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July 4-8, 2011 Max Delbrueck Center (MDC) for Molecular Medicine Berlin, Germany

Genetic Association Course in 2011

The 2011 Genetic Association Course

Course Description

The goal of the course was to teach the course participants both theory and application of methods for population based association analysis, with a concentration on the analysis of whole genome scan data. Emphasis in this course was on strategies for genetic mapping of complex human traits. It included theory as well as practical exercises.

The exercises were carried out using a variety of computer programs (PLINK, GenABEL, MACH, UNPHASED, EIGENSTRAT and R, etc.) and with pencil and paper.

TOPICS included: Association analysis of qualitative and quantitative traits; single marker and haplotype analysis; analysis of whole genome association study data; data quality control; haplotype reconstruction; tagSNP selection; controlling population admixture (genomic control, principal components analysis, etc); imputing genotype data; detecting gene x gene and gene x environmental interactions; power and sample size estimation; permutation (estimating empirical p-values); and false discovery rate (FDR).


The Course instructor

Michael Nothnagel (University of Kiel, Germany) and Suzanne M. Leal (Baylor College of Medicine, Houston, U.S.A.)

Data Sets

The data sets used during the computer exercises are available for download below. Due to its size, the data archive for the impute exercise will be available separately from the other data sets. To save the files to your computer, press the right-click button of your mouse (or Strg/Ctrl + mouse-click under MacOS), select 'Save as ..' and then choose an appropriate directory at your local disc.

To unpack the data under Windows, use either the integrated functionality of Windows 7's explorer or one of the following programs: 7-zipWinZip.

To unpack the data under linux/MacOs, open a shell, change to the directory that contains the archive files and type the following command:
tar -xvzf data.tar.gz
This should create numerous subdirectories containing the exercises-specific data sets.