Genetics and Gene Expression Profiling in Asthma

描述（由申请人提供）：哮喘影响美国超过1700万人。近一半的患者对目前最有效的哮喘治疗药物治疗无反应。为了开发新的治疗方法，需要一个全面的可修饰的遗传靶点及其代谢途径的目录，这些靶点有助于哮喘的发展和进展。包括表达微阵列和高通量基因分型平台在内的基因组技术为推进这一进程提供了前所未有的机会。该项目的首要前提是将基因表达数据与群体遗传学相结合，将导致识别有助于哮喘发展和进展的关键分子。我们将从来自370名参与儿童哮喘管理项目延续研究（CAMP CS2）的不同严重程度哮喘年轻成人的外周血CD 4+淋巴细胞的总RNA中生成全基因组基因表达谱，以鉴定与哮喘严重程度表型相关的差异表达基因转录本。使用这些受试者及其父母的DNA样本，我们将对映射到200个最差异表达基因的单核苷酸多态性（SNP）进行基因分型，并进行基于家族的基因型-基因表达关联分析，以确定强烈调节基因表达的调节性SNP（rSNP）。具有最强rSNP调节证据的那些基因将被评估为哮喘候选基因，评估这些基因中的SNP是否与哮喘相关的临床表型相关。使用这种方法，我们预计，我们将确定至少20个基因的顺式作用的调控基因变异的显着证据，这些基因中的许多也将窝藏遗传变异，直接影响哮喘的易感性和严重程度。具有最强关联证据的基因将在其他基于家族的哮喘队列中评估复制关联的证据，并将作为SNP发现工作的一部分进行重新测序，以鉴定功能多态性。我们预计，通过将基因表达数据与群体遗传学相结合，该项目将确定具有影响哮喘自然史的遗传变异的新基因，从而确定可能的治疗靶向的理想哮喘候选基因。这些发现可能最终导致开发新的治疗方法和这种常见疾病的临床预后测试。

DESCRIPTION (provided by applicant): Asthma affects over 17 million people in the United States. Nearly one-half of patients do not respond to treatment with the most effective classes of currently available asthma therapeutics. In order to develop novel therapies, a comprehensive catalog of modifiable genetic targets and their metabolic pathways that contribute to the development and progression of asthma is needed. Genomic technologies including expression microarrays and high-throughput genotyping platforms offer an unprecedented opportunity to advance this process. The overarching premise of this project is that combining gene expression data with population genetics will lead to the identification of critical molecules that contribute to the development and progression of asthma. We will generate genome-wide gene expression profiles from total RNA derived from peripheral blood CD4+ lymphocytes from 370 young adults with asthma of varying severity participating in the Childhood Asthma Management Program Continuation Study (CAMP CS2) to identify differentially expressed gene transcripts that are associated with asthma severity phenotypes. Using DNA samples from these subjects and their parents, we will then genotype single nucleotide polymorphisms (SNPs) mapping to 200 of the most differentially expressed genes and perform family-based genotype-gene-expression association analysis to identify regulatory SNPs (rSNP) that strongly regulate gene expression. Those genes with the strongest evidence of rSNP regulation will be evaluated as asthma candidate genes evaluating whether SNPs in these genes are associated with asthma-related clinical phenotypes. Using this approach we anticipate that we will identify at least 20 genes with significant evidence of cis-acting regulatory genetic variation and that many of these genes will also harbor genetic variation that directly influence asthma susceptibility and severity. Genes with the strongest evidence of association will be evaluated in other family- based asthma cohorts for evidence of replicated association and will be resequenced as part of a SNP- discovery effort to identify functional polymorphisms. We anticipate that by combining gene expression data with population genetics, this project will identify novel genes that harbor genetic variation that influence the natural history of asthma, thereby identifying ideal asthma-candidate genes for possible therapeutic targeting. These findings could ultimately lead to the development of novel therapies and a clinical prognostic test for this common disease.