Statistical Methods for System Genetics

Module: IN2344

Credit: 5 ECTS.

Room (lecture and exercise): MW 0250, Ludwig-Prandtl-Hörsaal (5502.EG.250)

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

Lecture: Thursdays, 14:00 - 15:30, room MW 0250, Ludwig-Prandtl-Hörsaal (5502.EG.250)

Exercise: Thursdays, 15:30 - 17:00, room MW 0250, Ludwig-Prandtl-Hörsaal (5502.EG.250)

Lecture Language: English

Prerequiste (recommended):

  • Basics in biology / genetics
  • Data analysis and visualization in R
  • Basics of statistics and probability

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
  • linear mixed models to deal with population structure
  • experimental techniques to measure gene expression
  • algorithms for transcriptome quantification from NGS
  • efficient algorithms for expression QTL analysis
  • methods of metabolome quantification
  • algorithms based on gene sets
  • statistical concepts for causal inference such as Mendelian randomization
  • regularized linear models and its applications in genetics
  • network inference methods such as Graphical Gaussian models
  • the application of graphical models for the integration of multiple OMICS data sets


  • Introduction to human genetics
  • Quantitative genetics / GWAS
  • Multiple hypothesis testing
  • Population structure
  • Transcriptomics
  • Metabolomics
  • Enrichment algorithms
  • Causal inference
  • Regularized linear models
  • Network models
  • Multi-OMICS data integration