Linear Models For The Prediction Of Animal Breeding Values - Softcover

Mrode, R. A., Ph.D.; Thompson, R.

 
9780851990002: Linear Models For The Prediction Of Animal Breeding Values

Inhaltsangabe

Best Linear Unbiased Prediction (BLUP) has become the most widely accepted method for genetic evaluation of domestic livestock. Since its introduction, the method has evolved and despite this there is no simple text on the application of linear models to the predictions of breeding values. This book has been written with a good balance of theory and application to fill this gap. Equations for partitioning breeding values into contributions from various sources of information are derived under the various models. Recent developments in the analysis of longitudinal data with random regression models and the inclusion of genetic marker information in the evaluation of animals have been incorporated. Threshold models have been introduced in addition to the basic concepts and methods for variance compenent estimation including the use of the Gibbs sampler. Overall, the book has been thoroughly updated since the first edition was published in 1996. It will appeal to advanced students in animal breeding and genetics.

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Über die Autorin bzw. den Autor

Raphael Mrode is Professor of Quantitative Genetics and Genomics at Scotland's Rural College and Principal Scientist in Quantitative Genetics in Dairy Cattle at the International Livestock Research Institute, Nairobi, Kenya. He has been lecturing on Edinburgh University's Masters course on quantitative genetics and genome analysis since 2005, and has given lectures on mixed linear models and the use of various BLUP models for genetic prediction. His research interests include data modelling and analysis, the incorporation of molecular information in genetic evaluation procedures, the application of innovative approaches for data capture, analysis and feedback and investigating methods for generating alternative and novel phenotypes in small dairy systems in developing countries.

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