High-Dimensional Mediation Analysis: A New Method Applied to Maternal Smoking, Placental DNA Methylation, and Birth Outcomes.


Journal

Environmental health perspectives
ISSN: 1552-9924
Titre abrégé: Environ Health Perspect
Pays: United States
ID NLM: 0330411

Informations de publication

Date de publication:
04 2023
Historique:
medline: 18 4 2023
entrez: 14 4 2023
pubmed: 15 4 2023
Statut: ppublish

Résumé

High-dimensional mediation analysis is an extension of unidimensional mediation analysis that includes multiple mediators, and increasingly it is being used to evaluate the indirect omics-layer effects of environmental exposures on health outcomes. Analyses involving high-dimensional mediators raise several statistical issues. Although many methods have recently been developed, no consensus has been reached about the optimal combination of approaches to high-dimensional mediation analyses. We developed and validated a method for high-dimensional mediation analysis (HDMAX2) and applied it to evaluate the causal role of placental DNA methylation in the pathway between exposure to maternal smoking (MS) during pregnancy and gestational age (GA) and birth weight of the baby at birth. HDMAX2 combines latent factor regression models for epigenome-wide association studies with HDMAX2 demonstrated increased power in comparison with state-of-the-art multidimensional mediation methods and identified several AMRs not identified in previous mediation analyses of exposure to MS on birth weight and GA. The results provided evidence for a polygenic architecture of the mediation pathway with a posterior estimate of the overall indirect effect of CpGs and AMRs equal to HDMAX2 outperformed existing approaches and revealed an unsuspected complexity of the potential causal relationships between exposure to MS and birth weight at the epigenome-wide level. HDMAX2 is applicable to a wide range of tissues and omic layers. https://doi.org/10.1289/EHP11559.

Sections du résumé

BACKGROUND
High-dimensional mediation analysis is an extension of unidimensional mediation analysis that includes multiple mediators, and increasingly it is being used to evaluate the indirect omics-layer effects of environmental exposures on health outcomes. Analyses involving high-dimensional mediators raise several statistical issues. Although many methods have recently been developed, no consensus has been reached about the optimal combination of approaches to high-dimensional mediation analyses.
OBJECTIVES
We developed and validated a method for high-dimensional mediation analysis (HDMAX2) and applied it to evaluate the causal role of placental DNA methylation in the pathway between exposure to maternal smoking (MS) during pregnancy and gestational age (GA) and birth weight of the baby at birth.
METHODS
HDMAX2 combines latent factor regression models for epigenome-wide association studies with
RESULTS
HDMAX2 demonstrated increased power in comparison with state-of-the-art multidimensional mediation methods and identified several AMRs not identified in previous mediation analyses of exposure to MS on birth weight and GA. The results provided evidence for a polygenic architecture of the mediation pathway with a posterior estimate of the overall indirect effect of CpGs and AMRs equal to
DISCUSSION
HDMAX2 outperformed existing approaches and revealed an unsuspected complexity of the potential causal relationships between exposure to MS and birth weight at the epigenome-wide level. HDMAX2 is applicable to a wide range of tissues and omic layers. https://doi.org/10.1289/EHP11559.

Identifiants

pubmed: 37058433
doi: 10.1289/EHP11559
pmc: PMC10104171
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

47011

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Auteurs

Basile Jumentier (B)

Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, TIMC CNRS UMR 5525, Grenoble, France.

Claire-Cécile Barrot (CC)

Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, TIMC CNRS UMR 5525, Grenoble, France.

Maxime Estavoyer (M)

Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, TIMC CNRS UMR 5525, Grenoble, France.

Jorg Tost (J)

Laboratory for Epigenetics and Environment, Centre National de Recherche en Genomique Humaine, CEA - Institut de Biologie François Jacob, University Paris Saclay, Evry, France.

Barbara Heude (B)

Université Paris Cité et Université Sorbonne Paris Nord, Inserm, INRAE, Centre de Recherche en Épidémiologie et StatistiqueS (CRESS), F-75004 Paris, France.

Olivier François (O)

Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, TIMC CNRS UMR 5525, Grenoble, France.
Inria Grenoble - Rhône-Alpes Inovallée, Montbonnot, France.

Johanna Lepeule (J)

Université Grenoble-Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences, Grenoble, France.

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