TASK ORDER 5R - PERSONALIZED ENVIRONMENT AND GENES STUDY (PEGS)

个性化环境和基因研究（PEGS）是一项长期项目，旨在收集来自北卡罗来纳州不同人群的健康，暴露，医疗和遗传数据。PEGS于2002年启动，以前称为环境多态性登记处（EPR），由美国国立卫生研究院（NIH）下属的国家环境健康科学研究所（NIEHS）赞助。共有19，672名参与者参加，约11，000人积极参与年度联系。参与者的招募正在进行中。 从2013年到2020年，三项调查用于收集队列中的表型和暴露数据。PEGS健康和暴露调查（N = 9，414）收集了一般人口统计学数据、家族病史、吸烟和饮酒等生活方式因素的信息以及职业暴露数据。从2017年开始，进行了NIEHS PEGS暴露组调查，以收集有关整个生命过程中内源性和外源性暴露的全面信息。A部分侧重于外部暴露，包括从童年到现在在工作和家庭中的化学品和环境暴露。B部分包括关于内部暴露的问题，包括药物和生活方式因素，如体力活动、压力、睡眠和饮食。 除了基于调查的暴露数据外，还收集了地址历史（包括最长寿的童年地址）。这些地址已被用来将参与者与越来越多的地理空间暴露估计数联系起来，包括空气污染以及与有毒物质释放和农业作业地点的距离。 除了收集广泛的接触数据外，还对4，737名参与者进行了全基因组测序（WGS），获得了最完整的接触数据。WGS数据使遗传学研究不仅能够评估单核苷酸变异，还能够评估WGS全面分析的其他变异，包括拷贝数和结构变异、端粒长度和高分辨率人类白细胞抗原（HLA）复合物变异。 正在与北卡罗来纳州查佩尔山大学和杜克大学共同努力，为PEGS参与者整合电子健康记录。这些努力共同代表了由电子病历和杜克系统电子病历（EMR）、暴露信息和WGS数据支持的医疗信息的前所未有的整合。国际努力，如日本的Tohoku Medical Megabank、丹麦的多个登记中心和加拿大的CanPath，在其队列中收集了大量数据。然而，在美国，不仅PEGS是独一无二的，而且PEGS参与者也是一个积极参与的群体。 总之，PEGS已经登记了19，672名北卡罗来纳州居民，并包括快速扩展的高维数据集，包括： · 对健康和接触调查、内部问题调查和外部问题调查的答复。 · 电子健康记录（EHR）和电子病历（EMR），包括国际疾病分类（ICD）信息。 · 全基因组测序数据。 · 地理信息系统数据。

The Personalized Environment and Genes Study (PEGS) is a long-term project to collect health, exposure, medical, and genetic data from a diverse group of people in North Carolina. Initiated in 2002, PEGS was formerly called the Environmental Polymorphisms Registry (EPR) and is sponsored by the National Institute of Environmental Health Sciences (NIEHS), which is part of the National Institutes of Health (NIH). A total of 19,672 participants are enrolled, and ~11,000 are actively engaged with annual contact. Enrollment of participants is ongoing. From 2013-2020, three surveys were used to collect phenotype and exposure data in the cohort. The PEGS Health and Exposure Survey (N = 9,414) collected data on general demographics, family medical history, information on lifestyle factors such as smoking and alcohol use, and data on occupational exposures. Beginning in 2017, the NIEHS PEGS Exposome Survey was administered to collect comprehensive information about endogenous and exogenous exposures throughout life. Part A is focused on external exposures, including chemical and environmental exposures at work and home from childhood to the present. Part B includes questions about internal exposures, including medications, and lifestyle factors such as physical activity, stress, sleep, and diet. In addition to the survey-based exposure data, address histories (included longest-lived childhood address) are collected. These addresses have been used to link participants with a growing list of geospatial exposure estimates, including air pollution and distance to toxic release and agricultural operation sites. In addition to the collection of extensive exposure data, whole genome sequencing (WGS) has been conducted for the 4,737 participants with the most complete exposure data. The WGS data enable genetic studies evaluating not only single nucleotide variants but also other variants comprehensively assayed by WGS, including copy number and structural variants, telomere length, and high-resolution human leukocyte antigen (HLA) complex variation. Efforts are ongoing with both the University of North Carolina at Chapel Hill (UNC) and Duke University to integrate electronic health records for PEGS participants. Together these efforts represent an unprecedented integration of medical information backed by UNC and Duke system electronic medical records (EMRs), exposure information and WGS data. International efforts such as the Tohoku Medical Megabank in Japan, multiple registries in Denmark, and CanPath in Canada have assembled extensive data within their cohorts. In the United States, however, not only is PEGS unique, but PEGS participants are also an active and engaged group. In summary, PEGS has registered 19,672 North Carolina residents and includes rapidly expanding sets of high-dimensional data that comprise: • Responses to health and exposure surveys, internal exposome surveys and external exposome surveys. • Electronic Health Records (EHRs) and Electronic Medical Records (EMRs), including International Classification of Diseases (ICD) information. • Whole genome sequencing data. • Geographic Information Systems (GIS) data.