Teaching

 

ANS (GN) 713 Quantitative Genetics and Breeding       3 cr.

The objective of the course is to provide the advanced graduate student with a sound knowledge of quantitative genetics breeding and genomics. Topics of the course cover the foundations of quantitative genetics with particular emphasis  on their use and consequences in breeding and genomic selections applications.

Pre-req: Stat 512 and familiarity with SAS and R. Elementary notions of matrix algebra and calculus. Offered Fall Semester

 

ANS 590 Linear Mixed Models in Agriculture        3 cr.

The objective of the course is to familiarize the student with LMM technologies with particular emphasis on genetics and genomic predictions. Topics of the course include the Likelihood framework, linear models,  BLUE and BLUP, single and multiple traits mixed models in genetic and genomic predictions.

Pre-req: Stat 512. familiarity with the SAS and R.  Elementary linear Algebra and Calculus. Offered Spring Semester alternate years

 

ANS (CS FOR) 726 Advanced Topics In Quantitative Genetics and Breeding    3 cr.

(team taught with F.Isik and J. Holland) Advanced topics in quantitative genetics pertinent to population improvement for quantitative and categorical traits with special applications to plant and animal breeding. DNA markers – phenotype associations. The theory and application of linear mixed models, BLUP and genomic selection using maximum likelihood and Bayesian approaches. Pedigree and construction of genomic relationships matrices from DNA markers and application in breeding.

Pre-req: Stat 512. familiarity with the SAS and R.  Elementary linear Algebra and Calculus. Offered Spring Semester alternate years

 

DE courses offered through AG*IDEA


MCMC Methods in Animal Breeding: A primer        1 cr.

The goal of this course is to introduce the student to computational techniques based on simulation 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.

Prerequisite: Genetic Prediction. Offered Spring semester.

An Introduction to R Programming       1 cr.

The goal of this course is to 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. Offered Spring semester.