3 edition of Variance components and animal breeding found in the catalog.
Variance components and animal breeding
|Statement||edited by L. Dale Van Vleck, Shayle R. Searle.|
|Contributions||Henderson, C. R., Van Vleck, L. Dale 1933-, Searle, S. R. 1928-|
|LC Classifications||SF105 .V26|
|The Physical Object|
|Pagination||x, 227 p. :|
|Number of Pages||227|
|LC Control Number||79054917|
A review. Beside the use of variance components for estimation of breeding values, the components have a high importance on further breeding aspects, such as indication of selection limits, optimisation of test period, change of performance during growth, and determination of the best selection traits. Maternal and non-additive genetic variance components can be estimated and their high Cited by: 1. Vaez TR, Nicholas FW, Raadsma HW () REML estimates of variance and covariance components for production traits in Australian Merino sheep, using an animal model 1. Cited by:
univariate and multivariate animal breeding, genetics data and the analysis of regular or irregular spatial data.” ASReml uses the Average Information (AI) algorithm and sparse matrix. Variance components for twinning rate and fertility were estimated using the ASREML and MCMCglmm/R packages, with a threshold animal model and a threshold sire model with binary distribution and a LOGIT link function. Estimates of additive genetic variance for fertility were much higher for the Bayesian methods than for by: 5.
REML Variance-Component Estimation In the numerous forms of analysis of variance (ANOVA) discussed in previ-ous chapters, variance components were estimated by equating observed mean squares to expressions describing their expected values, these being functions of the variance components. ANOVA has the nice feature that the estimators for theFile Size: KB. Genomic-polygenic and polygenic variance components and variance ratios. Genomic-polygenic and polygenic variance components for MY and FY were estimated using the Markov Chain Monte Carlo (MCMC) procedure of GS3 with option VCE (Legarra et al., ).The genomic-polygenic model included the fixed effects of herd-year-season, the Holstein fraction of the cow, heterozygosity of the cow and Author: Bodin Wongpom, Skorn Koonawootrittriron, Mauricio A. Elzo, Thanathip Suwanasopee.
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Variance components and predict breeding values. In addition to a sire model, it is possible to use an animal model with or without maternal effects, a sire-dam model, or a sire-maternal grandsire model. Compari-sons among results from different statistical models 2Current address: Departamento de Zootecnia, UFSM, Santa Maria-RS-Brazil.
Biotechnology in Animal Husbandry 30 (1), pISSN Publisher: Institute for Animal Husbandry, Belgrade-Zemun ' DOI: /BAHM UDC ESTIMATION OF (CO)VARIANCE COMPONENTS AND BREEDING VALUES FOR TEST-DAY MILKFile Size: KB. Variance component estimation in animal breeding: a review.
Hofer. Institute of Animal Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland. Search for more papers by this author.
Variance components and animal breeding book Hofer. Institute of Animal Science, Swiss Federal Institute of Technology (ETH), Zurich, by: n = 1. n = 5 n = 2 n = (Each locus is dominant and all have the same effect and same gene frequency, but with the effect sizes scaled in each case to have the same total genetic variance and to have 30% of the phenotypic variance be genetic).
Quantitative genetics: Variance components and heritability – File Size: KB. So the phenotypic variance is called σ 2 P, the genetic variance is called σ 2 G, and the environmental variance is called σ 2 E.
Our model of P = G + E is also applicable to variance components: σ 2 P = σ 2 G + σ 2 E + 2cov G,E = σ 2 G + σ 2 E.
The covariance between G and E is assumed to be 0. DFREML is a suite of programs by Karin Meyer (University of New England, Armidale, NSW, Australia) to estimate (co)variance components and genetic parameters for normally distributed traits by Restricted Maximum Likelihood, using a derivative-free algorithm.
Analysis is carried out fitting a mixed linear model with a random effect for the additive genetic merit of each animal (the so-called "animal model"). Estimation of variance components Random regression Bayesian estimation: introduction; McMC; mixed model Analysis and experimental design in animal breeding Learning outcomes: To know how to use the different methods for the estimation of genetic or environmental components of variance in animal Size: KB.
The book covers the area of animal bteeding under six broad sections with 23 chaptets starting from history to biotechnology in animal breeding including estimation and computation of numerical. The aims of this study were to estimate variance components; calculate heritability (h²) and genetic correlations (r g) predict breeding values (VG) and evaluate their trends over time Author: Daniel Gianola.
Overview of Animal Breeding Fall 1 Required Information Successful animal breeding requires 1. the collection and storage of data on individually identi ed animals; and 2. complete pedigree information about the sire and dam of each animal.
Without these two pieces of information little genetic change can be made in a Size: 1MB. this is an excellent book for the theoretically minded variance-components investigator. -- Journal of the American Statistical is an excellent book for the theoretically minded variance-components investigator.
-- Journal of the American Statistical Association The excellent book is clearly written and easy to by: Genetic aspects and consequences of various mating systems.
Effects of mating systems on mean and variance. Application of various mating system in animal improvement. Selection for general and specific combining ability. Genetic polymorphysim and its application in genetic Size: 90KB.
The low h 2 is the result of a small amount of additive genetic variance compared with dominance and interaction variance. Such a situation calls for special breeding schemes that make use of nonadditive variance.
One such scheme is the hybridinbred method, which is used almost universally for corn. A large number of inbred lines are created by : Anthony Jf Griffiths, Jeffrey H Miller, David T Suzuki, Richard C Lewontin, William M Gelbart. mixed models and variance components for statistically analyzing data.
This includes data from such widely disparate disciplines as animal breeding, biology in general, clinical trials, finance, genetics, manufacturing processes, psychology, sociology and so on. For students the book. mal model in animal breeding scenario.
This model provides inferences on parameters such as genetic (additive/breeding, dominance, ) values and possibly also co-variance components (additive genetic variance, heritability, ). Very nice introduction to this topic is in Mrode (), which also gives a.
Advanced Methods of Statistical Analysis used in Animal Breeding. Advanced Methods of Statistical Analysis used in Animal Breeding. variance components by REML are estimated based on residuals calculated after fitting by ordinary least squares from fixed effects part of the model. It Maximizes a marginal likelihood function.
So it is. Variance components and animal breeding: proceedings of a conference in honor of C.R. Henderson, July variance components: enumeration sampling, cereal experiments, swine breeding (three papers), corn breeding and soil sampling.
Papers by on sheep breeding could be added to the list. Clearly, by the mid - 40’s, animal and plant breeders were making considerable use of variance components.
The book also contains references to . called Best Linear Unbiased Prediction (BLUP) is the standard method in animal breeding evaluation. BLUP needs the variance components (genetic and environmental) for predicting the genetic values. To estimate them is a difficult task, because data come from different farms and different environments and they should be corrected as before.
Estimates of genetic parameters resulting from various analytical models for birth weight (BWT, n = 4,), d weight (WWT, n = 3,), and d weight (YWT, n = 3,) were compared. Data consisted of records for Line 1 Hereford cattle selected for postweaning growth from to at ARSUSDA, Miles City, MT.
Twelve models were compared. Model 1 included fixed effects of year, Cited by:. Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives’ performance.
This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed Cited by: Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) estimators of variance components are widely used in animal breeding. Hoeschele () has stated that the likelihood is always unimodal for REML, but this statement is not true in all by: 3.
In this model, the estimation of genetic parameters is often equivalent to the estimation of variance and covariance components. The book of Lynch & Walsh () gives a very readable and comprehensive account of most of the statistical and genetic principles in this review.
As an example, a recent estimation of genetic parameters using random regression models for a British population of dairy cattle Cited by: