Pharmacogenetics of Asthma Treatment

描述（由申请人提供）：尽管有三大类哮喘药物可用，但对治疗的反应是高度可变的，多达一半的患者无反应。药物遗传学提供了“个性化医疗”的希望，据此，个体对治疗的反应将由他或她的基因组成先验地指导。虽然我们在确定影响哮喘药物反应的候选基因方面取得了实质性进展，但这些关联在反应的变异性中所占的比例都很小。很明显，为了实现哮喘“个体化治疗”的目标，我们需要确定多个额外的药物遗传学位点。最近的技术进步提供了前所未有的能力，可以在全基因组范围内快速识别影响药物治疗反应的遗传变异。该项目的主要目标是通过有效而详细地识别与哮喘药物反应相关的新基因，加强我们在哮喘药物遗传学领域的先前工作。为此，我们制定了以下具体目标：我们将分析来自3493名哮喘临床试验受试者的b2肾上腺素能和糖皮质激素治疗反应的GWAS数据，并进行关联测试以确定哪些遗传变异与改变的治疗反应相关。将利用机器学习方法对遗传关联数据进行建模。2. 将在分子、细胞和整合基因组水平上探索强大的和可复制的药物遗传关联，以建立功能变异和药物遗传机制。功能方法将涉及生物信息学工具、mRNA分析、系统遗传学以及哮喘治疗反应性的细胞和动物模型。3. 我们将利用临床和功能知识以及遗传关联的高分辨率分析，包括上位性和基因-药物相互作用，开发并验证一套预测哮喘对短效b2肾上腺素能和吸入糖皮质激素药物治疗反应的测试。其他目标涉及我们与PharmGKB和PGRN的合作，并帮助对药物遗传学感兴趣的研究人员。除了GWAS样本外，还有近7000个DNA样本和匹配的哮喘表型数据可用于复制测试，以验证初始GWAS结果和预测测试。我们相信这些发现将揭示足够的新信息，结合遗传和临床特征的预测哮喘药物反应的预后测试的工作模型将会产生。相关性：哮喘影响全球约3亿人，美国每年的直接医疗费用约为200亿美元，其中大部分用于药物和住院费用。遗传变异的鉴定可以作为预测哪些个体会或不会对治疗有反应的预后测试的基础，从而最大限度地减少试验和错误药物分配的需要以及由于治疗不足而住院的风险，有可能大大降低与哮喘相关的发病率和经济负担。

DESCRIPTION (provided by applicant): Despite the availability of three major classes of medications for asthma, the response to therapy is highly variable, with as many one-half of all patients being non-responders. Pharmacogenetics provides the promise of "personalized medicine", whereby an individual's response to therapy will be guided a priori by his or her genetic make-up. While we have made substantial progress in identifying candidate genes influencing the response to asthma medications, none of these associations accounts for more than a small proportion of the variability in the response. It is clear that to accomplish the goal of "personalized medicine" in asthma, we need to identify multiple additional pharmacogenetics loci. Recent advances in technology offer the unprecedented ability to rapidly identify the genetic variants that influence drug treatment response on a genome-wide scale. The major goal of this project is to enhance our prior work in the field of asthma pharmacogenetics via the efficient, but detailed, identification of novel genes associated with the asthmatic response to medications. To accomplish this, we have structured our specific aims as follows: 1. We will analyze GWAS data from 3493 subjects in asthma clinical trials of B2-adrenergic and glucocorticoid treatment response, and perform association testing to determine which genetic variants are associated with an altered therapeutic response. Machine learning approaches will be utilized to model the genetic association data. 2. Robust and replicated pharmacogenetic associations will be explored at the molecular, cellular, and integrative genomic levels to establish functional variants and pharmacogenetic mechanisms. Functional methods will involve bioinformatics tools, mRNA profiling, systems genetics, and cellular and animal models of asthma therapeutic responsiveness. 3. We will develop and validate a set of predictive tests of asthma therapeutic response to short acting B2-adrenergic and inhaled glucocorticoid drugs using clinical and functional knowledge and high-resolution analysis of genetic associations, including epistasis and gene-drug interaction. Additional aims relate to our collaboration with PharmGKB and the PGRN and helping investigators interested in pharmacogenetics. In addition to the GWAS samples, nearly 7000 DNA samples and matching asthma phenotypic data are available for replication testing to validate the initial GWAS results and predictive tests. We believe that these findings will uncover sufficient new information that working models of prognostic tests combining genetic and clinical traits in the prediction of drug response in asthma will result. RELEVANCE: Asthma affects an estimated 300 million individuals worldwide and accounts for approximately $20 billion in direct health care costs in the United States annually, with the greatest proportion of those costs allocated to medication and hospitalization costs. The identification of genetic variants that can be used as the basis of a prognostic test to predict which individuals will or will not respond to therapy, thereby minimizing both need for trial and error medication dispensing and risk of hospitalizations due to inadequate therapy, has the potential to substantially decrease both morbidity and financial burden related to asthma.