Parâmetros genéticos e eficiência relativa de seleção para a produção de leite no dia do controle para vacas da raça Holandesa

Andreazza, Juliano; Rorato, Paulo Roberto Nogara; Faro, Lenira El; Boligon, Arione Augusti; Weber, Tomás; Kippert, Carlos Junior; Lopes, Jader Silva

doi:10.1590/s0103-84782008000200025

Cited by 2 publications

(2 citation statements)

References 11 publications

(9 reference statements)

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“…The strategies were repeated 5 times for the comparison of changes in average EBVs and inbreeding coefficients over 25 years. The annual phenotypic means necessary to obtain the costs and revenues were calculated by multiplying the average EBVs by the phenotypic standard deviation for milk production at 305 d of lactation for Holstein animals in Brazil (Andreazza et al 2008) and later the traits phenotypic means were added (Table 1).…”

Section: Methodsmentioning

confidence: 99%

Genomic selection in dairy cattle simulated populations

Seno

Guidolin

Aspilcueta-Borquis

et al. 2018

Journal of Dairy Research

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Genomic selection is arguably the most promising tool for improving genetic gain in domestic animals to emerge in the last few decades, but is an expensive process. The aim of this study was to evaluate the economic impact related to the implementation of genomic selection in a simulated dairy cattle population. The software QMSim was used to simulate genomic and phenotypic data. The simulated genome contained 30 chromosomes with 100 cm each, 1666 SNPs markers equally spread and 266 QTLs randomly designated for each chromosome. The numbers of markers and QTLs were designated according to information available from Animal QTL (http://www.animalgenome.org/QTLdb) and Bovine QTL (http://bovineqtl.tamu.edu/). The allelic frequency changes were assigned in a gamma distribution with alpha parameters equal to 0·4. Recurrent mutation rates of 1·0e-4 were assumed to apply to markers and QTLs. A historic population of 1000 individuals was generated and the total number of animals was reduced gradually along 850 generations until we obtained a number of 200 animals in the last generation, characterizing a bottleneck effect. Progenies were created along generations from random mating of the male and female gametes, assuming the same proportion of both genders. Than the population was extended for another 150 generations until we obtained 17 000 animals, with only 320 male individuals in the last generation. After this period a 25 year of selection was simulated taking into account a trait limited by sex with heritability of 0·30 (i.e. milk yield), one progeny/cow/year and variance equal to 1·0. Annually, 320 bulls were mated with 16 000 dams, assuming a replacement rate of 60 and 40% for males and females, respectively. Selection and discard criteria were based in four strategies to obtain the EBVs assuming as breeding objective to maximize milk yield. The progeny replaced the discarded animals creating an overlapping generation structure. The selection strategies were: RS is selection based on random values; PS is selection based on phenotypic values; Blup is selection based on EBVs estimated by BLUP; and GEBV is selection based on genomic estimated breeding values in one step, using high (GBlup) and low (GBlupi) density panels. Results indicated that the genetic evaluation using the aid of genomic information could provide better genetic gain rates in dairy cattle breeding programs as well as reduce the average inbreeding coefficient in the population. The economic viability indicators showed that only Blup and GBlup/GBlupi strategies, the ones that used milk control and genetic evaluation were economic viable, considering a discount rate of 6·32% per year.

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Section: Methodsmentioning

confidence: 99%

Genomic selection in dairy cattle simulated populations

Seno

Guidolin

Aspilcueta-Borquis

et al. 2018

Journal of Dairy Research

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“…In this sense, the estimation of genetic parameters and breeding values for milk production characteristics and components in dairy goats is necessary, as these parameters are the indicators of the genetic progress that can be achieved when a good selection and mating program in this species has been implemented [3]. Knowledge of these parameters will help in the selection of superior individuals for these characteristics [4], allowing to increase the average performance of animals and in this way, establishing a constant expansion of the goat milk industry and its derivatives [5].…”

Section: Introductionmentioning

confidence: 99%

Integrating Casein Complex SNPs Additive, Dominance and Epistatic Effects on Genetic Parameters and Breeding Values Estimation for Murciano-Granadina Goat Milk Yield and Components

Inostroza

Landi

González

et al. 2020

Genes

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Assessing dominance and additive effects of casein complex single-nucleotide polymorphisms (SNPs) (αS1, αS2, β, and κ casein), and their epistatic relationships may maximize our knowledge on the genetic regulation of profitable traits. Contextually, new genomic selection perspectives may translate this higher efficiency into higher accuracies for milk yield and components’ genetic parameters and breeding values. A total of 2594 lactation records were collected from 159 Murciano-Granadina goats (2005–2018), genotyped for 48 casein loci-located SNPs. Bonferroni-corrected nonparametric tests, categorical principal component analysis (CATPCA), and nonlinear canonical correlations were performed to quantify additive, dominance, and interSNP epistatic effects and evaluate the outcomes of their inclusion in quantitative and qualitative milk production traits’ genetic models (yield, protein, fat, solids, and lactose contents and somatic cells count). Milk yield, lactose, and somatic cell count heritabilities increased considerably when the model including genetic effects was considered (0.46, 0.30, 0.43, respectively). Components standard prediction errors decreased, and accuracies and reliabilities increased when genetic effects were considered. Conclusively, including genetic effects and relationships among these heritable biomarkers may improve model efficiency, genetic parameters, and breeding values for milk yield and composition, optimizing selection practices profitability for components whose technological application may be especially relevant for the cheese-making dairy sector.

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Parâmetros genéticos e eficiência relativa de seleção para a produção de leite no dia do controle para vacas da raça Holandesa

Abstract: RESUMO Para estudar a viabilidade da utilização das produções de leite no dia do controle (PLDC) em avaliações

Cited by 2 publications

References 11 publications

Genomic selection in dairy cattle simulated populations

Genomic selection in dairy cattle simulated populations

Integrating Casein Complex SNPs Additive, Dominance and Epistatic Effects on Genetic Parameters and Breeding Values Estimation for Murciano-Granadina Goat Milk Yield and Components

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