# ClipperQTL: ultrafast and powerful eGene identification method

## TL;DR

ClipperQTL is a fast and effective method for identifying genes regulated by nearby genetic variants, outperforming existing tools in speed without losing accuracy.

## Contribution

ClipperQTL introduces a novel permutation approach that achieves high power and speed for cis-eGene identification.

## Key findings

- ClipperQTL performs as well as FastQTL but runs up to 500 times faster.
- It requires only one permutation for large sample size datasets (>450 samples).

## Abstract

A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene identification methods are either computationally expensive, requiring thousands of permutations per gene (FastQTL), or statistically underpowered (eigenMT and TreeQTL). We propose ClipperQTL, which requires only one permutation for data sets with large sample sizes (>450; ClipperQTL works on smaller data sets too). We show that ClipperQTL performs as well as FastQTL and runs up to 500 times faster. The R package ClipperQTL is available at https://github.com/heatherjzhou/ClipperQTL.

The online version contains supplementary material available at 10.1186/s13059-025-03662-y.