--- title: "Atherosclerosis Risk in Communities (ARIC)" id: "1006" type: "cohort" slug: "atherosclerosis-risk-in-communities-aric" published_at: "2020-02-19T15:04:38+00:00" modified_at: "2024-09-13T19:57:03+00:00" url: "https://dss.niagads.org/cohorts/atherosclerosis-risk-in-communities-aric/" markdown_url: "https://dss.niagads.org/cohorts/atherosclerosis-risk-in-communities-aric.md" excerpt: "The Atherosclerosis Risk in Communities Study (ARIC), sponsored by the National Heart, Lung, and Blood Institute (NHLBI) is a prospective epidemiologic study conducted in four U.S. communities. ARIC is designed to investigate the causes of atherosclerosis and its clinical outcomes,..." taxonomy_cohort_categories: - "ADSP" taxonomy_cohort_countries: - "United States of America" --- Website: [https://sites.cscc.unc.edu/aric/desc_pub](https://sites.cscc.unc.edu/aric/desc_pub) ## Description The Atherosclerosis Risk in Communities Study (ARIC), sponsored by the National Heart, Lung, and Blood Institute (NHLBI) is a prospective epidemiologic study conducted in four U.S. communities. ARIC is designed to investigate the causes of atherosclerosis and its clinical outcomes, and variation in cardiovascular risk factors, medical care, and disease by race, gender, location, and date. To date, the ARIC study has published over 2,000 articles in peer-reviewed journals. ARIC includes two parts: the Cohort Component and the Community Surveillance Component. The Cohort Component began in 1987, and each ARIC field center randomly selected and recruited a cohort sample of approximately 4,000 individuals aged 45-64 from a defined population in their community, to receive extensive examinations, including medical, social, and demographic data. Follow-up also occurs semi-annually, by telephone, to maintain contact and to assess health status of the cohort. In the Community Surveillance Component, the four communities are investigated to determine the long term trends in hospitalized myocardial infarction (MI) and coronary heart disease (CHD) deaths in approximately 470,000 men and women aged 35-84 years. ## Related Datasets - [NG00067 – ADSP Umbrella](https://dss.niagads.org/datasets/ng00067/) This dataset includes sequencing data and harmonized phenotypes from cohorts sequenced by the Alzheimer’s Disease Sequencing Project and other AD and Related Dementia’s studies. Samples are processed using a common… [Learn more](https://dss.niagads.org/datasets/ng00067/) - [NG00116 – Resolving Mutations in Challenging Genomic Regions to Test Association with Disease Phenotypes](https://dss.niagads.org/datasets/ng00116/) Many regions of the human genome present challenges that prohibit scientists from discovering potential disease-causing mutations. We developed methods to characterize mutations in these regions to rescue mutations that are… [Learn more](https://dss.niagads.org/datasets/ng00116/) - [NG00176-CNVs from ADSP WES data using CANOES software](https://dss.niagads.org/datasets/ng00176/) This dataset contains Copy Number Variation (CNV) calling from the Whole Exome Sequencing (WES) from multiple distinct Alzheimer Disease (AD) sequencing projects: both discovery and replication ADSP family dataset, ADSP… [Learn more](https://dss.niagads.org/datasets/ng00176/) ## Related Studies - [sa000001 - Alzheimer’s Disease Sequencing Project (ADSP)](https://dss.niagads.org/studies/sa000001/) Background An initiative in response to the National Alzheimer’s Project Act (NAPA) has been working towards new biological insights and cures for Alzheimer’s Disease (AD) since its introduction by NIH… [Learn more](https://dss.niagads.org/studies/sa000001/) - [sa000081 - Extremely Rare CNVs and Alzheimer’s Disease Risk: Analysis of ADSP WES Data](https://dss.niagads.org/studies/sa000081/) The purpose of this study is to find new Alzheimer related variants and genes, by combining exome data from healthy controls and Alzheimer patients from different studies. CNV calling was… [Learn more](https://dss.niagads.org/studies/sa000081/) - [sa000042 - Resolving mutations in challenging genomic regions to test association with disease phenotypes](https://dss.niagads.org/studies/sa000042/) Many regions of the human genome present challenges that prohibit scientists from discovering potential disease causing mutations. We developed methods to characterize mutations in these regions to rescue mutations that… [Learn more](https://dss.niagads.org/studies/sa000042/) ## Related Sample Sets - [snd10000 - ADSP Discovery](https://dss.niagads.org/sample-sets/snd10000/) The initial phase of the ADSP research plan is called the Discovery Phase. Samples were selected from well-characterized study cohorts of individuals with or without an AD diagnosis and the… [Learn more](https://dss.niagads.org/sample-sets/snd10000/) - [snd10074 - Camouflaged Variants](https://dss.niagads.org/sample-sets/snd10074/) Provided here are variant calls in VCF format for 14,526 samples derived from the ADSP whole-exome and whole-genome sequencing dataset (available via DSS: NG00067). [Learn more](https://dss.niagads.org/sample-sets/snd10074/) - [snd10139 - CNV Calling from ADSP Whole-Exome Sequencing (WES) Data](https://dss.niagads.org/sample-sets/snd10139/) This dataset comprises CNV calls from Whole Exome Sequencing (WES) across multiple distinct Alzheimer’s Disease (AD) sequencing projects, including the discovery and replication ADSP family datasets, the ADSP case-control dataset,… [Learn more](https://dss.niagads.org/sample-sets/snd10139/) ```