Preprint describes snpXplorer, an interactive platform for haplotype-aware GWAS signal annotation
A preprint on bioRxiv presents snpXplorer, a browser-based tool designed to help researchers explore GWAS loci within their haplotype context and integrate functional and regulatory evidence.
A preprint posted to bioRxiv describes snpXplorer, an interactive web platform built to help researchers move from raw GWAS association signals towards biological interpretation. The tool is designed around what the authors call haplotype-aware exploration: rather than examining individual associated variants in isolation, snpXplorer encourages analysis of variants in the context of the linkage disequilibrium blocks in which they sit, preserving the shared regulatory and functional context that may be relevant to understanding which signals are biologically meaningful.
The platform integrates annotation from regulatory, functional, and cross-trait evidence sources, allowing users to examine correlated variants, their predicted functional consequences, and overlap with regulatory features such as enhancers, alongside signals from other traits that map to the same region.
The preprint addresses a recognised bottleneck in GWAS follow-up: most associated variants are non-coding, and interpreting them requires synthesising information from multiple databases and analytic perspectives that are not always straightforward to combine. snpXplorer is presented as a tool to lower that barrier for researchers who do not have the computational infrastructure to build bespoke pipelines.
This work is a preprint and has not yet been peer-reviewed. Readers should interpret its claims accordingly. The platform and its described functionality may change before or during peer review.
Sources
Read the original reporting — these are the public sources this summary draws from.
-
Primary sourcePreprint bioRxiv (Cold Spring Harbor Laboratory) · 2026-07-21snpXplorer: an interactive platform for haplotype-aware exploration and integrated annotation of GWAS data