Pharmacogenetics of Asthma Treatment

描述（由申请人提供）：尽管有三种主要类型的哮喘药物可用，但对治疗的反应是高度可变的，有一半的患者是无反应者。药物遗传学提供了“个性化医疗”的前景，据此，个体对治疗的反应将由他或她的遗传组成先验地指导。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，但这些相关性在反应的变异性中所占的比例都不超过一小部分。很明显，为了实现哮喘“个体化用药”的目标，我们需要确定多个额外的药物遗传学位点。技术的最新进展提供了前所未有的能力，可以快速识别在全基因组范围内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但详细地鉴定与哮喘药物反应相关的新基因来加强我们在哮喘药物遗传学领域的先前工作。为了实现这一目标，我们制定了以下具体目标：1.我们将分析来自3493例哮喘临床试验中B2-肾上腺素能和糖皮质激素治疗反应受试者的GWAS数据，并进行关联检验，以确定哪些遗传变异与治疗反应改变相关。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探索稳健和重复的药物遗传学关联，以建立功能变体和药物遗传学机制。功能性方法将涉及生物信息学工具、mRNA谱、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对遗传关联（包括上位性和基因-药物相互作用）的高分辨率分析，开发和验证一套对短效B2肾上腺素能和吸入性糖皮质激素药物的哮喘治疗反应的预测测试。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份DNA样本和匹配的哮喘表型数据可用于复制测试，以验证最初的GWAS结果和预测性测试。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预测哮喘药物反应的预后测试工作模型将产生。相关性：据估计，哮喘影响着全世界3亿人，在美国每年的直接医疗保健费用中约占200亿美元，其中最大比例的费用分配给药物和住院费用。遗传变异的鉴定可用作预后测试的基础，以预测哪些个体将或将不响应治疗，从而最小化试错药物分配的需要和由于治疗不充分而住院的风险，具有显著降低与哮喘相关的发病率和经济负担的潜力。

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.