Input selection

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).

Reference Genome

Browsing options

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)

Please remeber to press the 'Show Results!' button to update the plot.

Visualization options

Download your plot
Download Plot

Linkage Disequilibrium (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.
Download LD info
Download LD table

Structural variations (SV)

Download the SVs in the region
Download All SVs

Top SNPs info

Download the top associations
Download All associations

eQTL associations

You can add additional tissues by selecting them on the box below. Selecting All_tissues will display results of all GTEx tissues. It may take some time in case you select many tissues and the window-size is big, so please be patient.
Download the top associations
Download All eQTLs

Cross references

If you specify a Gene or a SNP id, you will find here links to GeneCards and dbSNP.
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.

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.
Once analysis is done, you'll receive results by email.
Please check your spam folder to get your snpXplorer results.
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.

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 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.

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.

Example file #1 ~ chr:position

16:89659
16:90318
16:439873
16:904328
.. .. ..

Download sample file
Example file #1 ~ chr position

16 89659
16 90318
16 439873
16 904328
.. .. ..

Download sample file
Example file #3 ~ rsID

rs7412
rs1234
rs439873
rs904328
.. .. ..

Download sample file

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 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.

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

17-02-22: Annotation: added sQTL analysis.

24-12-21: GWAS catalog release was updated to the latest available.

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.

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).

25-07-21: New GWAS added: GR@ACE GWAS of Alzheimer's disease was added.