Statistical Methods for System Genetics

Module: IN2344

Credit: 5 ECTS.

Room (lecture and exercise): 03.13.010

Lecturer: Matthias Heinig, Julien Gagneur, Michael Menden, Gabi Kastenmüller

Lecture: Tuesdays, 14:00 - 15:30

Exercise: Tuesdays, 15:30 - 17:00

Please note that on Tuesday, April 30, the lecture takes place from 09:00 - 11:00 in room 03.13.010 and the exercise from 11:00 - 13:00 in room 00.08.055!

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

Content:

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