Integrative genomic and geospatial analysis of insurance claim, biobank and GWAS summary statistics for complex traits

保险索赔的综合基因组和地理空间分析、生物库和复杂性状的 GWAS 汇总统计

• 批准号: 10595104
• 负责人: Bibo Jiang
• 金额: $ 28.8万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-20 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595104
• 关键词: Air Pollution; Algorithms; Biology; Characteristics; Code; Communities; Complex; Computer software; Data; Data Aggregation; Data Set; Databases; Diabetes Mellitus; Disease; Environment; Environmental Epidemiology; Environmental Exposure; Environmental Risk Factor; Environmental Wind; Etiology; Exposure to; Family; Gene Frequency; Genetic; Genetic Models; Genetic study; Genome Components; Genomics; Heritability; Heterogeneity; Human; Individual; Insurance; International; Joints; Knowledge; Licensing; Lipids; Location; Long-Term Effects; Meta-Analysis; Methods; Modeling; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Nuclear Family; Participant; Patient Self-Report; Phenotype; Plasma; Pollution; Population Control; Privacy; Privatization; Proxy; Public Health; Regression Analysis; Research; Resource Sharing; Sampling; Smoking; Speed; Statistical Methods; Structure; Testing; Therapeutic Intervention; Trans-Omics for Precision Medicine; Twin Multiple Birth; Work; addiction; biobank; biomedical informatics; cohort; design; drinking; gene environment interaction; genetic architecture; genetic association; genetic risk factor; genome wide association study; genome-wide; human disease; improved; instrument; insurance claims; multi-ethnic; novel; novel therapeutics; phenome; pulmonary function; risk prediction; sociodemographics; statistics; therapeutically effective; tool; trait; whole genome

ABSTRACT Human complex traits are jointly influenced by genetic and environmental risk factors, whose exact contributions are often subject to extensive debate. Detailed environmental risk factors are not often available, which makes it hard to jointly assess the genetic and environmental contributions. Yet, the emergence of large- scale national biobanks as well international genetic studies offers a great opportunity to make up for this knowledge gap. In particular, as study participants come from diverse locations, geospatial information of the study participants can be used as a proxy for environmental exposure. Models that incorporate geospatial information of study participants will lead to improved power for association analysis and more accurate heritability estimates. In this application, we propose to develop a Spatial MIxed Linear Effect model (SMILE) for improved association analysis and heritability estimation and Spatial Meta-Analysis Regression Test (SMART) for more powerful meta-analyses of genetic association studies. We will apply them to UK Biobank, MarketScan insurance billing database, TOPMed sequence data, and various large consortia studies on smoking/drinking addictions, lipids levels, and diabetes. To achieve the proposed research aims, we assembled a strong research team with complementary expertise from statistical genetics, addiction genetics, lung function genetics, biomedical informatics, and environmental epidemiology. Methods and tools developed from this study will open up new avenues for analyzing national biobanks such as UK Biobank and All of Us cohorts, and global consortium studies. The results from this study will help elucidate the genetic architecture of complex traits with significant

摘要 人类复杂的特征受到遗传和环境风险因素的共同影响，其确切的 捐款往往引起广泛的辩论。详细的环境风险因素通常无法获得， 这使得很难共同评估遗传和环境的贡献。然而，大规模的 大规模的国家生物库以及国际遗传研究提供了一个很好的机会来弥补这一点 知识差距。特别是，由于研究参与者来自不同地点，因此地理空间信息 研究参与者可用作环境暴露的代表。纳入地理空间的模型 研究参与者的信息将提高关联分析的能力， 遗传性估计。在这个应用中，我们提出了一个空间混合线性效应模型（SMILE） 用于改进关联分析和遗传力估计以及空间元分析回归检验 （SMART）进行更强大的遗传关联研究荟萃分析。我们将把它们应用到英国生物银行， MarketScan保险账单数据库，TOPMed序列数据，以及各种大型财团对 吸烟/饮酒成瘾、血脂水平和糖尿病。为了达到研究目的，我们组织了 一个强大的研究团队，拥有统计遗传学，成瘾遗传学，肺功能等互补专业知识 遗传学、生物医学信息学和环境流行病学。由此开发的方法和工具 这项研究将为分析国家生物库开辟新的途径，如英国生物库和我们所有人的队列， 全球联合研究。这项研究的结果将有助于阐明复杂的遗传结构 具有显著