The page uses Browser Access Keys to help with keyboard navigation. Click to learn moreSkip to Navigation

Different browsers use different keystrokes to activate accesskey shortcuts. Please reference the following list to use access keys on your system.

Alt and the accesskey, for Internet Explorer on Windows
Shift and Alt and the accesskey, for Firefox on Windows
Shift and Esc and the accesskey, for Windows or Mac
Ctrl and the accesskey, for the following browsers on a Mac: Internet Explorer 5.2, Safari 1.2, Firefox, Mozilla, Netscape 6+.

We use the following access keys on our gateway

n Skip to Navigation
k Accesskeys description
h Help
Cornell University    
  Jan 21, 2018
Courses of Study 2017-2018
[Add to Favorites]

BTRY 4830 - Quantitative Genomics and Genetics

Spring. 4 credits. Student option grading.

Prerequisite: BTRY 3080  and introductory statistics or equivalent. Co-meets with BTRY 6830 .

J. Mezey.

A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.

Outcome 1: Students will learn a statistical modeling strategy that is both basic and general, as well as how to apply this strategy to learn information about biological systems when analyzing genome-wide data. More specifically, students will learn the mathematics and interpretation of linear statistical models.

Outcome 2: Students will learn what these models can be used to infer when applied to genome-wide genetic and related data.

Outcome 3: Students will learn how to effectively and efficiently analyze large-scale genomic data and how to program in R for this purpose.

Outcome 4: Students will learn the limits of interpretation when applying these statistical models to genomic data when inferring information about a biological system.

[Add to Favorites]