Association Mapping: Design issues and data analysis approaches

 

One of the following instructors will teach this tutorial:

Jotun Hein, Oxford University, UK

Leif Schauser, Aarhus University, DK

 

Biographic sketches: Teaching experience

Jotun Hein (Professor of Bioinformatics, Dept. Statistics, Oxford University) has published extensively on topics related to LD mapping. He is co-authors (together with Mikkel Schierup and Carsten Wiuf) of a book entitled "Gene Genealogies, Variation and Molecular Evolution: A primer in applied coalescent theory" to be published at Oxford University Press in September 2004. His teaching experience includes, apart from regular university courses in molecular evolution and population genetics, the teaching in the 2003 and 2004 Summer Institute in Statistical Genetics, North Carolina State University, USA and Porto, Portugal on coalescence theory and its applications in LD mapping.

Leif Schauser (Associate Professor, Bioinformatics Research Center, Aarhus University) has extensive teaching experience in the fields of Bioinformatics and Genome Analysis.

 

Goals, objective and motivation of the tutorial

The goal on this tutorial is to introduce the participants to the field of Association Mapping/Linkage Disequilibrium (LD) mapping, useful for the genetic mapping of disease risk factors of complex diseases using association studies of case-control design. The participants will be introduced to theories regarding genealogical processes and models of chromosome evolution which, coupled with the development of powerful statistical tools, have significantly enhanced our abilities to detect signals in this type of studies. Coalescent-based methods of data analysis will be compared to standard statistical approaches. Design issues as well as putative pitfalls such as population structure will be discussed. The motivation for this tutorial is the growing interest in large-scale association studies of complex diseases, both from academic institutions, hospitals and industry.

 

Tutorial Level: Intermediate to advanced.

 

Intended audience: This tutorial should be of prime interest to bioinformaticians and statisticians working with the design of association studies and the analysis of data resulting from such studies. No specific background is expected, but a basic understanding of statistics and biology is required.