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Variation in human DNA
sequences account for a significant amount of the genetic risk factors for
common disease such as hypertension, diabetes, Alzheimer's disease, and
cancer. Identifying the common variation that influences susceptibility to
disease will usher in a new era of personalized medicine where treatment
decisions are based not only on clinical observations, but also take into
account an individual's genetic makeup. Recent technological advances in
high-throughput genotyping technology allow us for the first time to collect
human variation information on a large enough scale to identify the
variation involved in disease. This talk focuses on two challenges
associated with the analysis of high-throughput genotype data. Since the new
cost effective technologies obtain human variation information from both
pairs of human chromosomes simultaneously, the first step in analysis of
these datasets is computational prediction of the human variation on each
chromosome or the haplotype phasing problem. We discuss the results of our
collaboration with Perlegen Sciences on the phasing of whole genome human
haplotypes. A second challenge is the association of whole genome variation
data to phenotypic data or clinical traits. Using the inbred mouse as a
model organism, we demonstrate how our methods are able to discover many
regions in the mouse genome associated with phenotypes and how many of our
predictions are consistent with genes known to influence specific traits.
Bio:
Eleazar Eskin received his Ph.D. in Computer Science at Columbia University
in October 2002. After graduation, he was a post-doctoral researcher at
Hebrew University in Jerusalem, Israel. He is currently an Assistant
Professor in Residence in the Department of Computer Science at the
University of California, San Diego and affiliated with the California
Institute of Telecommunications and Information Technology (Calit2).
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