Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan.

ABSTRACT: Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM_2.5), nitrogen dioxide (NO₂), climate, and sociodem

SUBMITTER: McGuire D 

PROVIDER: S-EPMC11199552 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature