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. 2022 May;130(5):55001.
doi: 10.1289/EHP9098. Epub 2022 May 9.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Christy L Avery  1   2 , Annie Green Howard  3   2 , Anna F Ballou  1 , Victoria L Buchanan  1 , Jason M Collins  1 , Carolina G Downie  1 , Stephanie M Engel  1 , Mariaelisa Graff  1 , Heather M Highland  1 , Moa P Lee  1 , Adam G Lilly  2   4 , Kun Lu  5 , Julia E Rager  5 , Brooke S Staley  1 , Kari E North  1 , Penny Gordon-Larsen  6   2
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Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Christy L Avery et al. Environ Health Perspect. 2022 May.

Abstract

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

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Figures

Figure 1 is a flowchart titled Exposome with four steps. Step 1: The natural and built environment, including buildings, rivers, forests, and factories. Step 2: Consider the social environment, which includes education, currency, and population. Step 3: The chemical environment, which includes medical, chemical, and radioactive elements. Step 4: Health behaviors, including sleeping, eating, alcohol consumption, and exercise routine. All the steps are interrelated with genetics. An icon of genetics appears in the middle of the figure, with arrows pointing to all four steps.
Figure 1.
Conceptual diagram of the exposome. By placing genetic data in the middle of four exposome domains (the natural and built environment, the social environment, the chemical environment, and health behaviors), the central role of genetic data is emphasized. Figure adapted from Vermeulen R, Schymanski EL, Barabasi AL, Miller GW. 2020. The exposome and health: Where chemistry meets biology. Science 367:392–396. Reprinted with permission from American Association for the Advancement of Science (AAAS).
Figure 2 is a flowchart with two steps. Step 1: Genetic variants uppercase italic g with established causal relationship lead to exposure uppercase italic x. Step 2: Exposure uppercase x with unmeasured confounding and hypothesis tested lead to outcome uppercase italic y.
Figure 2.
Example causal diagram representing the relationship between genetic variants G, exposure X, and outcome Y. The hypothesis tested by Mendelian randomization is shown by the dotted arrow where G serves as an instrumental variable for X (solid arrow).
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