Selection index and introduction to mixed model methods
著者
書誌事項
Selection index and introduction to mixed model methods
CRC Press, c1993
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注記
Includes bibliographical references (p. 471-481)
内容説明・目次
内容説明
The principles covered in Selection Index and Introduction to Mixed Model Methods are developed using easy-to-follow rules for expected values and definitions of variances and covariances. The principles are reenforced with practice problems and exams. Indexes in retrospect as well as restricted indexes are developed. Selection for embedded traits such as maternal, grandmaternal, or paternal effects and for categorically measured traits are discussed. The introduction to mixed models applies expected values to create rules to set up least squares and mixed model equations. The similarity between selection index and mixed model methods is demonstrated with simple examples.
The third part of the book introduces basic methods used to estimate genetic parameters such as heritabilities, repeatabilities, and genetic correlations needed for both selection index and mixed model methods. This is an excellent text for advanced undergraduates, graduate students, and researchers who use selection index methods.
目次
Selection Index: Parameters, Statistics, and Expected Values. A Little About Matrix Algebra. Quantifying the Simple Mendelian Model. A Short Summation on Population Genetics. Genes Identical by Descent-The Basis of Genetic Likeness. Genetic Values and Genetic Covariances. The Selection Index. Determining the Coefficients for Selection Index Equations. Sire Evaluation, Example of Application of Selection Index. Probability Statements About True Value. Superiority of Selected Groups. Selection Index Flow Chart for Single Traits. Selection with More Than One Trait Measured. Using Records on All Traits of Relatives. Selection Index for Categorical Data. Selection for Embedded Traits: Maternal Effects. Selection when Traits Influenced by Grandmaternal and Maternal Effects. Fetal Effects Model (Sire of Fetus Effect). Cytoplasmic Effects Model. Selection for Traits with Nonlinear Economic Value. Restricted Selection. Index and Economic Values in Retrospect. Introduction to Mixed Model Prediction: Prediction from Linear Models. Least Squares Equations: One-Way Classification Model. The Animal Model. Sire Models. Computing the Inverse of the Additive Relationship Matrix. Models with Animals Related. Sire Models with Some Relationships. Variance of Prediction Errors. Numerical Example of Animal Model with Different Constraints. Creating and Solving Least Squares and Mixed Model Equations. Models for Crossbreeding. Flow Chart for Mixed Model Equations. Estimation of Genetic Parameters Using Simple Statistical Models: Summation and Dot Notation. Expected Values. Repeatability. Heritability. Genetic, Environmental, and Phenotypic Correlations. Monte Carlo Simulation. Generating Random Standard Normal Variables. Problem Sets and Exam Questions: Problem Sets. Exam Questions. Bibliography and Further Readings.
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