Integrative Post-GWAS Methods: Advanced Statistics, Functional Genomics, and Machine Learning
Graduate School of Health Sciences at Aarhus University
This advanced PhD course is designed for health science students interested in understanding and exploring how genome-wide association studies (GWAS) data can provide deeper biological insights. The course provides theoretical knowledge and hands-on experience in GWAS and post-GWAS analytical approaches, focusing on the integration of genetic results with functional genomics data to identify affected genes, tissues and cell types, and to uncover the biological mechanisms underlying the diseases or phenotype of interest. The course further expands on the use of GWAS in the context of polygenic risk scores (PRSs) in disease prediction.
Students will learn to work with publicly available GWAS summary statistics and pre-trained machine learning models for complex traits such as metabolic traits (e.g. obesity, type 2 diabetes) and psychiatric conditions (e.g. ADHD, depression). The course emphasizes how to analyse post-GWAS data, interpret findings, through analyses using a variety of R-based, Linux-based, and web-based tools.
The main focus will be on secondary GWAS analyses, but students will also learn the principles of how to do the primary GWAS.
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