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July 17-21, 2006 Max Delbrueck Center (MDC) for Molecular Medicine Berlin, Germany

Gene Mapping Course in 2006

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  • The 2006 Course Participants
    The 2006 Course Participants

Course Description

Emphasis:

This course was mainly focussed on strategies for genetic mapping of human mendelian and complex traits. The course is for researchers with little or no experience in using linkage programs. It included theory as well as practical exercises. The exercises were carried out using a variety of computer programs. The course participants learned how to use and interpret the results for a number of programs, e.g. MLINK, GENEHUNTER, ALLEGRO, SimWalk2, Merlin, HOMOG, R, and others.

Topics included:

  • parametric methods (two-point and multipoint analysis)
  • allele sharing (nonparametric) methods for sib-pairs and extended families (two-point and multipoint analysis)
  • fine mapping
  • linkage admixture
  • association studies (trios and case-control)
  • sample size estimation (extended families [simulation studies], sib-pairs and case-control data) and estimating empirical p-values

 

The Course instructor

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

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 gmc2006.data.tar.gz
This should create numerous subdirectories containing the exercises-specific data sets.