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

摘要 人类的复杂性状受到遗传和环境风险因素的共同影响，其确切的 投稿经常受到广泛的辩论。详细的环境风险因素通常不是可用的， 这使得联合评估遗传和环境贡献变得困难。然而，大型-- 大规模的国家生物库以及国际基因研究提供了一个很好的机会来弥补这一点 知识鸿沟。特别是，由于研究参与者来自不同的地点， 研究参与者可以作为环境暴露的替代指标。包含地理空间的模型 研究参与者的信息将导致关联分析的能力提高和更准确 遗传力估计。在这个应用中，我们建议开发一个空间混合线性效应模型(SMILE) 改进的关联分析和遗传力估计及空间Meta分析回归检验 (SMART)用于更强大的遗传关联研究的荟萃分析。我们将把它们应用到英国生物库， MarketScan保险账单数据库、TOPMed序列数据和各种大型财团对 吸烟/饮酒成瘾、血脂水平和糖尿病。为了实现所提出的研究目标，我们集合了 一支强大的研究团队，拥有统计遗传学、成瘾遗传学、肺功能等方面的互补专业知识 遗传学、生物医学信息学和环境流行病学。在此基础上开发的方法和工具 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，以及 全球财团研究。这项研究的结果将有助于阐明复合体的遗传结构 具有显著意义的特征

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