Comparative Approach to Genomics of Complex Traits

Project: Research project

Description

DESCRIPTION: (provided by applicant) The determinants of genetic susceptibility
for most common traits are complex, likely to involve contributions from
multiple gene variants. These variations in gene function that affect critical
phenotypes can represent amino acid replacements as well as extragenic
differences that might affect expression levels; in most instances, these are
single nucleotide polymorphisms (SNPs). Although one would like to assay for
these gene variants in a completely unbiased fashion, ultimately measuring the
contribution of every variant of every gene, this is obviously impractical at
this point in time. An alternative strategy is to identify those genes most
likely to make contributions to disease variation, identify the variations
within this group of genes, and then conduct association studies to link gene
variants with disease phenotype. Using cardiovascular disease as a model
system, this proposal describes a multi-dimensional approach to this overall
problem. In particular, we will use multiple methods for the identification of
candidate genes most likely to make contributions to disease variation within
populations of patients. This work will take advantage of three unique clinical
resources here at Duke. First, we have begun the collection of a large series
of aorta samples from heart transplant donors as a source of vascular tissue
for gene expression analysis. These samples are unique in the volume of the
samples (hundreds) as well as the range of phenotype: early stages of
atherosclerosis to advanced forms of the disease. As such, it provides an
opportunity to match gene expression profiles with the development of disease
in a very unique way. This will be the focus of work in Component 1 as well as
statistical efforts in Component 5. Second, a large study of the genetics of
early onset cardiovascular disease, representing a collaboration between Duke
investigators and GlaxoSmith-Kline, offers the opportunity to identify loci
that are linked with the development of disease. This provides a mechanism for
the identification of additional candidate genes without any bias whatsoever,
including whether the gene actually functions within cardiovascular tissue or
not. This represents the focus of Component 2, and bioinformatic efforts in
Component 6. The combined efforts of Component I and 2, taking different
approaches to the identification of candidate genes, will then be the source of
substrate to discover SNPs within this group of genes (Component 3). Much of
this work will take advantage of existing information regarding SNPs as well as
other major studies directed at cardiovascular disease. But, it will also
necessitate efforts within this component to identify as exhaustively as
possible those sequence variants that can then be the subject for assays in
clinical populations. Third, and possibly the most important asset of this
program, is the Duke Cardiovascular Database, an effort initiated some thirty
years ago at Duke to follow the clinical course of every cardiovascular disease
patient. As such, we now have access to over 40,000 patients who are being
followed on a regular basis, creating a clinical dataset that is unmatched.
This clinical dataset provides a completely unique resource for this study,
both from the quantity as well as the quality of patient clinical data to allow
the validation of candidate gene variants with disease variation. Thus, the
SNPs identified in Component 3 will go into an expansive genotyping program
(Component 4), to bring these candidate genes to a point of validation. A major
challenge in an undertaking of such magnitude will be the statistical power to
find associations in complex situations. Component 5, will develop the
methodologies for understanding the complex gene expression datasets, will also
develop the statistical approaches to the analysis of the complex genotyping
studies. The program will also enhance and integrate with existing and
developing educational programs in bioinformatics and genome technology at Duke
(Component 6). This synergy will be of clear benefit to the program, bringing
in talented investigators from multiple disciplines in each area critical for
the program. Hence, our project will advance the frontiers of genome sciences
and technology in the field of common traits. The culmination of our program
will provide essential tools to clinicians to improve risk stratification of
patients and to design novel preventive and therapeutic strategies.
StatusFinished
Effective start/end date9/30/028/31/08

Funding

  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health

Fingerprint

Single Nucleotide Polymorphism
Coronary Artery Disease
Microsatellite Repeats
Genetic Association Studies
Alleles
Genes
Age of Onset
website
Multiplex Polymerase Chain Reaction
DNA
Medical Genetics
interaction
Oligonucleotides
Genome
Ligation
Chromosome Mapping
Genotype
Genetic Predisposition to Disease
Costs and Cost Analysis
resources

ASJC

  • Medicine(all)