BIOS Consortium

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: https://omics-explorer.bbmri.nl.

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 Zwet^15, 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