CREST-GV: Cell types Ranking and Enrichment Score for selecTive Genetic Variants

Genome-Wide Association Studies (GWAS) have transformed our understanding of genetic traits and diseases since 2005 by analysing genetic variants across the human genome. Whilst they have successfully identified thousands of genetic locations linked to various conditions, converting these discoveries into practical understanding remains challenging. A key challenge lies in understanding through which cell types and tissues a specific set of genetic variants functions. This knowledge would aid researchers in designing experiments and identifying therapeutic targets. Causal variants often affect cell type specific cis-regulatory elements (CREs) and therefore the genetics of a trait are enriched within the CREs of effector cell types. Recent advances in genomics and computational methods are improving our understanding of genetic variants effects across cellular environments. In this work, we introduce Cell types Ranking and Enrichment Score for selecTive Genetic Variants (CREST-GV), a statistical tool that analyses genetic variants intersecting with open chromatin regions across hundreds of cell types, by performing a systematic computation of probability distributions and standardised metrics, transforming genomic data into quantitative enrichment measures with corresponding significance values. The tool demonstrates strong capability in prioritising disease-relevant cell types, for example, in diabetes studies where it correctly identified immune cells for Type 1 diabetes, and pancreatic islet cells and adipose tissue for Type 2 diabetes. CREST-GV shows robust generalisation across diverse genetic traits, successfully analysing variants associated with different conditions whilst maintaining consistent performance and adaptability through its flexible framework. This work includes a user-friendly web application connected to our public database of precomputed enrichment scores. The platform enables rapid querying across cell types and genetic variants, provides standardised results for comparative analysis, and allows the exploration of multiple cell type relationships simultaneously. This accessible interface makes complex enrichment analyses available to the broader research community, accelerating genetic research.