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

ARIC 队列中痛风疾病基因的下一代医学重测序

• 批准号: 7853113
• 负责人: JAMES E. HIXSON
• 金额: $ 131.77万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7853113
• 关键词: Accounting; Adult; Affect; African American; Age; Aging; Alleles; Amino Acids; Architecture; Arthritis; Atherosclerosis; Bioinformatics; Candidate Disease Gene; Cardiovascular system; Caucasians; Caucasoid Race; Clinical; Communities; Complex; DNA Resequencing; DNA Sequence; Data; Deposition; Disease; Disease Progression; Drug Delivery Systems; Educational workshop; Elements; Epidemiology; Etiology; Exons; Framingham Heart Study; Gene Expression; Gene Frequency; Gene Targeting; Genes; Genetic; Genomics; Genotype; Gout; Health; Heart; Human; Human Genome; Individual; Intercistronic Region; Investigation; Joints; Laboratories; Lead; Learning; Measures; Medical; Medical Technology; Messenger RNA; Molecular; Nonsense Codon; Outpatients; Pain; Phenotype; Prevention; Process; Promoter Regions; Quality Control; RNA Splicing; Research; Research Personnel; Resources; Risk; Risk Factors; Serum; Solid; Susceptibility Gene; System; Technology; Testing; Tissues; United States National Institutes of Health; Urate; Uric Acid; Variant; Visit; case control; cohort; cost; data management; database of Genotypes and Phenotypes; design; follow-up; genetic variant; genome sequencing; genome wide association study; improved; interest; multidisciplinary; next generation; novel; programs; protein structure; public health relevance; success; tool; trait

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.

描述（由申请人提供）：该提案响应 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学重测序技术。在此应用中，我们建议使用下一代测序对基因进行医学重测序，这些基因在“基因组流行病学心脏和衰老研究队列（CHARGE）”的全基因组关联研究（GWAS）中显示出与痛风和血清尿酸水平高度显着的关联。重测序将通过鉴定构成 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“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，包括稳健且经济高效的亚基因组捕获流程，以丰富重测序的基因靶标，以及实施 SOLID System v.3（Applied Biosystems）以及用于数据管理和质量控制的相关管道。我们从 CHARGE GWAS 结果中选择了 11 个基因，用于对来自“社区动脉粥样硬化风险 (ARIC)”队列的痛风病例和对照（总共 n=1,199）的功能区（启动子、外显子、保守区）进行重新测序。重测序后，我们将对整个 ARIC 队列 (n=16,000) 中的变异进行基因分型，以验证重测序结果，并提高统计分析的功效。统计分析将包括相对常见等位基因的标准关联研究，以及通过测试病例与对照中罕见变异携带者数量差异对罕见变异进行分析，以及携带者与整体队列中平均尿酸浓度的比较。对于表现出显着关联的常见和罕见变异，我们将使用生物信息学来识别可能的功能后果，例如非保守氨基酸替换和过早终止密码子、正常 mRNA 剪接的破坏或调节基因表达的控制元件的改变。我们建议通过基因分型和统计分析在另外两个 CHARGE 队列中复制我们的发现，包括“弗雷明汉健康研究 (FHS)”和“心血管健康研究 (CHS)”。 公共健康相关性：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，痛风是最常见的关节炎形式之一，影响着美国近 300 万成年人。我们的受试者来自“社区动脉粥样硬化风险 (ARIC)”研究，这是一个大型多种族流行病学队列（16,000 名受试者），已测量多种疾病相关风险因素和临床终点。遗传变异的鉴定将有助于更好地理解调节血清尿酸水平（痛风的主要危险因素）的分子机制，并最终产生新的药物靶点来改善痛风的治疗。