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，它们的确切贡献 经常成为广泛辩论的主题。详细的环境风险因素往往无法获得，这使得 很难联合评估遗传和环境的贡献。然而，出现了大规模的全国性 生物库以及国际基因研究为弥补这一知识鸿沟提供了一个很好的机会。在……里面 特别是，由于研究参与者来自不同的位置，因此研究参与者的地理空间信息可以 被用作环境暴露的代用品。包含研究的地理空间信息的模型 参与者将提高关联分析的能力和更准确的遗传力估计。在这 应用，我们建议开发一种空间混合线性效应模型(SMILE)来改进关联 分析和遗传力估计及空间Meta分析回归检验(SMART) 遗传关联研究的荟萃分析。我们将把它们应用于英国生物银行，MarketScan保险账单 数据库、TOPMed序列数据和一项关于吸烟和饮酒的大型多种族GWA元分析 上瘾。为了达到预期的研究目标，我们组建了一支强大的研究团队，与之相辅相成 来自统计遗传学、成瘾遗传学、肺功能遗传学、生物医学信息学和 环境流行病学。通过这项研究开发的方法和工具将开辟新的途径 分析国家生物库，如英国生物库和我们所有人的队列，以及全球联盟的研究。这个 这项研究的结果将有助于阐明吸烟/饮酒成瘾的遗传结构和肺功能 在其他特征中。

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 a large multi-ethnic GWAS meta-analysis of smoking and drinking addictions. 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 smoking/drinking addiction and lung function among other traits.