From Genetic Variants to Risk Prediction

About

This hands-on summer course introduces modern genomic data analysis and is organised within the framework of the Horizon Europe TeamPerMed – Centre for Data-Enriched Medicine (project 101060011). It directly supports TeamPerMed WP2 and WP4 by building capacity in genomic risk prediction methods (GWAS, PRS) and reproducible analysis pipelines that underpin our pilot studies and future clinical decision support tools. Starting from raw genotype-phenotype datasets, we will explore how to clean, visualise, and interpret complex biological patterns. Along the way, students will learn practical skills that underpin today’s genetic research, including running exploratory analyses (EDA), applying quality control (QC), uncovering population structure, and conducting basic genome-wide association studies (GWAS).

Students will work in groups and learn to interpret findings from a biological perspective, understanding how ancestry influences the signals we detect. Students will build confidence in core computational methods, such as dimensionality reduction, clustering, and polygenic risk scoring, while maintaining a focus on clarity and reproducibility

Course leader

Linda Repetto Research Fellow of Population Genetics at the University of Tartu Institute of Genomics Elia Tiso Junior Research Fellow of Population Genetics at the University of Tartu Institute of Genomics Kristi Läll Research Fellow of Statistical Genetics at the University of Tartu Institute of Genomics

Target group

master's, PhD students

Course aim

By the end of the course, students will be able to:

- perform exploratory data analysis (EDA) and visualise genotype-phenotype datasets;
- apply quality control (QC) procedures to genomic data;
- conduct dimensionality reduction and clustering to identify population structures and more;
- run simplified GWAS and interpret association signals;
- compute and interpret simple polygenic risk scores (PRS);
- integrate computational analysis with biological understanding of ancestry;
- understand where statistical genetics methods fit in the broader TeamPerMed research pipeline (data → models → trials → guidelines → socio-economic impact);
- collaborate effectively and maintain reproducible workflows;
- present findings in a clear, visually appealing, and scientifically rigorous manner.

Fee info