Linear Models in Animal BreedingStudents completing this course will learn about linear models used in Animal Breeding. These models will be discussed in the context of the random variable that is to be predicted. Specifically, the course will cover animal models, sire/maternal grandsire models, and sire models. Models including a single record, repeated records, and models with both direct and maternal effects will be discussed.
Genetic PredictionThe goal of this course is to increase student understanding of best linear unbiased prediction and to develop skills in genetic prediction. A wide array of material will be covered with emphasis on real-world datasets designed to develop applied analytical skills relative in animal breeding. Topics will include data integrity diagnosis, contemporary grouping strategies, adjusting for known non-genetic effects, the AWK Programming Language, UNIX/Linux scripting, and use of the Animal Breeder's Toolkit to perform genetic evaluations. Students will develop procedures for the utilization of various sources of information for the calculations of predictions of genetic merit in the form of estimated breeding values.
Applied Variance Component EstimationThe goal of this course is to extend upon content covered in linear models and genetic prediction, with specific emphasis on estimation of (co)variance components and genetic parameters required to solve mixed models typical in livestock genetics. Upon successful completion of this course, students should have an applied knowledge of approaches used to estimate the G and R submatrices of the mixed model equations. Several tools will be used to demonstrate the models and approaches most commonly used in parameter estimation. Where appropriate, scientific literature that explains their implementation, and some attributes of the solutions obtained will be used. A general knowledge of linear models, matrix algebra, moment statistics, rules of expectation and familiarity with UNIX/Linux Operating Systems will be assumed, including scripting tools such as awk, octave, join, sort, paste, wc, etc. This course will begin in a somewhat historical manner, proceeding on to methods and software currently used for research and field data implementation.
Marker-Assisted and Gene-Assisted SelectionThe goal of this course is to extend concepts learned in previous courses to include DNA marker information with the objective of increasing the accuracy of selection decision tools. A broad spectrum of material will be presented relative to this ever-changing field of research. The course will cover basic concepts behind marker information, interpretation of molecular breeding values, and inclusion of marker information in genetic prediction. The majority of the course will focus on inclusion of single marker data into genetic prediction. This initial information can then be extrapolated to the use of whole genome information; the course will conclude with an introduction into this type of data analysis.
Introduction to Marker Association Analysis and QTL DetectionThe goal of this course is to introduce the basic concepts of using genetic markers to identify QTL and of estimating marker-trait associations, and to expose students to applications of these methodologies. Materials will cover the basics of linkage and linkage disequilibrium, alternate designs or population structures for QTL mapping, and statistical methods for QTL detection, including QTL interval mapping and genome-wide association analyses. Properties, advantages, disadvantages, and requirements of alternate designs and analysis strategies will be discussed.
From Markers to Gene Function: Functional ChangeThe one-credit course, From Markers to Gene Function: Functional Change, builds upon the course, Introduction to Marker Association Analysis and QTL Detection, by taking the results from association analyses and helping the students learn how these markers translate into functional changes in the animal genome. Students then learn how these changes translate into differences in animal performance. Topics covered in the course include an introduction to the tools used to generate genomic data followed by introduction and application of key bioinformatics websites, databases to identify causative genetic variation, and develop gene pathways and networks. Ultimately, the whole course is tied back to the overriding concept of this program: livestock genetic improvement.
Prediction and Control of Inbreeding in Breeding ProgramsThe purpose of this course is to gain an understanding of the concepts of inbreeding, the impact of inbreeding on breeding populations, and of strategies to control and manage rates of inbreeding in breeding populations. Topics include definition of inbreeding and identity by descent, the impacts of inbreeding on genotype frequencies, trait means, and trait variances, random drift, computation of inbreeding coefficients in pedigreed populations, using pedigree-based versus marker-based measures of inbreeding, prediction of rates of inbreeding in closed populations, and control and management of inbreeding in breeding populations.
MCMC Methods in Animal BreedingThe goal of this course is to introduce the student to computational techniques based on simulation that have become a staple in the field of animal breeding (and beyond) over the last 20 years. An overview of the most popular Monte Carlo methods will be provided to the students with an emphasis on hands on reproducible examples developed through the R software. Minimal exposure to the R programming language will be required while no previous exposure to Monte Carlo methods is required. While a few examples in the class will be set in a Bayesian framework, no previous exposure to Bayesian statistics is required.
Introduction to R ProgrammingThe goal of this course is to familiarize students the R environment for statistical computing. Part of the course will be devoted to the use of R as a high-level programming language and a gateway for more formal low-level languages. No prior exposure to the language is necessary.
Diane M. Wasser