Course List

Basic concepts and methods for design and evaluation of genetic improvement programs for livestock. Prediction of response to selection, selection index theory, multiple trait selection, inbreeding, crossbreeding, and marker-assisted selection.

Advanced concepts in design and evaluation of animal breeding programs, including modeling and optimization, derivation of economic values, gene-flow, and predicting rates of inbreeding.

This course is an introduction and application of R software for basic and intermediate tasks in quantitative genetic analyses. Recommended prerequisite is Selection Index Theory and Application or equivalent training to ensure adequate understanding of quantitative genetic concepts for application in R software.

This course will 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. Prerequisite: Genetic Prediction

This course teaches students to make informed and effective decisions in a livestock breeding program by providing “hands-on” experience with selection and mating decisions, and their consequences. The vehicle for this instruction is “CyberSheep,” a web-based genetic simulation game played by teams of students. The genetic gains achieved in livestock breeding programs have the advantages of being permanent, cumulative and, in most cases, highly cost-effective. Still, such gains require time to achieve; in the course of an academic degree, let alone a semester or quarter, there is very little opportunity for students to witness the consequences of breeding decisions in any of our livestock species. Thus, CyberSheep is designed to offer students a virtual opportunity to “see,” in real-time, the outcome of their decision-making, and to experience the stochastic (chance) elements of a breeding program. Prerequisite: Graduate standing

This course will increase student understanding of best linear unbiased prediction and 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. Prerequisite: Linear Models in Animal Breeding

This course provides students with a historical perspective of the discipline of Animal Breeding and Genetics and an appreciation for the contributions of several scientists that have significantly impacted the discipline. Weekly lectures will consist of pre-recorded interviews with scientists that have had an international impact in the field of animal breeding and genetics. Prerequisite: Graduate standing

This course will 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. Prerequisite: Genetic Prediction

This course will introduce students to computational techniques based on simulations 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. Prerequisites: An Introduction to R Programming; Genetic Prediction

The course objective is to introduce quantitative genetics models and tools and apply them in a variety of contexts to enable students to approach and resolve quantitative problems in their own genetical research. The course presents the theory of Mendelian loci arranged on chromosomes and how loci, genetic maps, and populations are characterized. It introduces how data from DNA markers can be used to further our understanding of underlying genetic factors, describes mechanisms that change allele frequencies at Mendelian loci, considers how Mendelian loci affect continuously distributed and environmentally sensitive traits, models observations of such traits in terms of resemblance between relatives, and uses all of these topics to develop more effective programs for genetic improvement of agriculturally important animal and plant populations.