Educational attainment polygenic scores, socioeconomic factors, and resting-state functional connectivity in children and adolescents - Hansen, Melissa, author; Merz, Emily, advisor; Thomas, Michael, committee member; Seger, Carol, committee member; Riggs, Nathan, committee member (Colorado State University. Libraries, 2024)
Socioeconomic factors, such as family income and parental education, have been associated with resting-state functional connectivity (rsFC) in networks responsible for executive function in children and adolescents. Yet, children's socioeconomic context interacts with the genetics they inherit from their parents, and few studies of socioeconomic context and rsFC in children have considered genetics. Polygenic scores for educational attainment (PGS-EA) derived from genome-wide association studies (GWAS) reflect genetic predisposition to educational attainment. Yet, no studies have examined the associations between PGS-EA and rsFC. The goal of this study was to investigate how socioeconomic factors and PGS-EA jointly predict rsFC in neural networks associated with executive function, including the central executive (CEN), dorsal attention (DAN), salience (SN), and default mode networks (DMN) in children and adolescents. Participants are typically-developing 3- to 21-year-olds (N = 245, 51% female) from the previously-collected Pediatric Imaging, Neurocognition, and Genetics (PING) study. PGS-EA were computed based on the EA3 GWAS of educational attainment. Resting- state fMRI data were acquired, and system-level rsFC was computed. Findings indicated that family income was inversely associated with rsFC in the SN, while PGS-EA was positively associated with rsFC in the CEN. There were family income-by-age interactions for rsFC in the CEN and DAN, such that age was positively associated with rsFC in the CEN and DAN for children from higher income families and inversely associated with rsFC in the CEN for children from lower income families. These findings help to elucidate the independent genetic and socioeconomic contributions to connectivity in intrinsic functional neural networks underlying executive function.