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Course Description

This course provides an overview of the principles and applications of R packages tailored specifically for genetic research for crop improvement. Through a blend of theoretical knowledge and practical application, you will learn to navigate and harness the power of R packages tailored for genetic research in plants.

 

After a quick tutorial on the JupyterLab environment, you will delve into the fundamentals of genetic analysis, including various statistical methods, genome-wide association studies (GWAS), and applications of linear and mixed models. Through practical exercises and assignments, you will gain proficiency in utilizing R packages like rTASSEL and sommer to analyze large-scale genetic datasets efficiently. By the end of the course, you will be empowered to apply advanced genetic analysis techniques to enhance breeding programs, accelerate crop improvement, and address agricultural challenges. Furthermore, your analysis will be reproducible by you and your colleagues.

 

Note: This course is designed for plant scientists with expertise in breeding and genetics who are also comfortable working with the programming language R. Basic familiarity with statistics principles may be helpful but is not required. The course must be completed on a laptop or desktop with adequate processing speed to run complex R packages within a JupyterLab environment, and stable high-speed internet is required.


 

Benefits to the Learner

  • Use JupyterLab for documentation and execution of code.
  • Understand rTASSEL functionality, and perform genome-wide association analysis (GWAS) using rTASSEL and R for additional visualization and analyses.
  • Explore mixed model methodologies and execute common mixed models for the analysis of trials and genetic evaluations.

Applies Towards the Following Certificates

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