Graduate Courses

First Year Required Courses

Quantitative Genetics Applications of Matrix Algebra

This course teaches the matrix algebra skills needed to describe and solve problems in the agricultural and life sciences, with a particular focus on quantitative genetics. The course is designed for students with no prior knowledge of matrix algebra. The course includes vocabulary, concepts, application, and theory of matrix algebra relevant to graduate students in the agricultural and life sciences. Prerequisite: Graduate standing

Primer to Quantitative Genetics

This course will provide students with an introduction to the language and basic principles of quantitative genetics. Its purpose is to develop foundational knowledge in students entering a graduate program in animal breeding and genetics. Topics included will be the basic model for quantitative genetics (additive and non-additive genetic effects, including Mendelian sampling, and environmental effects), sources of variation, heritability, family resemblance and repeatability, selection response, and family selection. Expected values and concepts in applied statistics (e.g., linear regression) will also be considered. Prerequisite: Quantitative Genetics Applications of Matrix Algebra

Selection Index Theory and Application

This course will increase your skills and knowledge related to the design of animal breeding programs with a focus on the application of index theory to the definition of breeding objectives in animal agriculture. The course will also introduce approaches for deriving economic weights, which are useful when predicting economic response to selection. Prerequisite: Primer to Quantitative Genetics

Economic Breeding Programs

Economic selection indexes depend on relative economic values to derive a measure of merit. This course will provide background in system analysis techniques needed to derive sensible estimates of the relative economic values and apply them, particularly in indexes composed of estimated breeding values. Prerequisite: Selection Index Theory and Application

First Year Elective Courses

CyberSheep: A Genetic Simulation Game

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

History and Perspectives in Animal Breeding and Genetics

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

Heterosis and Crossbreeding Systems

Students completing this course will be able to evaluate and compare various crossbreeding mating schemes through predicted performance of the potential progeny and overall system performance. An introduction into selection within the parameters of the crossbreeding system will also be discussed. Prerequisite: Selection Index Theory and Application

Introduction to R Programming

This course will familiarize students with 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. Prerequisite: Graduate standing

Second Year Required Courses

Linear Models in Animal Breeding

Students 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. Prerequisite: Selection Index Theory and Application

Genetic Prediction

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

Applied Variance Component Estimation

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

Marker-Assisted and Gene-Assisted Selection

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

Introduction to Marker Association Analysis and QTL Detection

This course will introduce the basic concepts of using genetic markers to identify QTL and estimate 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. Prerequisite: Marker-Assisted and Gene-Assisted Selection

From Markers to Gene Function: Functional Change

This course builds upon the course titled 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 which is livestock genetic improvement. Prerequisite: Introduction to Marker Association Analysis and QTL Detection

Second Year Elective Courses

Prediction and Control of Inbreeding in Breeding Programs

Students will 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. Prerequisite: Genetic Prediction

MCMC Methods in Animal Breeding

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

Introduction to R Programming

This course will familiarize students with 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. Prerequisite: Graduate standing
Program Summary
Cost per credit hour:

2020-2021: $590

1 Hours

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