This project will conduct genetic analysis on 10,000 participants in the Wisconsin Longitudinal Study (WLS), a highly productive 53-year long study of the life course of Wisconsin high school graduates of 1957 and their randomly selected siblings. WLS data cover cognition;social and economic background;educational, career, and family histories;earnings, income, and wealth;psychological and health measures;and multiple causes of death. We expect 10,000 samples: 5223 graduates and 2924 sibling samples currently in hand and~1900 currently being collected. There will be more than 2500 sibling pairs. Phenotypic data from the WLS have long been public, with appropriate protections of the privacy and confidentiality of participants. Similarly, we plan to share rich genomic data, which will be obtained using the Illumina HumanOmniExpress beadchip. Researchers, via varying mechanisms, will have access to all of the genetic and phenotypic data WLS has to offer. Further, our procedures and mechanisms to access these data are designed to reduce barriers to access and use, while still ensuring protections for our research participants. Adding genetic data to the WLS will help to create a consortium of large longitudinal studies of aging with harmonized phenotypic and extensive genomic data, e.g., combining the Wisconsin Longitudinal Study, the Health and Retirement Study, and other studies. The project will create a platform for discovery at the frontier of biosocial research.
This project will conduct genetic analysis on 10,000 individual participants in a lifelong study of Wisconsin high school graduates of 1957 and their randomly selected brothers and sisters. It will create sustainable mechanisms by which qualified researchers can access the genetic and phenotypic data within dbGaP and directly via the Wisconsin Longitudinal Study (WLS) for scientific analysis of health and behavior across the life course. It will help to create a consortium of large longitudinal studies of aging in which there wll have harmonized phenotypic and extensive genomic data, e.g., a combination of the WLS with the Health and Retirement Study and other longitudinal studies of aging.