Computational Modeling for System Genetics

Module: CIT4230001

Credit: 6 ECTS

Room (lecture and exercise): Seminarroom Taurus 1, Galileo (8120.EG.002)

Lecturer: Matthias Heinig, Julien Gagneur, Gabi Kastenmueller, Michael Menden, Paolo Casale, Elefteria Zeggini

Lecture: Thursdays, 14:00 - 15:00, semiar room Taurus 1, Galileo (8120.EG.002)

Exercise: Thursdays, 15:00 - 17:00, semiar room Taurus 1, Galileo (8120.EG.002)

Lecture Language: English

Prerequisite (recommended):

Intended Learning Outcomes: At the end of the module, students understand / are able to practically implement:

  • the challenges of complex trait genetics
  • statistical models for QTL mapping and GWAS
  • methods for adjustment for multiple testing
  • regularized linear models and its applications in genetics
  • linear mixed models to deal with population structure
  • experimental techniques to measure gene expression
  • efficient algorithms for expression QTL analysis
  • statistical concepts for causal inference such as Mendelian randomization
  • network inference methods such as Graphical Gaussian models and application to omics data (metabolome, transcriptome)
  • Apply some of the above-mentioned techniques on an actual problem from systems genetics. Evaluate model performance, calibration and provide biological interpretation of its application to real data.


This is a two-part-module: (1) Eight lectures introduce basics of systems genetics, and statistical models employed. The Eight lectures are supported with tutorials in R or python. In addition, we will provide a script covering statistical concepts required for the lectures that we expect the students to be familiar with before the start of the lectures.

This is followed by (2) an eight-week hands-on-project which will be suggested and supervised by lab members from the lecturers. This way, you will be given a unique opportunity to work on ongoing research projects currently addressed by the respective lab.

During the lectures, the following topics will be covered:

  • Introduction to human genetics and genome-wide association studies (GWAS)
  • Population structure
  • Polygenic risk score
  • Gene-mapping and variant fine-mapping
  • Gene expressions QTLs (eQTLs)
  • Causal inference with omics data (metabolomics, transcriptomics, etc.)
  • Omics approaches for rare diseases

 Over these lectures, various machine learning methods are introduced including:

  • Linear regression and hypothesis testing
  • Linear mixed models
  • Regularized linear models
  • Multiple testing correction
  • Graphical Gaussian models