Next-Generation Medical Resequencing of Gout Disease Genes in the ARIC Cohort

描述(由申请人提供)：本提案响应GO计划“ARRA医学测序发现项目”，旨在与基因组测序中心等大型设施相比，在较小的学术实验室中建立下一代医疗重测序技术。在这一应用中，我们建议使用下一代测序技术对基因进行医学重新测序，这些基因已经在基因组范围的关联研究(GWAS)中显示出与痛风和血清尿酸水平高度显著的相关性，该研究发表在《基因组流行病学心脏和衰老研究队列》(CHARE)中。重新测序将通过识别构成GWAs统计关联的常见功能变异以及具有较大表型效应的罕见变异来表征整体的“遗传结构”。我们的实验室已经建立了下一代测序的关键要素，包括强大和具有成本效益的亚基因组捕获过程，以丰富用于重新测序的基因靶标，以及实施Solid System v.3(应用生物系统)和用于数据管理和质量控制的相关管道。我们从Charge Gwas的结果中选择了11个基因，用于痛风患者和对照组(总共n=1,199)的功能区域(启动子、外显子、保守区)的重新测序。重新测序后，我们将对整个ARIC队列(n=16,000)中的变异进行基因分型，以验证重新测序的结果，并增加统计分析的能力。统计分析将包括对相对常见的等位基因的标准关联研究，以及通过测试病例和对照中罕见变异携带者数量的差异来分析罕见变异，以及比较携带者和总体队列中的平均尿酸浓度。对于显示出显著相关性的常见和罕见变体，我们将使用生物信息学来确定可能的功能后果，如非保守的氨基酸替换和过早停止密码子、正常mRNA剪接的中断，或调控基因表达的控制元件的变化。我们建议通过在另外两个费用队列中进行基因分型和统计分析来复制我们的发现，这些费用队列包括“弗雷明翰健康研究(FHS)”和“心血管健康研究(CHS)”。 与公共卫生相关：我们建议使用下一代DNA测序技术来识别影响痛风的基因变异，痛风是影响美国近300万成年人的最常见的关节炎形式之一。我们的受试者来自“社区动脉粥样硬化风险(ARIC)”研究，这是一个大型多民族流行病学队列(16,000名受试者)，已对多种疾病相关风险因素和临床终点进行了测量。基因变异的识别将提供对调节血清尿酸水平(痛风的主要危险因素)的分子机制的更好的理解，并最终导致新的药物靶点来改善痛风的治疗。

DESCRIPTION (provided by applicant): This proposal responds to the GO Program "ARRA Medical Sequencing Discovery Projects" to establish next-generation technologies for medical resequencing in smaller academic laboratories compared to larger facilities like the Genomic Sequencing Centers. In this application, we propose to use next-generation sequencing for medical resequencing of genes that have shown highly significant associations with gout and serum uric acid levels in genome-wide association studies (GWAS) in the "Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)". Resequencing will characterize the overall "genetic architecture" by identification of both common functional variants that underlie GWAS statistical associations, as well as rare variants with larger phenotypic effects. Our laboratories have established key elements of next-generation sequencing including a robust and cost-effective process for subgenomic capture to enrich gene targets for resequencing, as well as implementation of the SOLID System v.3 (Applied Biosystems) and associated pipelines for data management and quality control. We have selected 11 genes from CHARGE GWAS results for resequencing of functional regions (promoters, exons, conserved regions) in gout cases and controls (total n=1,199) from the "Atherosclerosis Risk in Communities (ARIC)" cohort. After resequencing, we will genotype variants in the entire ARIC cohort (n=16,000) to verify resequencing results, and to increase power for statistical analysis. Statistical analyses will include standard association studies for relatively common alleles, as well as analyses of rare variants by tests for differences in numbers of rare variant carriers in cases versus controls, and comparisons of mean uric acid concentrations in carriers versus the overall cohort. For both common and rare variants that show significant associations, we will use bioinformatics to identify possible functional consequences like non-conservative amino acid replacements and premature stop codons, disruption of normal mRNA splicing, or alterations in control elements that regulate gene expression. We propose to replicate our findings by genotyping and statistical analysis in two additional CHARGE cohorts including the "Framingham Health Study (FHS)" and the "Cardiovascular Health Study (CHS)". PUBLIC HEALTH RELEVANCE: We propose to use next-generation DNA sequencing technologies to identify genetic variants that influence gout, one of the most common forms of arthritis affecting nearly 3 million adults in the US. Our subjects are from the "Atherosclerosis Risk in Community (ARIC)" study, a large multi-ethnic epidemiological cohort (16,000 subjects) that has been measured for multiple disease-related risk factors and clinical endpoints. The identification of genetic variants will provide an improved understanding of molecular mechanisms that regulate serum levels of uric acid (the major risk factor for gout), and eventually lead to novel drug targets to improve treatment of gout.