Resources
NEXUS: Network of Exposomics in the US
The NEXUS (Network of EXposomics in the United States) is a Center for Exposome Research Coordination (CERC). The center aims to serve the broad biomedical research community by orchestrating the advancement and promotion of exposome research. The center is supported by the National Institutes of Health under grant number U24ES036819.
The Confluence Projects
Analytics and data resources to study the geospatial exposome in exacerbating health outcomes of the elderly population.
Exposome-wide studies
Human Exposomic Architecture of the Proteome
HEAP (Human Exposomic Architecture of the Proteome) is a comprehensive tool designed to analyze the interactions between genetics, exposures, proteomics, and disease outcomes. It leverages data from the UK Biobank to provide insights into how various factors contribute to health and disease.
The Architecture of Physiological Phenotypes: Exposome-Phenome Atlas
How much variation do exposures explain in phenotype? This is an atlas of 127K correlations between 278 phenotypes (e.g., body mass index, glucose, height, creatinine) and 651 exposures and behaviors (e.g., self-reported nutrient intake; blood lead levels; urinary phthalates; blood PFOA) in healthy individuals of the United States.
Claims Analysis of Twins Correlation and Heritability
We estimate the relative contribution of genetics and shared exposome in 560 phenotypes in a large health insurance cohort analyzing data from ~60,000 twins and ~500,000 siblings.
Exposome Globe Browser
What is the “linkage disequilibrium” of the exposome? View how biomarkers of exposures are correlated with one another and disease-related phenotypes.
Databases
RepoDB
Developing new computational approach for predicting new drug repositioning candidates? Test your predictions with our standard database for drug repositioning.
MeshDD
MeSHDD uses MeSH-term enrichment to discover literature-based similarities between FDA approved drugs.
Software
Exposome Atlas Creation
Software to document exposome-phenome correlations and systematically assesses replicability across independent cohorts.
Human Exposomic Architecture of the Proteome
Software to develop associations between the exposome and proteome, considering GxE, mediation, and longitudinal outcomes.
Vibration of Effects
How do inferences change based on the parameters of statistical models? Estimate the distribution of association sizes, and p-values based on model selection, called the Vibration of Effects (VoE).
- Journal of Clinical Epidemiology 2016: Mortality
- PLOS Biology 2022 Microbiome
- PLOS Biology 2021: Data-driven for quantitative phenotypes (quantvoe)
Polyexposure Risk Scores (PXS) via PXSTools
PXStools provides an analytical package to standardize exposome-wide studies as well as derive and validate polyexposure risk scores in the UK Biobank. PXSTools
Course Materials
Course materials and starter code for exposome-phenome data analysis.
Other Resources
Machine Learning and Biological Age
Biological age is the deviation from chronological age and is hypothesized to be one causal factor for age-related disease. Biological age, however, is difficult to measure. Here, we analyzed 676,787 samples from 502,211 UK Biobank participants aged 37-82 years with deep learning artificial intelligence approaches to build a total of 331 biological age predictors on different data modalities (e.g., Magnetic Resonance Imaging)
HIV+ Exposure-wide study across Sub-Saharan Africa
Predisposition to become HIV positive (HIV + ) is influenced by a wide range of correlated economic, environmental, demographic, social, and behavioral factors. A data-driven approach to identify risk factors for HIV+ in across Sub-Saharan Africa in over 600,000 individuals.
Non-linear relationships between physiological indicators and all-cause mortality in the US
We document linear and non-linear relationships of 27 physiological indicators with all-cause mortality to evaluate whether the current clinical thresholds are suitable in distinguishing patients at high risk for mortality from those at low risk.
Data-driven characterization of exposome risk variables for type 2 diabetes in the Netherlands
We query for environmental and modifiable drivers of tpe 2 diabetes risk in the Lifelines Biobank Cohort.
Probing the space of COVID-19 non-pharmaceutical interventions
We use a“multiverse approach” to evaluate the role of non-pharmaceutical inventions deployed during the pandemic to assess what worked and what didn’t. TL;DR: not enough data.