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Asthma is a chronic inflammatory syndrome of the airways, which afflicts over
12 million people in the USA at an annual cost exceeding 7 billion dollars.
It is characterized physiologically by recurrent
airway obstruction that resolves spontaneously or as a result of treatment, although
it's etiology remains unknown. Despite the lack of understanding of its etiology,
there are effective treatments for asthma.
There are three main classes of asthma
treatment in use today:
A given patient may use one, two, or all three types of
treatments. The treatments are not uniformly effective; they vary in their efficacy
amongst individuals and there are compelling preliminary data
suggesting that at least half of the variance in the treatment response may be
genetic in origin; our proposal is structured to identify the genes responsible
for this variable response.
- inhaled beta-agonists
- inhaled corticosteroids
- leukotriene modifiers
Our proposed approach is straightforward. For each of the three major asthma medications we will:
- Define a panel of target genes that are likely to modify the function of the pathway.
- Scan these targets for DNA sequence variants.
- As variants are identified we will ascertain the evolutionary history of these variants and cover the gene completely.
- We will use a case-control association approach to define specific genotypes and haplotypes associated with either a salutary treatment response or lack thereof. We will utilize genomic control to assess population stratification.
- For genes with positive associations in our association studies, functional implications in vitro will be ascertained in asthma patients, who have been (existing patient resources) or will be (to be acquired patient resources) phenotype with respect to the response to treatment with the class of asthma medication of interest.
- Once genotypes associated with potential pharmacogenetic predictive value are defined, we will collaborate with the NIH sponsored ACRN to study patients with specific genotypes to determine if they provide predictive information about treatment responses.
Our approach will allow us to ascertain the pharmacogenetic basis for the observed variability in asthma treatment responses.