A Multi-Ancestry Study of Gene-Lifestyle Interactions and Multi-Omics in Cardiometabolic Traits

项目总结/摘要 心脏代谢疾病和危险因素，包括高血压、血脂异常、肥胖和 2 型 糖尿病是一项重大的公共卫生负担，对不同的（非欧洲国家）产生不成比例的影响 祖先）人群。心脏代谢特征受遗传和环境（生活方式）的影响 因素。因此，了解这些因素之间的相互作用 (GxE) 可以深入了解 减轻疾病负担的干预、预防和治疗策略。许多全基因组 关联研究（GWAS）通过以下方式对许多复杂性状做出了重要的遗传发现： 基于“遗传主效应”的方法。然而，全基因组相互作用研究（GWIS）仍然 有限。因此，本研究的目的是确定与心脏代谢相关的新遗传位点 通过大样本中的 GWIS 分析性状，研究心脏代谢性状基因座中的基因与生活方式的相互作用， 并通过利用现有的“组学”数据来表征相互作用背后的分子效应，例如 DNA 甲基化、基因表达和代谢物。通过调查基因组和生活方式的影响因素 我们的建议是通过不同人群和性别之间的相互作用来影响健康结果 反映了精准医学计划 (PMI) 的优先事项，其重点是两者之间的相互作用 生活方式/环境和遗传学，重点是不同人群。 我们建议评估不同人群（欧洲人、非洲人、西班牙人、 和亚洲）。我们最近在血压、血脂和二分生活方式因素（吸烟、 饮酒、体力活动、教育程度、社会心理因素和睡眠） 有限的统计能力。因此，我们建议大幅增加 GWIS 的功率，使其增大约 10 倍 样本量并扩大我们的关注范围以包括其他心脏代谢特征（肥胖和糖尿病特征）， 定量的生活方式暴露（例如，每天吸烟），以及总体生活方式风险评分 生活方式对心脏代谢健康的危险因素；评估已确定的相互作用对临床终点的影响 （如冠心病）；评估已确定的相互作用的成药性；并描述相互作用的特征 使用多个组学数据。我们的提案包括我们最近的发现研究（N~130,000）、我们最近的 复制研究（N~252,000），以及增加了几项新的、规模更大的研究，包括 百万退伍军人计划（N~234,000）、英国生物银行（N~478,000）、日本生物银行（N~160,000）和 对拉丁裔（N~13,000）等的研究。总体样本包括 912,000 名欧洲人、91,000 名非洲人、 33,000 名西班牙裔和 231,000 名亚裔人士代表了迄今为止最重大的努力 调查总样本量约为 126.7 万的交互作用。研究结果可能会带来新的结果 诊断和治疗工具，有助于心脏代谢疾病管理的精准护理， 并为 PMI 提供见解，最终目标是加强临床实践。

PROJECT SUMMAR/ABSTRACT Cardiometabolic diseases and risk factors, including hypertension, dyslipidemia, adiposity, and type 2 diabetes, represent a major public health burden that disproportionately affects diverse (non-European ancestry) populations. Cardiometabolic traits are influenced by both genetic and environmental (lifestyle) factors. Therefore, understanding interactions (GxE) between these factors could provide insights into intervention, prevention and therapeutic strategies to reduce the burden of disease. Many genome-wide association studies (GWAS) have made important genetic discoveries for many complex traits through approaches based on “genetic main effects”. However, genome-wide interaction studies (GWIS) are still limited. The objective of this study is therefore to identify novel genetic loci associated with cardiometabolic traits through GWIS in large samples, to investigate gene-lifestyle interactions in the cardiometabolic trait loci, and to characterize the molecular effects underlying the interactions by leveraging existing “Omics” data such as DNA methylation, gene expression, and metabolites. By investigating genomic and lifestyle contributors to health outcomes through their interactions across diverse populations and between sexes, our proposal reflects the priorities of the Precision Medicine Initiative (PMI) whose focus is the interplay between lifestyle/environment and genetics, with an emphasis on diverse populations. We propose to evaluate gene-lifestyle interactions across diverse populations (European, African, Hispanic, and Asian). Our recent progress with blood pressure, lipids, and dichotomized lifestyle factors (smoking, alcohol consumption, physical activity, educational attainment, psychosocial factors, and sleep) encountered limited statistical power. Therefore, we propose to vastly increase the power for GWIS with an ~10-fold larger sample size and expand our focus to include other cardiometabolic traits (adiposity and diabetes traits), quantitative lifestyle exposures (e.g., cigarettes per day), and aggregate Lifestyle Risk Scores as an overall lifestyle risk factor for cardiometabolic health; evaluate the impact of identified interactions on clinical endpoints (like coronary heart disease); assess the druggability of identified interactions; and characterize interactions using multiple omics data. Our proposal includes our recent discovery studies (N~130,000), our recent replication studies (N~252,000), and the addition of several new, substantially larger studies including the Million Veteran Program (N~234,000), the UK Biobank (N~478,000), the Biobank Japan (N~160,000), and the Study of Latinos (N~13,000), among others. The overall sample, including 912,000 European, 91,000 African, 33,000 Hispanic, and 231,000 Asian ancestry individuals, represents the most significant effort to date to investigate interactions with an aggregate sample size of about 1.267 million. Findings can lead to new diagnostic and therapeutic tools, contribute to precision care in the management of cardiometabolic disorders, and provide insights for the PMI with the ultimate goal of enhancing clinical practice.