First Year Courses
Quantitative Genetics Applications of Matrix AlgebraThe goal of this course is development of skills in matrix algebra to describe and solve problems in the agricultural and life sciences, with particular focus on quantitative genetics. As such, the course is designed for students with no prior knowledge of matrix algebra. It will consider the vocabulary, concepts, application and, to a lesser extent, theory of matrix algebra that is relevant to graduate students in the agricultural and life sciences.
Primer to Quantitative GeneticsThe goal of this course is 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.
Selection Index Theory and ApplicationThe overall goal of this course is to increase your skills and knowledge related to the design of animal breeding programs. The focus will be 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.
Economic Breeding ProgramsEconomic selection indexes depend on relative economic values to derive a measure of merit. The focus of this course is to 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.
CyberSheep: A Genetic Simulation GameThe overarching goal of this course is for students to be able to make informed and effective decisions in a livestock breeding program. In order to accomplish this goal, the course will provide “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 your decision-making, and to experience the stochastic (chance) elements of a breeding program.
History and Perspectives in Animal Breeding and GeneticsThe goal of this course is provide 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.
Heterosis and Crossbreeding SystemsStudents 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.
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.
University ContactThese 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
Diane M. Wasser