GABeyond2026
From Statistical Genetics Courses
Contents
Genetic Association and Beyond:
Statistical Methods to Elucidate Complex Trait Etiology
September 14-18, 2026 Max Delbrück Center (MDC) for Molecular Medicine Berlin, Germany
This new course is being offered in 2026 at the MDC – Genetic Association and Beyond: Statistical Methods to Elucidate Complex Trait Etiology – that reflects advances in the field of statistical genetics which have occurred in recent years. The course will be held from September 14-18, 2026. The goal of the course is to teach course participants both the theory and application of statistical methods to provide insight into the etiology of human complex traits using a variety of statistical and bioinformatic methods.
Course Fees
The cost of the 5-day course is: Academic Researchers: 975 EUR and Private (for-profit) Companies: 1,950 EUR. The fee covers tuition, the Monday course dinner, and all course-related materials. It does not cover room or board. Housing is available for participants at nearby hotels.
Topics Covered
The course curriculum includes:
- Association analysis of qualitative and quantitative traits (rare and common variants)
- Obtained from genotype arrays, imputation, and exome and whole genome sequence data
- Data quality control (genetic and phenotype)
- Linear mixed models (LMM/GLMM)
- Controlling for population admixture, substructure, and confounders
- Gene x gene and gene x environment interactions
- Imputation of variant data
- Pleiotropy, mediation analysis, and Mendelian randomization (MR)
- Heritability estimation, genetic correlations, and polygenic scores (PGS)
- Power and sample size estimation
- Permutation, family-wise error rate (FWER), and false discovery rate (FDR)
- Meta-analysis and fine mapping
- Proteomic-wide association studies (PWAS)
Computer Exercises
Hands-on exercises will be conducted using the following programs:
- ANNOVAR
- bcftool
- Fast-LMM
- GCTA
- LD clumping
- LDSC regression
- Mediation
- PLINK
- R
- REGENIE
- TwoSampleMR.
Instructors
- Suzanne Leal (Columbia University)
- Michael Nothnagel (University of Cologne)
