Available GWAS
Please check up to 5 boxes at the same time.
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.
Structural Variants
Choose dataset
GWAS Catalog settings
Choose dataset
Download center
SNPs table
GWAS Catalog results
SVs in the region
LD info
Plot
You can get a .png image of the plot by hovering on the plot. Click on the camera icon!
Alternatively, here you can get a .pdf plot or the original .html page (preserves hovering information).
Note that it will take about 10 seconds to produce your plot!
Cross references
If you specify a Gene or a SNP id, you will find here links to GeneCards and dbSNP.
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Top SNPs info
GWAS Catalog SNPs
Structural Variants
GTEx settings
Download
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eQTL Table
sQTL Table
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.
Example file
chr pos p
16 89659 0.19
16 90318 0.48
16 439873 0.28
16 904328 0.01
.. .. ..
Download sample file
Example PLINK file
#CHROM POS ID REF ALT A1 A1_FREQ A1_CASE_FREQ A1_CTRL_FREQ MACH_R2 TEST OBS_CT BETA SE Z_STAT P
17 15802438 rs12449443 G A A 0.07 0.06 0.07 0.99 ADD 4191 0.01 0.16 0.05 0.96
17 29378199 rs57278847 T C C 0.01 0.01 0.02 0.31 ADD 4191 16.40 18.15 0.90 0.36
17 43286432 rs57847 T C C 0.02 0.11 0.42 0.31 ADD 4191 1.40 1.15 0.70 0.16
17 22318299 rs5724327 T C C 0.31 0.61 0.42 0.11 ADD 4191 6.40 58.12 0.60 0.26
.. .. ..
Download sample PLINK file
Annotation section
The annotation section accepts any set of SNPs (possibly more than 1).
Multiple format are accepted: you can choose between rs-identifier or genomic position.
Please see examples on the right or download a trial dataset.
Video tutorials
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.