Computational Problems in Modern Human Genetics
Gadi Kimmel
School of Computer Science, TAU
Most of genetic variation among human individuals is due to single
nucleotide polymorphisms (SNPs). These are single base genomic sites at
which mutations occurred during human history and were passed on through
heredity. As a result, two or more different bases (or alleles) are
observed across the population at such sites.
Preliminary analyzes have shown that the knowledge of genome variation is
expected to play a key role in disease association studies. Associating
genome variation with common diseases will shed light on specific areas in
the human genome, and direct researchers to study these spots specifically.
Hopefully, this will enable to improve diagnosis and to develop novel drugs
and other therapies for common diseases, such as cancer and cardio-vascular
diseases. Hence, the identification and analysis of SNPs is currently a
major goal of the international scientific community.
Through the course of my PhD, we studied several of the major computational
problems which arise in the analysis of these new data sets. We used
computational techniques from graph theory, probability and statistical
theory and integrate them with biological principles to develop models for
blocks partitioning, phasing, tag SNPs selection and association studies.
This talk describes the results of my PhD thesis which is done under the
supervision of Prof. Ron Shamir.