** 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.
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.
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.
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.