Snpannotator: Automated Functional Annotation of Genetic Variants and Linked Proxies Publisher Pubmed

Summary: Can software help explain genetic associations? SNPannotator automates post-GWAS variant interpretation and pathway analysis, offering researchers an accessible online tool. #Genomics #Bioinformatics

Ani A ; Nolte I M ; Kamali Z ; Snieder H ; Vaez A
Source: Bioinformatics Published:2026

Abstract

Summary: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. Availability and implementation: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator. © The Author(s) 2026. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.