BIOS Consortium

The mission of the Biobank-based integrative omics study (BIOS) Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks.

The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. For most of these variants, however, the mechanisms through which they contribute to these phenotypes remain unknown.

The BIOS Consortium provides researchers with a large-scale multiple genomics data infrastructure for the discovery of mechanisms and biomarkers of disease. The data infrastructure hosts genetic (imputed SNPs), methylome (Illumina 450k array), transcriptome (RNA-seq), and phenotypic data on ~4000 individuals from 6 Dutch biobanks. The BIOS Consortium is enabling researchers within and outside BBMRI to publish on a wide range of topics in (high-impact) journals.

Available data from the BIOS data infrastructure

Please, use our dedicated explorer to see what is available:

Accessing the data

There are two ways to access BIOS data:

  1. Download the selected data through EGA (fall back option for most research questions)
    RNA-seq, DNA methylation, sex, age and cell count data (but not genetic and more extended phenotype data) can be requested and downloaded from the European Genome-phenome Archive (EGA), accession EGAS00001001077.

    Advantages: Raw data and RNA-seq count data can be downloaded to own computational resources, available for any (international) researchers.
    Disadvantages: data not available in ready-to-analyze formats, no genetic and complete phenotype data available, legal requirements.

    To request access to BIOS data through EGA, please, review and fill out this document.

  2. Analyze all data on a centralized Cloud service (easiest for most research questions)
    All data shared by participating biobanks including genome-wide SNP data, phenotypes, RNA-seq and DNA methylation on all individuals can be analyzed on centralized computational facilities, namely a 64-core virtual machine in the SurfSARA High Performance Computing cloud. Here, BIOS data are readily accessible through the dedicated R package BBMRIomics.

    Advantages: full support including access to all available data including (e.g. privacy sensitive genotypes), data provided in ready-to-analyze formats (i.e. fully -pre-processed, QC’ed, linkages between data types verified), simplified process to get access.
    Disadvantages: data cannot be downloaded, service not yet available for researchers from non-Dutch institutes (unless they are guest employee of a Dutch university or university medical center), not allowed to analyze data on own computational resources.

    To request access to BIOS data through the Cloud, please fill out this Data Access Request Form and sign the BIOS Code Of Conduct.

Where can I get support?

  • Please, view the documentation for the dedicated R package for BIOS data BBMRIomics.
  • More detailed information on the BIOS data infrastructure can be found on our wiki.
  • Post questions on our Biostars forum.

What research is published and ongoing within the BIOS consortium?

  • To find BIOS publications, please visit our Google Scholar page.
  • For an overview of approved data requests click here (this list is updated irregularly).

What are results from the BIOS consortium?

  • You can query and download results from our previous work (e.g. QTL mapping, EWASs, TWASs) using our dedicated BBMRI browser.
  • We developed multiple Bioconductor packages including MethylAid (for the quality control of methylation arrays), omicsPrint (to detect sample swaps, verify data linkage, check family relations, and infer genotypes from methylation arrays), and Bacon (controlling for bias and inflation of test statistics in ome-wide assocations studies).

Who is the BIOS Consortium?

Management Team
Bastiaan T. Heijmans (chair)1, Peter A.C. ’t Hoen2, Joyce van Meurs3, Rick Jansen5, Lude Franke6.

Cohort collection
Dorret I. Boomsma7, René Pool7, Jenny van Dongen7, Jouke J. Hottenga7 (Netherlands Twin Register); Marleen MJ van Greevenbroek8, Coen D.A. Stehouwer8, Carla J.H. van der Kallen8, Casper G. Schalkwijk8 (Cohort study on Diabetes and Atherosclerosis Maastricht); Cisca Wijmenga6, Lude Franke6, Sasha Zhernakova6, Ettje F. Tigchelaar6 (LifeLines Deep); P. Eline Slagboom1, Marian Beekman1, Joris Deelen1, Diana van Heemst9 (Leiden Longevity Study); Jan H. Veldink10, Leonard H. van den Berg10 (Prospective ALS Study Netherlands); Cornelia M. van Duijn4, Bert A. Hofman11, Aaron Isaacs4, André G. Uitterlinden3 (Rotterdam Study).

Data Generation
Joyce van Meurs (Chair)3, P. Mila Jhamai3, Michael Verbiest3, H. Eka D. Suchiman1, Marijn Verkerk3, Ruud van der Breggen1, Jeroen van Rooij3, Nico Lakenberg1.

Data management and computational infrastructure
Hailiang Mei (Chair)12, Maarten van Iterson1, Michiel van Galen2, Jan Bot13, Dasha V. Zhernakova6, Rick Jansen5, Peter van ’t Hof12, Patrick Deelen6, Irene Nooren13, Peter A.C. ’t Hoen2, Bastiaan T. Heijmans1, Matthijs Moed1.

Data Analysis Group
Lude Franke (Co-Chair)6, Martijn Vermaat2, Dasha V. Zhernakova6, René Luijk1, Marc Jan Bonder6, Maarten van Iterson1, Patrick Deelen6, Freerk van Dijk14, Michiel van Galen2, Wibowo Arindrarto12, Szymon M. Kielbasa15, Morris A. Swertz14, Erik. W van Zwet15, Rick Jansen5, Peter-Bram ’t Hoen (Co-Chair)2, Bastiaan T. Heijmans (Co-Chair)1.


  1. Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
  2. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
  3. Department of Internal Medicine, ErasmusMC, Rotterdam, The Netherlands
  4. Department of Genetic Epidemiology, ErasmusMC, Rotterdam, The Netherlands
  5. Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands
  6. Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
  7. Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands
  8. Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands
  9. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
  10. Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
  11.  Department of Epidemiology, ErasmusMC, Rotterdam, The Netherlands
  12. Sequence Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
  13. SURFsara, Amsterdam, the Netherlands
  14. Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
  15. Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

Updated: 7 January 2019