Large-scale analysis of temporal gene expression variation in peripheral blood

Transcriptomic profiling of peripheral blood offers a promising, non-invasive approach for disease diagnosis and monitoring. However, its clinical translation is hindered by limited knowledge of the natural temporal variation. Here, we present a comprehensive reference map of longitudinal transcriptomic variability, based on RNA-sequencing of 333 healthy individuals sampled at three time points over six months. We find that 85% of genes and 99% of transcripts exhibit greater intra-individual than inter-individual variation, primarily driven by dynamic regulation of housekeeping pathways. In contrast, immune-related transcripts -particularly those linked to T and B cell activity- are strikingly stable over time. Gene expression levels drive inter-individual differences, while splicing variation contributes more to intra-individual fluctuation. In an independent twin cohort (148 monozygotic, 166 dizygotic), genes with high inter-individual variability show greater heritability, suggesting genetic control of steady-state expression. By integrating extensive clinical and environmental data, we trace temporal expression changes to genetic, compositional, and external factors, and identify robust seasonal and sex-specific signatures. These findings were validated in a third, cross-sectional cohort of 3,480 individuals. The observed temporal variation patterns have important implications for cohort-based transcriptomic analyses, as they may limit discovery and reproducibility of expression quantitative trait loci and increase the risk of spurious associations in cross-sectional studies. This resource provides a critical baseline for distinguishing disease-associated transcriptomic changes from normal physiological variation, advancing the reliability of blood-based biomarkers in clinical practice.

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Journal
Nature Communications
Published
2026-05-29
DOI
https://doi.org/10.1038/s41467-026-73218-6
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

Large-scale analysis of temporal gene expression variation in peripheral blood

Kenneth G. C. Smith, Johanna I. Blase, Kerrin S. Small, Benedikt Reiz et al.
Nature Communications
Genetic Associations and Epidemiology
article

Large-scale analysis of temporal gene expression variation in peripheral blood

Kenneth G. C. Smith, Johanna I. Blase, Kerrin S. Small, Benedikt Reiz, Souad Rahmouni, Lindsey De Commer, Joana P. Bernardes, Axel Finckh, Michel Georges, Chloë Verspecht, Marek Ostaszewski, Sara Vieira‐Silva, Jeroen Raes, Aggelos Banos, Eoin McKinney, Lindsay Devolder, Gordan Lauc, Vlatka Zoldoš, Aleksandar Vojta, E. T. Dermitzakis, Despina Sanoudou, Christina Kratsch, Łukasz Kreft, Doris Vandeputte, Paul Lyons, Roland Krause, Amy Kenyon, Sebastian Proost, Jonas Schulte-Schrepping, Mohamad Jawhara, Gwen Falony, Nordstrom Karl, Gioacchino Natoli, Elisa Rosati, Reinhard Schneider, Valentina Talevi, Marija Pezer, Stephan Huthmacher, Alexander Botzki, Venkata Satagopam, Julia S. El-Sayed Moustafa, Prodromos Sidiropoulos, George Bertsias, Robert Häsler, Dimitrios Boumpas, Silja H. Overgaard, Philip Rosenstiel, Arnau Vich Vila, Franziska Kimmig, N. Ahmad Aziz, Stefanie Warnat-Herresthal, Gilles Gasparoni, Monique M. B. Breteler, Jörn Walter, Vibeke Andersen, Paraskevi Christofidou, Wei Gu, Marc Beyer, Dongmeng Wang, Joachim L. Schultze, Signe B. Sørensen, Konrad Aden, Stefan Schreiber, Massimo Mangino, Andre Franke, Youssef El Darzi, Neha Mishra, Timothy Spector
article en

Abstract

Transcriptomic profiling of peripheral blood offers a promising, non-invasive approach for disease diagnosis and monitoring. However, its clinical translation is hindered by limited knowledge of the natural temporal variation. Here, we present a comprehensive reference map of longitudinal transcriptomic variability, based on RNA-sequencing of 333 healthy individuals sampled at three time points over six months. We find that 85% of genes and 99% of transcripts exhibit greater intra-individual than inter-individual variation, primarily driven by dynamic regulation of housekeeping pathways. In contrast, immune-related transcripts -particularly those linked to T and B cell activity- are strikingly stable over time. Gene expression levels drive inter-individual differences, while splicing variation contributes more to intra-individual fluctuation. In an independent twin cohort (148 monozygotic, 166 dizygotic), genes with high inter-individual variability show greater heritability, suggesting genetic control of steady-state expression. By integrating extensive clinical and environmental data, we trace temporal expression changes to genetic, compositional, and external factors, and identify robust seasonal and sex-specific signatures. These findings were validated in a third, cross-sectional cohort of 3,480 individuals. The observed temporal variation patterns have important implications for cohort-based transcriptomic analyses, as they may limit discovery and reproducibility of expression quantitative trait loci and increase the risk of spurious associations in cross-sectional studies. This resource provides a critical baseline for distinguishing disease-associated transcriptomic changes from normal physiological variation, advancing the reliability of blood-based biomarkers in clinical practice.

Nature Communications
University of Geneva (CH), SIB Swiss Institute of Bioinformatics (CH), Vrije Universiteit Brussel (BE), University of Bonn (DE), University of Liège (BE), Queen Mary University of London (GB), University of Southern Denmark (DK), University of Zagreb (HR), King's College London (GB), University of Luxembourg (LU), Johannes Gutenberg University Mainz (DE), Academy of Athens (GR), Rega Institute for Medical Research (BE), University Hospital Bonn (DE), German Center for Neurodegenerative Diseases (DE), Christian-Albrechts-Universität zu Kiel (DE), Ghent University (BE), Institut für Medizinische Biometrie, Informatik und Epidemiologie (DE), University Medical Center of the Johannes Gutenberg University Mainz (DE), Quantitative BioSciences (US), University Hospital of Geneva (CH), University Hospital Schleswig-Holstein (DE), VIB-KU Leuven Center for Microbiology (BE), Genos (Croatia) (HR), VIB-VUB Center for Structural Biology (BE), Institute of Medical Microbiology and Hygiene (DE), Science Research Laboratory (US), Institute of Molecular Biology (DE), European Institute of Oncology (IT), Foundation for Research and Technology Hellas (GR), Wageningen University & Research (NL), Saarland University (DE), University of Lübeck (DE), KU Leuven (BE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 6%
Genetic Associations and Epidemiology
DOIOpen Access PDFOpenAlex
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