The primary objective of this course is to provide graduate students in Animal Science with a comprehensive understanding of the theoretical foundations and practical applications of variance component estimation and genetic parameter analysis in animal breeding. The course aims to equip students with the knowledge required to quantify the genetic and environmental sources of phenotypic variation and to estimate key genetic parameters, including heritability, repeatability, genetic correlations, and phenotypic correlations, using performance, pedigree, and, where appropriate, genomic data. The course further develops students' competence in the application of modern quantitative genetic methodologies, including linear mixed models, Best Linear Unbiased Prediction (BLUP), and Restricted Maximum Likelihood (REML), for the estimation of breeding values and genetic parameters. Emphasis is placed on the interpretation of variance components, the evaluation of genetic merit, and the design of scientifically sound selection and breeding programs for livestock populations. Upon successful completion of the course, students will be able to critically evaluate quantitative genetic studies, select appropriate statistical models for different breeding scenarios, interpret the biological significance of estimated genetic parameters, and apply these concepts to support evidence-based genetic improvement programs in animal production systems.

1- Definition and partitioning of variance components in quantitative genetics. 2- Data sources for genetic parameter estimation, including phenotypic, pedigree, and genomic information. 3- Experimental and breeding designs for estimating genetic parameters. 4- Genetic and phenotypic parameters in animal breeding: heritability, repeatability, genetic, phenotypic, and environmental correlations. 5- Principles, interpretation, and applications of genetic parameter estimation in animal breeding and selection programs. 6- Linear statistical models used in quantitative genetics, including fixed, random, and mixed models. 7- Properties of variance component estimators, including unbiasedness, consistency, efficiency, and precision. 8- Methods for variance component estimation, including ANOVA-based approaches, Maximum Likelihood (ML), Restricted Maximum Likelihood (REML), and Bayesian methods. 9- Best Linear Unbiased Prediction (BLUP) and breeding value estimation using variance components. 10- Computer software and practical applications for genetic evaluation and variance component estimation (e.g., BLUPF90, ASReml, WOMBAT, MTDFREML, DMU, and R).