Variability of multi-omics profiles in a population-based child cohort

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• dc.contributor.author Gallego-Paüls, Marta
• dc.contributor.author Hernández-Ferrer, Carles
• dc.contributor.author Bustamante Pineda, Mariona
• dc.contributor.author Basagaña Flores, Xavier
• dc.contributor.author Barrera Gómez, Jose
• dc.contributor.author Vives Usano, Marta, 1990-
• dc.contributor.author Ruiz Arenas, Carlos, 1990-
• dc.contributor.author Casas Sanahuja, Maribel
• dc.contributor.author Borràs, Eva
• dc.contributor.author Sabidó Aguadé, Eduard, 1981-
• dc.contributor.author Estivill, Xavier, 1955-
• dc.contributor.author Urquiza, José M.
• dc.contributor.author González, Juan Ramón
• dc.contributor.author Vrijheid, Martine
• dc.contributor.author Maitre, Léa
• dc.date.accessioned 2021-11-30T07:16:54Z
• dc.date.available 2021-11-30T07:16:54Z
• dc.date.issued 2021
• dc.description.abstract Background: Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. Methods: We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. Results: All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. Conclusions: Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.
• dc.description.sponsorship The study has received funding from the European Community’s Seventh Framework Programme (FP7/2007-206) under grant agreement no 308333 (HELIX project) and the H2020-EU.3.1.2. - Preventing Disease Programme under grant agreement no 874583 (ATHLETE project). Additionally, BiB received core infrastructure funding from the Wellcome Trust (WT101597MA) and a joint grant from the UK Medical Research Council (MRC) and Economic and Social Science Research Council (ESRC) (MR/N024397/1). INMA data collections were supported by grants from the Instituto de Salud Carlos III, CIBERESP, and the Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the Lithuanian Agency for Science Innovation and Technology (6-04-2014_31V-66). The Rhea project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV- 1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX), and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011- 2014; “Rhea Plus”: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I+D+i 2013-2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. MV-U and CR-A were supported by a FI fellowship from the Catalan Government (FI-DGR 2015 and #016FI_B 00272). MC received funding from Instituto Carlos III (Ministry of Economy and Competitiveness) (MS16/00128). LM is funded by a Juan de la Cierva-Incorporación fellowship (IJC2018-035394-I) awarded by the Spanish Ministerio de Economía, Industria y Competitividad.
• dc.format.mimetype application/pdf
• dc.identifier.citation Gallego-Paüls M, Hernández-Ferrer C, Bustamante M, Basagaña X, Barrera-Gómez J, Lau CE et al. Variability of multi-omics profiles in a population-based child cohort. BMC Med. 2021;19(1):166. DOI: 10.1186/s12916-021-02027-z
• dc.identifier.doi http://dx.doi.org/10.1186/s12916-021-02027-z
• dc.identifier.issn 1741-7015
• dc.identifier.uri http://hdl.handle.net/10230/49090
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof BMC Med. 2021;19(1):166
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/308333
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874583
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/211250
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/226285
• dc.rights © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Children
• dc.subject.keyword Cross-omics
• dc.subject.keyword DNA methylation
• dc.subject.keyword Exposome
• dc.subject.keyword Metabolomics
• dc.subject.keyword Multi-omics
• dc.subject.keyword Population study
• dc.subject.keyword Variability
• dc.subject.keyword mRNA
• dc.subject.keyword miRNA
• dc.title Variability of multi-omics profiles in a population-based child cohort
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion