DIMACS/RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotype Inference

November 21 - 22, 2002
DIMACS Center, Rutgers University, Piscataway, NJ

Andrew G. Clark, Cornell University and Celera, Andy.Clark@celera.com
Sorin Istrail, Celera, Sorin.Istrail@celera.com
Michael Waterman, University of Southern California and Celera, msw@hto.usc.edu
Presented under the auspices of the Special Focus on Computational Molecular Biology.

The ability to score large numbers of DNA variants (SNPs) in large samples of humans is rapidly accelerating, as is the demand to apply these data to tests of association with diseased states. The problem suffers from excessive dimensionality, so any means of reducing the number of dimensions to the space of genotype classes in a biologically meaningful way would likely be of benefit. Linked SNPs are often statistically associated with one another (in "linkage disequilibrium"), and the number of distinct configurations of multiple tightly linked SNPs in a sample is often far lower than one would expect from independent sampling. These joint configurations, or haplotypes, might be a more biologically meaningful unit, since they represent sets of SNPs that co-occur in a population. Recently there has been much excitement over the idea that such haplotypes occur as blocks across the genome, as these blocks suggest that fewer distinct SNPs need to be scored to capture the information about genotype identity. There is need for formal analysis of this dimension reduction problem, for formal treatment of the hierarchical structure of haplotypes, and for consideration of the utility of these approaches toward meeting the end goal of finding genetic variants associated with complex disease.

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Document last modified on June 20, 2002.