Relationship between blup and selection index

relationship between blup and selection index

4. Estimation of breeding value. BLUP. Introduction. The selection index principle . The inverse of the relationship matrix can be set up directly, using the . Prediction of genetic gains by selection indexes and REML/BLUP methodology .. The difference between the REML/BLUP estimates of gain compared with the . Relation between bull selection index and BLUP-AM index estimated on progeny weights of Chianina sires in Umbria. F.M. Sarti, E. Lasagna, V. Palucci.

The following components of variance individual REML were estimated: Phenotypic variance; h2 mp: Heritability of average progeny, assuming complete survival; Acprog: The objective of passion fruit breeding programmes is development of plants with a greater number of fruits, which should also be larger and have greater masses, to meet the demands of the market.

However, the work of Pesek and Baker stands out based on the desired gain in the trait EC because fruits with a thinner peel tend to have a larger amount of pulp. The Smith and Hazel indexes delivered genetic gains for the traits NF and yield, with values of A positive gain was noted for MF, but the value was low 1. However, undesirable gains were observed for the traits DLF Thus, this method is not indicated for selection of progenies in that population because undesirable gains were observed for most traits of economic interest.

The authors found that the Smith and Hazel indexes showed the lowest predicted gain in several alternatives, which resulted in unsatisfactory gains compared with the other indexes assessed. The genetic gains predicted by the Mulamba and Mock index were satisfactory for all traits Table 1 assessed. Genetic gains of In the cycles of recurrent selection that preceded this study, the results obtained by the authors mentioned above led to the conclusion that an increase in gains occurred with the advance of recurrent selection for the main traits of sour passion fruit, which indicates that the chosen selection method is satisfactory.

The Williams index allowed satisfactory genetic gains for the traits NF, yield and MF, with values of These gains were greater than those obtained by the Mulamba and Mock index. Positive but low gains were observed for DLF 0. Simultaneous selection was not satisfactory for PP and EC because they presented gains of These results demonstrate that the Williams index is suitable for selection of progenies in the current population, but undesirable gains were observed for the traits of interest PP and EC.

In mixed models, estimators and predictors are obtained using iterative processes.

Animal breeding | Digital Textbook Library

To begin the iterative process, the initial value adopted for h2 was equal to 0. The main evaluated traits of NF, yield and MF presented estimated values for the genotypic variance of These values indicate that the population studied presents high genetic variability for these characters, which demonstrates that it is possible to select progenies with increased yield and increased fruit mass.

Knowledge of the genotypic variance is highly important for breeding programmes because it indicates the range of genetic variation of a character that breeders intend to improve. Estimates of phenotypic variance were The higher values of phenotypic variance observed in this study indicate a stronger influence of the environment compared with the previous cycle.

This result is probable because Itaocara was more heavily affected by the environment than the municipality of Campos dos Goytacazes, where the previous cycles were conducted.

The h2 mp estimates were 0. However, it cannot be concluded that genetic gains obtained by selection will be lower because estimates of heritability values of high magnitude might occur for characters of small genetic variance, provided that the environmental effect on the trait is of small magnitude.

Thus, heritability is not an immutable characteristic and is a property not only of the trait but also of the population and environmental conditions to which the population submits.

The accuracy values of 0. The accuracy value indicates the precision in the access to the actual genetic variation based on the phenotypic variation observed in each trait. Low accuracy indicates that the data for these traits are less reliable, possibly because the trait is highly affected by the environment.

Comparison of index selection and best linear unbiased prediction for simulated layer poultry data.

The mean values of the traits NF, yield and MF were In a cycle preceding that of this study, the mean values reported by Silva et al. This result highlights the potential of this population to achieve genetic gains from the UENF sour passion fruit intrapopulation breeding programme. The highest predicted gains were observed for the traits NF Selection indexes commonly use economic weights, which become arbitrary genetic gains.

In passion fruit, this result is even more evident due to the negative correlation between the characteristics of economic importance, namely, shell thickness and pulp percentage. Because these traits are negatively correlated, direct selection via genotypic values of the characteristics becomes compromised. This high genetic correlation was expected because fruits with more pulp tend to have thinner skin, which is an objective of the UENF passion fruit breeding programme.

The coefficients are generally low except for the coefficients found between the indexes of Smith and Hazel and Williamswith a value of 0.

relationship between blup and selection index

According to Pedrozo et al. With the aim of selecting progenies of sour passion fruit, Silva and Viana evaluated the indexes based on the sum of ranks of Mulamba and Mock and the distance between the genotype and the ideotype Cruz, and observed a coefficient of coincidence of 0. This result allowed simultaneous selection of superior progenies for the traits of number of fruits, total productivity, average fruit weight, fruit diameter, fruit length, fruit width and weight of fruit pulp. Genetic gains were predicted, and the new estimated averages were higher than the overall average for all variables Table 4.

relationship between blup and selection index

Progeny 26 was best ranked in the identification of promising progenies for number of fruits and yield, i. For NF, the gain predicted by the selection of this progeny was A total of 22 progenies selected for NF were also selected for yield. According to Silva et al. Considering the importance of selecting progenies with greater fruit mass MFthe highest-ranking position was obtained by progeny 69, the expected gains of which exceeded Progeny 48 was ranked at the lowest position, with an estimated gain of 4.

The genotypic values predicted by BLUP refer to those observed without the environmental effect.

relationship between blup and selection index

Therefore, contrary to observations in species of vegetative propagation, in which all genotypic values are utilized, in allogamous or outcrossing species, which are subjected to progeny tests, only the additive effects are transmitted to the descendants and should be considered during the selection of genotypes that will be used as parents in the following generations Resende, In practical animal breeding, selection is often not solely on own phenotype but on estimates of breeding values EBV that are derived from records on the animal itself as well as its relatives using Best Linear Unbiased Prediction BLUP for an animal model Lynch and Walsh, An important property of EBV derived from an animal model is that all records that are available on the individual and its relatives are optimally used, while simultaneously adjusting for systematic environmental effects e.

Stochastic simulation models of breeding programs can directly incorporate genetic evaluations based on animal models because the data that provide the input for such models are individually simulated. This is not possible for deterministic models. Thus, when developing deterministic models for genetic improvement, other methods to model selection and accuracy of EBV from BLUP animal models must be used.

In addition to allowing deterministic modelling of selection on EBV, these methods are also required to develop a basic understanding of factors that affect accuracy of selection, which are important for the design of breeding programs, including the contribution that different types of records make to accuracy of EBV.

EBV can estimated in different ways, based on the information on the phenotype and relatives: EBV from own records — simple regression EBV from records on a single type of relatives — simple regression EBV from multiple sources of information — multiple regression — selection index theory EBV from BLUP animal models As noted above, the common theme through these methods is the use of linear regression for the prediction of EBV from phenotypic records.

Before going into these developments, we will first describe some general properties of EBV. All methods for prediction of breeding values are based on the principles of linear regression: As a result, properties of linear regression can be used to derive general properties of EBV. One important property of EBV is unbiased.

How to find a Selection Differential, Genetic Gain and Heritability

This means that the expected magnitude of the true breeding value of an animal is equal to its estimated breeding value. Selection Index and Animal Model BLUP An assumption in the use of selection indexes to estimate breeding values is either that there are no fixed effects in the data used, or that fixed effects are known without error.

This may be true in some situations.

Comparison of index selection and best linear unbiased prediction for simulated layer poultry data.

An example are some forms of selection in egg-laying poultry where all birds are hatched in one or two very large groups and reared and recorded together in single locations. But in most cases, fixed effects are important and not known without error.

relationship between blup and selection index

For example, with pigs, different litters are born at different times of the year, often in several different locations. In progeny testing schemes in dairy cattle, cows are born continuously, begin milking at different times of year and in a very large number of different herds.