EU and the Seventh Framework Programme (the MeDALL project).

Background:Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes.Objectives:We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter <10 (PM10) or <2.5μm (PM2.5) and DNA methylation in newborns and children.Methods:We meta-analyzed associations between exposure to PM10 (n=1,949) and PM2.5 (n=1,551) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression.Results:Six CpGs were significantly associated [false discovery rate (FDR) <0.05] with prenatal PM10 and 14 with PM2.5 exposure. Two of the PM10-related CpGs mapped to FAM13A (cg00905156) and NOTCH4 (cg06849931) previously associated with lung function and asthma. Although these associations did not replicate in the smaller newborn sample, both CpGs were significant (p<0.05) in 7- to 9-y-olds. For cg06849931, however, the direction of the association was inconsistent. Concurrent PM10 exposure was associated with a significantly higher NOTCH4 expression at age 16 y. We also identified several DMRs associated with either prenatal PM10 and or PM2.5 exposure, of which two PM10-related DMRs, including H19 and MARCH11, replicated in newborns.Conclusions:Several differentially methylated CpGs and DMRs associated with prenatal PM exposure were identified in newborns, with annotation to genes previously implicated in lung-related outcomes. https://doi.org/10.1289/EHP4522

BackgroundMultilevel data integration is becoming a major area of research in systems biology. Within this area, multi-‘omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-‘omics signatures of disease states.MethodsThe framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification.ResultsWe illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-‘omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.ConclusionsThis framework will help health researchers plan and perform multi-‘omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0556-z) contains supplementary material, which is available to authorized users.