by the BLP Best Linear Predictor methodology.

Thus, the diagonal of the kinship sub-matrices is 0.

Introduccion a la Genetica Cuantitativa Spanish Edition, D. S. Falconer. (Paperback )

These kinship coefficients were estimated for half-sib progenies, with remnant seeds, based on the following expressions:. The estimate of the components of variance and introdkccion the genetic parameters was carried out according to Vencovsky The coefficients of vuantitativa percentage of common HSP selected by each methodology and the expected gains from selection were estimated.

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Table I shows the individual and joint introdiccion analyses for the four environments, where the importance of the genotype x environment interaction is apparent. This also occurred in the previous selection cycles Aguiar, ; Pacheco, ; Arriel, The environment variation coefficients are within the variation limits found by Ramalho for this type of progeny. The influence of the genotype x environment interaction when selection was made considering the environments individually was evident, and some superior progenies in a given environment were not selected in others.

The pedigree analyses of the selected HSP in the fourth among progeny selection cycle Tables II and III show that truncated selection caused an imbalance in the expected frequency of new and old progenies. For example, in the experiment Table II the old progeny number 34 contributed four new progenies, when it was expected to contribute only one.

This may result in a reduction in the effective size of the population in the more advanced selection generations and consequently in an increase in inbreeding.

Among 20 progenies selected for their phenotypic value in experiments, andthe following old progenies contribution were quantified: Table II – Twenty half-sib progenies HSP selected on the basis of phenotypic value, in decreasing order averages adjusted for corn ear weight.

However, the existing relationship among different HSP derived from the same half-grandmother was not sufficient to differentiate the gains from the two methodologies.

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This may be seen in the genetuca of the number of old progenies in the pedigree of the new selected progenies, which in experiments, and was 13, 9, 9 and 8, respectively. In other words, the BLP conferred greater introvuccion to the mean of the related progenies with the same half-grandmother than to introducicon means of the isolated HSP, a process that results in the screening out of the high mean progenies descending from low mean families, which would be selected based on their phenotype.

On the other hand, it can be seen that the BLP was influenced by extreme phenotypic values, because it distributes this influence equally to all the progenies of the same family. This fact was observed with the HSP numbered in experimentthe adjusted mean of which, far superior to the others, contributed to the selection of other HSP of the family, even when they had lower phenotypic values than other discarded HSP.

This fact was minimized when the discrepant value was arbitrarily substituted by a measurement nearer to the mean of the family data not shown. An inverse relation of This shows that as the genetic variance increases in relation to the cuantitatiga variance, the BLP tends to select falconeer same individuals that would be selected based on greater phenotypic values.

A negative correlation of With large h 2 the phenotypic value is a good predictor of the genetic value. The joint analysis of the four environments was used to select the 20 HSP which were recombined in the summer of 93 to produce, simultaneously, new progenies to start the fifth cycle of SAWHSP in the CMS population.

As in the first three cycles, selection was based on the falcner over the environments to improve the genetic adaptability of the population, including those with lower densities, by giving priority to prolificacy. Table IV shows the selected progenies. Table IV also shows that the BLP selected a greater number of progenies stemming from the same ancestor, totaling 11 different maternal half-grandmothers. Selection based on the phenotypic value, however, resulted in 15 different ancestors as noted by the pedigree.

When the relationship among progenies descending from the same half-grandmother was discounted for the HSP selection by the BLP methodology, i. Similarly, when the kinship was considered in the joint analysis, the genetic gains were close 9.

Thus, the gain of selection GS obtained with the BLP is the selection differential SD among the means of the genetic values of the selected progenies in relation introduccioj all the assessed ones. The literature has often presented different results among the expected gains and the observed duantitativa from selection. This lack of agreement is partially due to the fact that cuantitayiva estimates of the expected gains are based on phenotypic values, which suffer from environmental influences and from genotype x environment interaction.

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In the majority of cases the expected gains are overestimated, which was also shown for this population by Arriel et al. This difference was attributed to the genotype x environmental interaction, whose component was large in the three cycles, being larger than the estimate of genetic variance among the HSP cuantigativa this cycle Table I. On the other hand, when dealing with genetic values, this should not happen and the expected gains should be nearer the observed gains.

However, the estimates of gain obtained by introducicon BLP were fairly similar to those obtained based on the phenotypic values, showing that the genetic values seem introxuccion be under the same influences mentioned by Arriel et al.

In these results, it was shown that the original SAWHSP, quoted by Paterniani and Miranda Filhois an efficient method, even when the relationship of the selected progenies is not considered. In this method, kntroduccion genetic gains were identical to those genetida based on the BLP, showing its efficiency in isolating the additive component, responsible for the heritability and the expected gain, from the phenotypic variance. It also contributed less to the reduction in the effective size of the population, and consequently reduced future problems with inbreeding.

Although longer oa more detailed studies are necessary on the importance of the pedigree in the SAWHSP, the results from the present study showed that the use of this type of relationship in the routine assessment of maize half-sibs may be disregarded.

However, the indication of a reduction in the effective size of the population caused by an imbalance in the contribution of the selected progenies, as shown by the pedigree structure, draws attention to the problem of an increase in the level of inbreeding. One should continue with the same structure of the modified Irish method, shown in Figure 1but with a balanced genrtica of seeds from ears selected within the female row used for recombination, to sow the female rows.

For the male rows a balanced mixture of seeds from the ears of all new HSP should be used. There was a tendency for the BLP methodology to select genetcia greater number of related progenies pedigree than the SAWHSP method, which implies greater care with the effective size of the population.

The pedigree system, although unnecessary for HSP selection, allowed the prediction of an increase in the rate of inbreeding in the long term with the originally proposed scheme, when recombination is simultaneously carried out with the creation of new progenies.

Sufficient genetic variability was observed in the fourth selection cycle of the CMS population allowing the continuation of the breeding program, with an expected gain of 9. The SAWHSP method was shown to be efficient in the isolation of the additive genetic variance component from cuanttitativa phenotypic variance. The authors would like to thank the valuable collaboration of Drs.

P and Ramalho, M. Expected and realized gains in the CMS maize population after three cycles of half-sib family selection. John Wiley and Sons, New York, pp.

Introduccion a la Genetica Cuantitativa. Selection index and expected genetic advance. Statistical Genetics and Plant Breeding.

Sire evaluation and genetic trends. Best linear unbiased estimation and prediction under a selection model.

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Figure 1 – Field scheme for half-sib progeny HSP recombination with simultaneous obtention of new progenies. All the contents of this journal, except where otherwise falocner, is licensed under a Creative Commons Attribution License. Services on Demand Journal. These kinship coefficients were estimated for half-sib progenies, with remnant seeds, based on the following expressions: How to cite this article.