Class Dates

10/30/2023 to 12/10/2023

Units

1.0

Delivered By

Online Asynchronous

Instructor(s)

Jennifer Bormann
(jbormann@ksu.edu)

Meeting Times

Meets by Appt

Meeting Location

Distance Education

Description:

The goal of this course is to extend on concepts 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 will have an applied knowledge of approaches used to estimate the G and R sub-matrices 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.

• This course is part of the Quantitative Genetics program in the AG IDEA consortium and is taught by CSU. Please contact gpideacc@ksu.edu for permission to enroll.
• This course is part of the Quantitative Genetics program in the AG IDEA consortium.

Permissions:

• Instructor Jennifer Bormann's permission is always required.

Student Requirements:

• Computer meeting recommended requirements.

• Internet access

• This course is part of the Quantitative Genetics program in the AG IDEA consortium and is taught by CSU. Please contact gpideacc@ksu.edu for permission to enroll.

Prerequisites:

• Genetic Prediction