By default (i.e. without selecting any input dataset, snpXplorer will load IGAP study (chr16).
Add your GWAS
Visualize your own GWAS data
Please make sure data has at least chromosome, position and p-value columns. A header is required. See More/Help to download sample files and More/Documentation for additional information.
Reference Genome
Choose your reference genome
By default, GRCh37 (hg19) is used. snpXplorer uses liftOver tool to change genomic coordinates between reference genome versions.
Browsing options
Choose how to browse the genome
Locus: genomic location of interest. The format is chromosome:position (e.g 1:12345678)
RsID: variant of interest. The format is rsXXXX (e.g rs7412)
Gene name: gene of interest. The format is gene symbol (e.g APOE)
Graphical options
Customize your plot
Linkage Disequilibrium
Add LD
Select population(s) to calculate LD
** If you don't select any population, by default European samples of the 1000Genome project will be used. Please refer to the Help section for additional information about the populations reported above.
** In case the top SNP is not in 1000Genome data, we take the second-most significant, then the third-most significant, etc.
snpXplorer and its annotation pipeline have been developed in collaboration with Amsterdam UMC and TU Delft.
Any suggestion or bug report is highly appreciated. Please email n.tesi@amsterdamumc.nl or snpxplorer@gmail.com.
By default, only Blood is considered. Adding multiple tissues may impact your analysis as a larger number of genes may be associated with each SNP.
You will receive a confirmation e-mail for your analysis.
Once analysis is done (typically less than 30 minutes), you'll receive results by e-mail.
Please check your spam folder to get your snpXplorer results.
snpXplorer and its Annotation pipeline have been developed in collaboration with Amsterdam UMC and TU Delft.
Any suggestion or bug report is highly appreciated. Please email n.tesi@amsterdamumc.nl or snpxplorer@gmail.com.
Studies and References
We are including more and more summary statistics.
For particular requests, please contact snpxplorer@gmail.com
Available summary statistic within snpXplorer
1000 Genome Project population codes
Structural variants datasets
snpXplorer and its annotation pipeline have been developed in collaboration with Amsterdam UMC and TU Delft.
Any suggestion or bug report is highly appreciated. Please email n.tesi@amsterdamumc.nl or snpxplorer@gmail.com
Documentation and code
Documentation can be viewed in the blox below.
Source code is available at Github
Source code is freely available through our Github page.
snpXplorer can also be installed locally in your machine, but you will need to download summary statistics yourself.
snpXplorer and AnnotateMe have been developed in collaboration with Amsterdam UMC and TU Delft.
Any suggestion or bug report is highly appreciated. Please email n.tesi@amsterdamumc.nl.
Cite us
Please do not forget to cite us if you find snpXplorer useful for your research.
!! snpXplorer was published in Nucleic Acid Research
snpXplorer functional annotation was used in the following papers:
Niccolo’ Tesi et al., Polygenic risk score of longevity predicts longer survival across an age-continuum. The Journals of Gerontology: Series A, , glaa289, https://doi.org/10.1093/gerona/glaa289
Niccolo’ Tesi et al., The effect of Alzheimer's disease-associated genetic variants on longevity. Front Genet., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8724252/
snpXplorer and AnnotateMe have been developed in collaboration with Amsterdam UMC and TU Delft.
Any suggestion or bug report is highly appreciated. Please email n.tesi@amsterdamumc.nl.
Help page
This page provides sample files to try out both the Exploration and the Annotation part, and a quick-start video.
Exploration section
For the exploration section, please provide a space- or tab-separated file with header.
Make sure there are at least chromosome number, position and p-value. snpXplorer will try to understand the columns from the file header.
Alternatively, you can use PLINK formatted files. Look at the examples below or download a ready-to-use dataset to try out.
Have a look at our quick start videos of snpXplorer.
There are tutorials about exploration section and annotation sections.
Quick Start tutorial
Load your own file tutorial
Functional annotation of your SNP list
Discover LD patterns
snpXplorer and its annotation pipeline have been developed in collaboration with Amsterdam UMC and TU Delft.
Any suggestion or bug report is highly appreciated. Please email n.tesi@amsterdamumc.nl or snpxplorer@gmail.com
Bug Report page
snpXplorer remembers the settings of your last run. In case you found a problem in the last run, you can now directly report the bug to snpXplorer.
What's new
March 2022
22-03-22: Completely redesigned and reprogrammed Exploration section which allows for much more investigation.
February 2022
17-02-22: Annotation: added sQTL analysis.
December 2021
24-12-21: GWAS catalog release was updated to the latest available.
November 2021
11-11-21: New GWAS added: Largest GWAS of Alzheimer's disease was added.
11-11-21: New GWAS added: Multivariate analysis of Longevity, Healthspan and Lifespan was added.
10-11-21: New Annotation analysis added: possibility to do only SNP-gene mapping without gene-set enrichment analysis. This allows the user to annotate up to 10,000 SNPs.
August 2021
25-08-21: CADD v1.6 (the most updated) is now used for SNP annotation.
25-08-21: To cope with rare SNPs, we now use data from all individuals of the 1000Genome Project (before, only European individuals and common SNPs were recognized).
July 2021
25-07-21: New GWAS added: GR@ACE GWAS of Alzheimer's disease was added.