scholarly journals Genetic parameters for worm resistance in Santa Inês sheep using the Bayesian animal model

2021 ◽  
Vol 34 (2) ◽  
pp. 185-191
Author(s):  
Francelino Neiva Rodrigues ◽  
José Lindenberg Rocha Sarmento ◽  
Tânia Maria Leal ◽  
Adriana Mello de Araújo ◽  
Luiz Antonio Silva Figueiredo Filho
Keyword(s):  
Cluster Analysis ◽  
Animal Model ◽  
Bayesian Inference ◽  
Genetic Correlation ◽  
Genetic Parameters ◽  
Multiple Trait ◽  
Multivariate Cluster Analysis ◽  
Trait Model ◽  
Santa Inês ◽  
Multivariate Cluster

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses.Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model.Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model).Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

Estimation of genetic parameters for litter size in sheep: a comparison of a repeatability and a multivariate model

1998 ◽  
Vol 66 (3) ◽  
pp. 685-688 ◽  
Author(s):  
M. J. de Vries ◽  
E. H. van der Waaij ◽  
J. A. M. van Arendonk
Keyword(s):  
Animal Model ◽  
Genetic Correlation ◽  
Litter Size ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Multivariate Model ◽  
Phenotypic Variance ◽  
Multiple Trait ◽  
Environmental Correlations ◽  
Estimation Of Genetic Parameters

AbstractGenetic parameters were estimated for litter size in two prolific sheep breeds, i.e. the Zwartbles and the synthetic breed Swifter. Genetic parameters and breeding values for litter size in different parities were estimated using both a repeatability and a multivariate animal model. The estimated heritability from the repeatability model was 0·10 for the Zwartbles and 0·12 for the Swifter. For the multivariate model, heritability of litter size in first, second and third parity was 0·05, 0·07 and 0·10 for the Zwartbles and 0·09, 0·12 and 0·09 for the Swifter. Genetic correlation for litter size in Swifter was 0·81 between parity 1 and 2 and 0·99 between parity 2 and 3. For the Zwartbles genetic correlations were all very close to unity. Environmental correlations between litter size in subsequent parities were not constant over parities. Phenotypic variance in litter size in both breeds was 0·309 in first parity and was almost 50% higher in later parities. Based on the results it is recommended to apply a multiple trait model.

scholarly journals Assessing the Pollution Level in the Kuban River Basin by Multivariate Cluster Analysis

2020 ◽  
Vol 17 (4) ◽  
pp. 73-80 ◽  
Author(s):  
Vera Snezhko ◽  
Dmitrii Benin ◽  
Artem Lukyanets ◽  
Larisa Kondratenko
Keyword(s):  
Cluster Analysis ◽  
River Basin ◽  
Ecological Status ◽  
Chemical System ◽  
Pollution Level ◽  
Multivariate Cluster Analysis ◽  
Water Chemical Composition ◽  
Aluminium Copper ◽  
Kuban River ◽  
Multivariate Cluster

Considering features of hydrological conditions for hydro-chemical system, this paper analyses the performance of the hydro-ecological status of the Kuban river basin.. The results of the study on water chemical composition depending on the distance from the source are presented. By comparing the results with the reference values of water quality, increased aluminium, zinc, and copper content was established. Respective dendrograms of hydro-ecological studies obtained according to performed analysis for the Kuban River and its tributaries are presented. The relevance of the findings received is p<0.0005 and the correlation coefficient corresponds to 0.935...1. The results of multivariate cluster analysis showed that the Kuban basin has an increased content of particular heavy metals such as aluminium, copper, and zinc.

Genetic parameter estimations and genomic insights for teat and udder structure in young and mature Canadian Angus cows

2021 ◽  
Author(s):  
K Devani ◽  
J J Crowley ◽  
G Plastow ◽  
K Orsel ◽  
T S Valente
Keyword(s):  
Animal Model ◽  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Age Groups ◽  
Morbidity And Mortality ◽  
Parameter Estimations ◽  
Angus Cattle ◽  
Genetic Evaluations

Abstract Poor teat and udder structure, frequently associated with older cows, impact cow production and health, as well as calf morbidity and mortality. However, producer culling, for reasons including age, production, feed availability, and beef markets, creates a bias in teat and udder scores assessed and submitted to the Canadian Angus Association for genetic evaluations towards improved mammary structure. In addition, due to the infancy of the reporting program, repeated scores are rare. Prior to adoption of genetic evaluations for teat and udder scores in Canadian Angus cattle, it is imperative to verify that teat and udder scores from young cows are the same trait as teat and udder scores estimated on mature cows. Genetic parameters for teat and udder scores from all cows (n=4,192), and then from young cows (parity 1 and 2) and from mature cows (parity ≥ 4) were estimated using a single trait animal model. Genetic correlations for the traits between the two cow age groups were estimated using a two-trait animal model. Estimates of heritability (PSD) were 0.32 (0.07) and 0.45 (0.07) for young teat and udder score, and 0.27 (0.07) and 0.31 (0.07) for mature teat and udder score, respectively. Genetic correlation (PSD) between the young and mature traits was 0.87 (0.13) for teat score and 0.40 (0.17) for udder score. GWAS were used to further explore the genetic and biological commonalities and differences between the two groups. Although there were no genes in common for the two udder scores, 12 genes overlapped for teat score in the two cow age groups. Interestingly, there were also 23 genes in common between teat and udder scores in mature cows. Based on these findings, it is recommended that producers collect teat and udder score on their cow herd annually.

The Application of Mathematical Statistics in Urban Geography Research

2014 ◽  
Vol 919-921 ◽  
pp. 1630-1633
Author(s):  
Xiu Feng Ma
Keyword(s):  
Cluster Analysis ◽  
Regression Analysis ◽  
20Th Century ◽  
Statistical Methods ◽  
Urban Geography ◽  
Mathematical Statistics ◽  
Multivariate Cluster Analysis ◽  
Important Position ◽  
Multivariate Cluster

In the middle of the 20th century, geography has experienced a number "revolution". statistical occupies more and more important position in the urban geography research. This paper analyzes the application of statistics in the development of the urban geography. And with the method of case study, illustrates the multivariate cluster analysis, regression analysis and other statistical methods in the study of urban geography specific applications

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