About the course
The majority of human traits including common diseases are polygenic in nature, that is, they are affected by numerous genetic variants with small individual effects. During the past couple of decades thousands of genetic loci associated with various human traits have been discovered throughout the genome using Genome-Wide Association Studies (GWAS).
Although GWAS proved to be an effective approach to investigate the genetic architecture of complex human traits, it has left several questions unanswered setting the stage for more research to be conducted building on it.
In the course we will cover:
- basic aspects of genetics and statistics important for understanding the GWAS framework
- essential stages of the standard GWAS pipeline, including processing of raw genotypes, imputation, QC, association (meta-) analysis and interpretation of association results. Tools to conduct these steps (including practical training)
- publicly available resources providing access to GWAS summary statistics, online analysis tools and other related data
- several strategies for advanced post-GWAS analysis, including polygenic risk assessment and investigation of genetic overlap and correlation between different traits (including practical tasks)
- clinical implications of GWAS
Credits
3 ECTS (directly available for PhD students of the Medical Faculty at UiO, PhD students at other faculties and universities should ask about availability at their educational offices)
Level
PhD
Registration
Please fill in this registration form if you would like to sign up for this course. We have a maximum capacity of 40 students.
Course objectives
After completing this course, students will be able to:
- apply basic genotyping quality control procedures using plink to assess data integrity and prepare for GWAS
- perform GWAS using plink and meta-analyse GWAS results across ancestries using METAL to identify trait-associated variants in diverse populations
- annotate GWAS results with FUMA to link variants to genes
- compute and compare polygenic risk scores using PRSice to evaluate predictive accuracy and risk stratification
- calculate the heritability of and genetic correlation between traits using LD score regression to estimate the influence of (shared) genetics on traits
- explain concepts of genetic variation and population genetics to support understanding of GWAS results
- describe statistical genetic principles underlying GWAS design and analysis
- evaluate GWAS designs and interpret results to identify pitfalls and justify best practices
- discuss the theoretical background and interpret the results of annotation, heritability, genetic correlation, and PRS to place GWAS findings in biological and clinical context
- list advanced post-GWAS methods and recall their purpose in genetic research
- discuss GWAS applications in current biological and medical research to identify their interdisciplinary impact
- reflect on personal and collaborative learning to develop skills in teamwork and self-awareness as a researcher
Program
Teaching is organized as a four-day (23.03.2026 – 26.03.2026) intensive online-course (Zoom). Start time is at 9AM (CET) and lasts approximately 5 hours per day (including breaks).
The course consists of a series of lectures and workshops. Practical tasks will be conducted on a server. Accounts and passwords to access the server will be provided on the first course day.
Additionally, participants should read recommended literature and prepare a final report on a given topic.
Prerequisites
Basic knowledge of genetics and statistics, as well as some experience with UNIX command line (workshops) is recommended for a smooth advance through the course.
Examination
The exam will be in the form of reports that participants should prepare within 4 weeks after the course week. Topics for the reports will be discussed at the last day of the course.
Reports will be evaluated and classified as “pass” or “fail” within two weeks after receiving. Students will also get reviewer’s comments on their report regardless of the outcome.
Contact
If you have any questions, please contact Elleke Tissink