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.
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