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

BMC Medicine

John Wright¹  Alexandros P. Siskos²  Hector C. Keun²  Xavier Estivill³  Regina Grazuleviciene⁴  Leda Chatzi⁵  Muireann Coen⁶  Martine Vrijheid⁷  Juan R. González⁷  Maribel Casas⁷  Marta Gallego-Paüls⁷  Carlos Ruiz-Arenas⁷  Xavier Basagaña⁷  Jose Barrera-Gómez⁷  Léa Maitre⁷  Carles Hernández-Ferrer⁷  Jose Urquiza⁷  Marta Vives-Usano⁸  Mariona Bustamante⁸  Chung-Ho E. Lau⁹  Ángel Carracedo^1,10  Remy Slama^1,11  Eva Borràs^1,12  Eduard Sabidó^1,12  Barbara Heude^1,13 

[1] Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK;Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer and Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK;Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain;Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania;Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA;Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington, London, UK;Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK;ISGlobal, Barcelona, Spain;Universitat Pompeu Fabra (UPF), Barcelona, Spain;Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain;ISGlobal, Barcelona, Spain;Universitat Pompeu Fabra (UPF), Barcelona, Spain;Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain;Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain;MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK;Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington, London, UK;Medicine Genomics Group, Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER), University of Santiago de Compostela, CEGEN-PRB3, Santiago de Compostela, Spain;Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Servicio Gallego de Salud (SERGAS), Santiago de Compostela, Galicia, Spain;Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Inserm, CNRS, Université Grenoble Alpes, Grenoble, France;Universitat Pompeu Fabra (UPF), Barcelona, Spain;Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain;Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, F-75004, Paris, France;

关键词: Multi-omics;    Exposome;    Variability;    Population study;    Metabolomics;    DNA methylation;    Cross-omics;    mRNA;    miRNA;    Children;   

DOI  :  10.1186/s12916-021-02027-z

来源: Springer

【 摘 要 】

BackgroundMultiple 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.MethodsWe 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.ResultsAll 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.ConclusionsMethylation 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.

【 授权许可】

CC BY