First Year Courses

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

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
Program Summary
Cost per credit hour:

2018-2019: $580

1 Hours


1 Hours

University Contact
These campus coordinators can help you navigate Great Plains IDEA. Click on the university name to learn more about how Great Plains IDEA works at that campus. Gayle Roslund
Casey Smith
Rachel Ohmes
Julie Holder
Diane M. Wasser
University Members
Members of the Great Plains IDEA are universities accredited by a regional accrediting agency recognized by the U.S. Department of Education. Member universities recruit, admit and graduate students, teach in an academic program and contribute to the leadership and maintenance of the alliance. Membership in the alliance is a selective process that engages institutional leadership at all levels.

– – April-Dawn Knudsen, Community Development Graduate