SPEAKER:

Anya Tsalenko
Agilent Technologies

TITLE:

Common approaches to analysis of gene expression and SNP genotype data
for complex diseases.

ABSTRACT:

The sequencing of most of the human genome, the improved understanding
of this sequence, and the fast development of parallel measurement
platforms such as microarrays are three important recent advances in
molecular biology. The combination of these advances drive an
increasing interest and activity in measuring gene expression profiles
of different cell types and disease stages or types as well as in
understanding the role of human sequence variation in influencing
disease and treatment susceptibility. Gene expression profiling data
are accumulating at a fast rate and the association between profile
properties and clinical attributes is being explored. Such studies
reveal sets of genes that separate phenotypically distinct classes of
samples according to their expression signatures. The study of
naturally occurring DNA sequence variations and the relationship
between genetic variants and clinically meaningful phenotypes precedes
the interest in expression profiling by many years. The recent
developments mentioned above, bring them together and allow for the
exploitation of common characteristics and for the study of joint
properties.

I will describe statistical methods, visualization tools and
algorithmic approaches to questions that arise in pursuing
correlations between gene expression profiles, SNPs as well as sets of
SNPs, and sample properties. Some of the methods draw on the common
characteristics of expression data and genotyping data in case/control
studies.

These methods will be demonstrated on gene expression data sets
collected at the Reynolds Cardiovascular Clinical Research Center at
Stanford University, and on SNP genotype data sets collected at the
University of Chicago for the type 2 diabetes studies.  Analysis was
performed using prototype software package 'BioTools' developed at
Agilent Labs.