Discovery of Gene Expression Signatures Using Machine Learning: Social Isolation and Genetic Expression in Adolescence and Young Adulthood

Citation

Levitt, Brandt; Gaydosh, Lauren; Shanahan, Mike; Cole, Steve; & Harris, Kathleen M. (2019). Discovery of Gene Expression Signatures Using Machine Learning: Social Isolation and Genetic Expression in Adolescence and Young Adulthood. Population Association of America annual meeting. Austin, TX.

Abstract

A large literature has identified social isolation as an important psychosocial determinant of health, but the biological mechanisms that explain this connection are relatively unknown. We address this gap using genome-wide transcriptome data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine whether social isolation operates through gene expression of innate and adaptive immune responses within the stress process system. We expand upon previous work that identified conserved immunological genes as important mediators of poor health outcomes in socially isolated individuals. We construct a quantitative measure of social isolation across multiple contexts. We use regression models and machine learning algorithms to develop a gene expression signature correlated with social isolation and seek to better understand this connection by analyzing genetic regulatory features and immunological cell subsets to identify causal patterns that explain the biological processes that make social isolation a risk factor for poor health.

URL

http://paa2019.populationassociation.org/abstracts/193059

Reference Type

Conference proceeding

Book Title

Population Association of America annual meeting

Author(s)

Levitt, Brandt
Gaydosh, Lauren
Shanahan, Mike
Cole, Steve
Harris, Kathleen M.

Year Published

2019

City of Publication

Austin, TX

Reference ID

7308