Welcome to the BBMRI-NL atlas


This website gives you access to the summary data of all association studies that have been performed by the BBMRI-NL consortium.

With the atlas you can:

  • Query – search the results of the BBMRI -omics studies
  • Data – download the original data tables and find corresponding literature references
  • API – see how you can directly communicate with the server
  • About – get contact information and FAQ

For information on how to use the search function and how to interpret the results please click the icon here.

The data in the BBMRI -omics atlas has been generated in the following studies:

Study

Bonder, M.J., et al. (2017) Disease variants alter transcription factor levels and methylation of their binding sites, Nat. Genet. 49(1):131-138

Data

independant top cis- and trans-meQTL and eQTMs (FDR ≤ 0.05)
Cis-meQTLs independent top effects
Cis-eQTMs independent top effects
Trans-meQTLs top effects

Study

Zhernakova, D.V., et al. (2017) Identification of context-dependent expression quantitative trait loci in whole blood, Nat. Genet. 49(1):139-145

Data

cis-eQTLs (FDR ≤ 0.05)
Cis-eQTLs Gene-level independent top effects with context specific effects
Cis-eQTLs Gene-level all primary effects
Cis-eQTLs Exon-level independent top effects
Cis-eQTLs Exon-ratio independent top effects
Cis-eQTLs PolyA-ratio independent top effects

Study

van der Lee, S.J., et al. (2018) Circulating metabolites and general cognitive ability and dementia: Evidence from 11 cohort studies, Alzheimer's & Dementia S1552-5260(17)33855-4

Data

General Cognitive Ability MWAS (all results)

Study

van Dongen, J., et al. (2016) Genetic and environmental influences interact with age and sex in shaping the human methylome, Nat Commun 7:11115

Data

Heritability of DNA methylation

Study

van Dongen, J., et al. (2018) DNA methylation signatures of educational attainment, npj Science of Learning

Data

Educational Attainment EWAS (all results)

Study

Onderwater, G.L.J., et al. (2018) Large scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine, submitted

Data

Data under embargo until accepted for publication Migraine MWAS (all results)

Study

Baselmans, B., et al. (2018) Multivariate Genome-Wide Analyses of the Well-being Spectrum, Nat. Genet. (in press)

Data

Materials and Methods
NET_RNA_download.RData (zipped)
NET_Methylation_download.RData (zipped)

HTTP JSON web service

It is possible to directly access the BBMRI atlas database through our web service. Queries can be sent to http://bbmri.researchlumc.nl/atlas/api as an HTTP POST request with the query encoded as a JSON object. The JSON object is expected to contain 3 fields: "SNP", "CpG" and "Gene", where each field should contain an empty string except for the one corresponding to the database that has to be queried, which should contain a comma-separated list of identifiers.

Example of a SNP query:

{
  SNP:"rs1831701,rs663565,rs11163924,rs145123183,rs7535991,rs76963882,rs815306,rs11161447,rs12541647",
  CpG:"",
  Gene:""
}

The server's response will be a JSON object containing the subset of the BBMRI data tables that correspond to the query.

Contact

The BBMRI atlas website has been developed by Jan Bert van Klinken. For questions, comments or to report bugs, please contact j dot b dot van underscore klinken at lumc dot nl. For (technical) questions related to the association studies that have been used for this resource, please contact the authors of the corresponding articles.

Development

The front-end of the web application is based on Bootstrap and AngularJS, with D3 and dagre for the network visualization. The back-end was implemented in NodeJS. For the database PostgreSQL was used as database management system and the node-postgres module to connect to the webserver.

It was chosen to set up the database structure in such a way that the multi-layered-ness of the data is best represented. Specifically, two types of tables are used to store the data: tables containing information about the specific items in each layer (SNPs, CpG sites, genes) and tables containing association results between two layers. This implementation makes it possible to perform graph-like queries and facilitates future extensions of the database with new data layers.

Query statistics are logged to monitor server use and performance. Specifically, the client IP address, type of query, number of queried ids, and request/response time are logged, while the query content is discarded. Information about the IP address and/or query statistics will not be used for other than the aforementioned purpose and is not shared with third parties (e.g. Google Analytics).

FAQ

I used the BBMRI atlas in my study. How should I refer to it?
Please refer to the studies reported here. The BBMRI -omics atlas itself has not been published.

I cannot find a SNP although it is present in dbSNP.
The BBMRI atlas contains GoNL SNPs, so SNPs exclusively called in other panels (e.g. 1000G, HRC) can currently not be queried. If you use rs ids in your SNP query, it is possible that the rs id has been merged with that of another SNP. In this case you can submit the chromosomal location (hg19) of the SNP (e.g. chr11:18325146).

I queried a gene, but do not find any eQTLs or eQTMs.
The -omics data (methylation, gene expression, metabolite) on which the BBMRI atlas is based have been measured in plasma samples, so the reported associations first of all reflect those present in the blood. As a consequence, if the queried gene is not expressed, or has low expression in the blood, no eQTMs and eQTLs will be reported. Also, note that eQTLs and eQTMs are tissue specific, so if no associations with your gene are reported in the BBMRI atlas then this does not exclude the possibility that these associations exist in other tissues.

I am not able to download the query results in JSON or TSV format.
Currently the query results can only be downloaded using the Firefox Web browser.

SNP SNP (proxy) LD R2 alleles CpG type p-value Z-score FDR
{{ QTL_table.snp_idstr }} {{ QTL_table.snp_proxy_idstr }} {{ QTL_table.r2 }} {{ QTL_table.snp_proxy_a1 }}/{{ QTL_table.snp_proxy_a2 }} {{ QTL_table.cpg_idstr }} {{ QTL_table.type }} {{ QTL_table.pvalue }} {{ QTL_table.zscore }} {{ QTL_table.fdr }}
SNP SNP (proxy) LD R2 alleles gene type p-value Z-score FDR
{{ QTL_table.snp_idstr }} {{ QTL_table.snp_proxy_idstr }} {{ QTL_table.r2 }} {{ QTL_table.snp_proxy_a1 }}/{{ QTL_table.snp_proxy_a2 }} {{ QTL_table.gene_name }} {{ QTL_table.type }} {{ QTL_table.pvalue }} {{ QTL_table.zscore }} {{ QTL_table.fdr }}
CpG gene type p-value Z-score FDR
{{ QTL_table.cpg_idstr }} {{ QTL_table.gene_name }} {{ QTL_table.type }} {{ QTL_table.pvalue }} {{ QTL_table.zscore }} {{ QTL_table.fdr }}
# query database id nr. SNP-CpG associations nr. SNP-Gene associations nr. CpG-Gene associations
{{ QTL_table.query_number }} {{ QTL_table.query }} {{ QTL_table.identifier }} {{ QTL_table.SNP_CpG }} {{ QTL_table.SNP_Gene }} {{ QTL_table.CpG_Gene }}