Applications of Linear Models in Animal Breeding PDF Download
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Author: Charles R. Henderson
Publisher: Guelph, Ont. : University of Guelph
ISBN: 9780889550308
Category : Amélioration génétique - Méthodes statistiques
Languages : en
Pages : 462
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Book Description
Author: Charles R. Henderson
Publisher: Guelph, Ont. : University of Guelph
ISBN: 9780889550308
Category : Amélioration génétique - Méthodes statistiques
Languages : en
Pages : 462
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Book Description
Author: National Dairy Research Institute, Karnal
Publisher:
ISBN:
Category :
Languages : en
Pages : 251
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Book Description
Author: R. A. Mrode
Publisher: Cab International
ISBN: 9781845939816
Category : Technology & Engineering
Languages : en
Pages : 343
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Book Description
The prediction of producing desirable traits in offspring such as increased growth rate or superior meat, milk and wool production is a vital economic tool to the animal scientist. Summarizing the latest developments in genomics relating to animal breeding values and design of breeding programs, this new edition includes models of survival analysis, social interaction and sire and dam models, as well as advancements in the use of SNPs in the computation of genomic breeding values.
Author: Raphael Mrode
Publisher: CABI
ISBN: 1800620489
Category : Technology & Engineering
Languages : en
Pages : 409
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Book Description
Fundamental to any livestock improvement programme by animal scientists, is the prediction of genetic merit in the offspring generation for desirable production traits such as increased growth rate, or superior meat, milk and wool production. Covering the foundational principles on the application of linear models for the prediction of genetic merit in livestock, this new edition is fully updated to incorporate recent advances in genomic prediction approaches, genomic models for multi-breed and crossbred performance, dominance and epistasis. It provides models for the analysis of main production traits as well as functional traits and includes numerous worked examples. For the first time, R codes for key examples in the textbook are provided online. Suitable for graduate and postgraduate students, researchers and lecturers of animal breeding, genetics and genomics, this established textbook provides a thorough grounding in both the basics and in new developments of linear models and animal genetics.
Author: R. A. Mrode
Publisher: C A B International
ISBN: 9780851989969
Category : Nature
Languages : en
Pages : 187
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Book Description
Best Linear Unbiased Prediction (BLUP) has become the most widely accepted method for the genetic evaluation of domestic livestock. Since its introduction, the method has evolved in terms of its application to sire models, and to sire and maternal grandsire models in the early years, to animal models and multivariate analysis more recently. Despite these developments, there has been no straightforward text available on the application of linear models to the prediction of animal breeding values. This book fills this gap, providing a blend of theory and practical applications. It covers basic principles of breeding value predictions and the application of BLUP to genetic evaluations under various models. Some knowledge of basic matrix algebra and of quantitative genetics is assumed on the part of the reader, although some introductory matrix algebra is included in an appendix.
Author: Daniel Gianola
Publisher: Springer Science & Business Media
ISBN: 3642744877
Category : Technology & Engineering
Languages : en
Pages : 554
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Book Description
Developments in statistics and computing as well as their application to genetic improvement of livestock gained momentum over the last 20 years. This text reviews and consolidates the statistical foundations of animal breeding. This text will prove useful as a reference source to animal breeders, quantitative geneticists and statisticians working in these areas. It will also serve as a text in graduate courses in animal breeding methodology with prerequisite courses in linear models, statistical inference and quantitative genetics.
Author: Shayle R. Searle
Publisher:
ISBN:
Category :
Languages : en
Pages : 249
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Book Description
Author: Fikret Isik
Publisher: Springer
ISBN: 3319551779
Category : Science
Languages : en
Pages : 409
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Book Description
This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
Author: Ronald R. Hocking
Publisher: John Wiley & Sons
ISBN: 0471458627
Category : Mathematics
Languages : en
Pages : 773
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Book Description
A popular statistical text now updated and better than ever! The ready availability of high-speed computers and statistical software encourages the analysis of ever larger and more complex problems while at the same time increasing the likelihood of improper usage. That is why it is increasingly important to educate end users in the correct interpretation of the methodologies involved. Now in its second edition, Methods and Applications of Linear Models: Regression and the Analysis of Variance seeks to more effectively address the analysis of such models through several important changes. Notable in this new edition: Fully updated and expanded text reflects the most recent developments in the AVE method Rearranged and reorganized discussions of application and theory enhance text’s effectiveness as a teaching tool More than 100 new exercises in the areas of regression and analysis of variance As in the First Edition, the author presents a thorough treatment of the concepts and methods of linear model analysis, and illustrates them with various numerical and conceptual examples, using a data-based approach to development and analysis. Data sets, available on an FTP site, allow readers to apply analytical methods discussed in the book.
Author: Jamis J. Perrett
Publisher: Springer Science & Business Media
ISBN: 1441955569
Category : Mathematics
Languages : en
Pages : 235
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Book Description
Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formulas behind attractive user interfaces. This book bridges that gap, demonstrating theory put into practice. Concepts presented in a theoretical linear models course are often trivialized in applied linear models courses by the facility of high-level SAS procedures like PROC MIXED and PROC REG that require the user to provide a few options and statements and in return produce vast amounts of output. This book uses PROC IML to show how analytic linear models formulas can be typed directly into PROC IML, as they were presented in the linear models course, and solved using data. This helps students see the link between theory and application. This also assists researchers in developing new methodologies in the area of linear models. The book contains complete examples of SAS code for many of the computations relevant to a linear models course. However, the SAS code in these examples automates the analytic formulas. The code for high-level procedures like PROC MIXED is also included for side-by-side comparison. The book computes basic descriptive statistics, matrix algebra, matrix decomposition, likelihood maximization, non-linear optimization, etc. in a format conducive to a linear models or a special topics course. Also included in the book is an example of a basic analysis of a linear mixed model using restricted maximum likelihood estimation (REML). The example demonstrates tests for fixed effects, estimates of linear functions, and contrasts. The example starts by showing the steps for analyzing the data using PROC IML and then provides the analysis using PROC MIXED. This allows students to follow the process that lead to the output.
