Course Integrative Post-GWAS Methods: Advanced Statistics, Functional Genomics, and Machine Learning

The course Integrative Post-GWAS Methods: Advanced Statistics, Functional Genomics, and Machine Learning is being offered by the Graduate School of Health, Aarhus University, 2026.

Criteria for participation: PhD students in health, medical, and clinical sciences with/without prior knowledge of GWAS.

Requirements for participation:

• PhD students in medicine, clinical sciences, or health sciences
• Experience with R and Linux is highly recommended for smooth participation
• Familiarity with GWAS is beneficial but not mandatory

Aim: 
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.

Learning outcomes: 

After completing the course, students will be able to:

• Understand the key principles and applications of GWAS and post-GWAS analyses.
• Identify likely causal variants in associated genomic loci.
• Link variants to genes using genomic proximity and functional evidence.
• Integrate genetic findings with functional genomics data.
• Perform tissue, and pathway-level enrichment analysis.
• Connect genetic signals to relevant biological contexts, including specific cell types.
• Understand the basics of PRSs in disease risk prediction

Perform disease prediction using pre-trained machine learning models.

Workload: The full workload of the course is expected to be 21 teaching hours plus preparation time (equivalent to 2.5 ECTS)

Content: The course will be delivered across 3 full days and includes lectures and computer-based exercises. Topics include:

1. Introduction to GWAS
   Introduction to GWAS and summary statistics
2. Identify credible sets of variants
   Identify the credible sets of variants using tools like FINEMAP
3. Variant-to-Gene Mapping
   Mapping credible variants to genes via proximity and functional genomics data using tools like FUMA
   Mapping credible variants to gens via integration with functional genomics data using FLAMES
4. Gene-to-Function Mapping
   Gene level enrichment analysis using MAGMA
   Linking variants to cell types using scRNA-seq (SCDRs)
5. Linking full GWAS results to tissues, cell types and pathways
   SNP-Based Partitioned Heritability (LDSC)
   MAGMA enrichment analyses (FUMA)
6. Machine learning and risk prediction (Simon Rasmussen group form Copenhagen)
   Introduction to machine learning and polygenic risk score (PRS) including discussion on the use of linear and deep learning methods
   Prediction of disease risk using pre-trained EIR-Foundation Model (EIR-FM)

Instructors: Teachers:

• Ditte Demontis, Aarhus University
• Simon Rasmussen, University of Copenhagen
• Per Qvist, Aarhus University
• Jiyeon Min, University of Copenhagen
• Simon Wengert, University of Copenhagen
• Asmat Ullah, Aarhus University
• Samuele Soraggi, Aarhus University
• Sufyan Suleman, Aarhus University
• Dimitrios Pediotidis-Maniatis, Aarhus University
• Clara Albiñana, Aarhus University

Venue: Aarhus University, Aarhus

Participation in the course is without cost for:

• PhD students, Health Research Year students from Aarhus University
• PhD students enrolled at partner universities of the Nordoc collaboration
• PhD students from other institutions in the open market agreement for PhD courses

Course dates:

• 24 August 2026 09:00 - 16:00
• 25 August 2026 09:00 - 16:00
• 26 August 2026 09:00 - 16:00