The Personalized Environment and Genes Study

National Institute Of Environmental Health Sciences

Investigators

Linked publications, trials & patents

Abstract

As co-PI of the Personalized Environment and Genes Study, I direct many scientific projects using the data. The PEGS data includes whole genome sequencing, extensive health and exposure questionnaires, GIS estimates of exposure based on residential histories, and very recently epigenetic data. This rich resource has allowed for several recently published projects. The summaries of these published projects are listed below. Epigenetic Clock Predictions The PEGS cohort study has generated comprehensive epigenetic data from 4,260 blood samples using Illumina 850k Methylation EPIC chips. Using this data we have applied over 150 previously published prediction models across three categories: age clocks (20 models that predict chronological or biological age to assess aging quality), health predictions (29 models estimating traits like BMI and smoking status), and protein levels (109 models predicting circulating proteins based on methylation's role in gene expression regulation). We are now leveraging the cohort's detailed questionnaire and GIS location data to investigate how methylation predictions correlate with actual health measures, identifying which diseases most strongly associate with poor aging, determine which environmental exposures mediate these relationships, and assess geographic factors as potential contributors to healthy aging outcomes. EAA and T2D Using the PEGS cohort, we are investigating the relationship between Epigenetic Age Acceleration (EAA), the difference between methylation-predicted age and chronological age, and Type 2 Diabetes (T2D). Our investigation revealed that T2D shows the strongest association with accelerated biological aging among all tested diseases. Using nearly 50 epigenetic clocks we showed that 14 out of 20 biological age and health clocks have statistically significant positive correlations with a T2D diagnosis (p < 0.05), with this relationship persisting across all age quartiles even when controlling for age, sex, BMI, income, ancestry, and methylation batch. The findings suggest that individuals with T2D exhibit faster epigenetic aging compared to healthy controls, and this association is evident even in younger individuals, indicating that the connection between T2D and accelerated biological aging is not simply due to the higher prevalence of diabetes in older populations, opening avenues for future research into specific environmental exposures that may mediate this relationship. Gene Environment Correlation Using whole genome sequencing data from nearly 5,000 participants in the PEGS cohort, we conducted genome-wide association studies (GWAS) to investigate previously unexplored gene-environment correlations beyond addiction-related behaviors, finding genome-wide significant associations for 26 of 81 environmental exposures analyzed. Notable discoveries included a colocalized genetic signal in the NEDD4L gene associated with workplace exposure to both cleaning liquids and alcohol-based cleaners (with 93% posterior probability of colocalization), which was validated in an independent dataset (NIEHS Sister Study), and multiple brain-related genes (RBFOX1, NEDD4L, CSMD1) showing associations across multiple exposures. The study revealed that exposure-associated genetic variants were significantly enriched in neuronal system and developmental biology pathways (as determined through Reactome ontology analysis) and in gene promoter regions, suggesting that genetic factors influencing exposure likelihood operate through neuronal and developmental processes, thereby advancing understanding of how genetics shape environmental exposure patterns and ultimately health outcomes. DREAM Challenge Farida Akhtari's contributions were focused on key programmatic initiatives, including the successful completion of the PEGS DREAM Challenge and robust PEGS data management activities. She played a significant role in the PEGS DREAM Challenge, which was completed successfully. Her work included scoring submissions, presenting the work at the RSG DREAM 2024 Conference, and finalizing the manuscript with collaborators. Concurrently, Ms. Akhtari managed substantial PEGS data operations, such as creating data use agreements (DUAs), facilitating data sharing with numerous collaborators, and performing data queries and reviews. She is leading the effort with the DLH data management team for creating the PEGS Data Freeze version 4. She also worked on multiple manuscripts including the PEGS Data Resource manuscript, Variance decomposition analysis manuscript and reviewed several manuscripts that used the PEGS data for analysis. Furthermore, her efforts extended to providing research guidance for various analyses, including ExWAS, polyexposure scores, and methylation. She also led the initiative for NIH Security Standards for the PEGS CADR and presented and contributed to the BCBB BSC review.