# Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

## TL;DR

This study shows how RNA-seq data from public databases can be used to find genetic variants that influence gene expression levels and rare pathogenic variants with tissue-specific effects.

## Contribution

The study demonstrates the feasibility of genotype calling from public RNA-seq data to identify eQTLs and rare pathogenic variants with strong ASE effects.

## Key findings

- Cis-eQTL effects were identified for 8,034 unique genes across 1,262 RNA-seq samples.
- Strong ASE effects were observed for 34 rare pathogenic variants, aligning with effects on protein levels.
- RNA-seq samples from the same cell-type clustered together despite diverse sequencing protocols.

## Abstract

Given increasing numbers of RNA-seq samples in the public domain, we studied to what extent expression quantitative trait loci (eQTLs) and allele-specific expression (ASE) can be identified in public RNA-seq data while also deriving the genotypes from the RNA-seq reads. 4,978 human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though this data originated from many different laboratories, samples reflecting the same cell-type clustered together, suggesting that technical biases due to different sequencing protocols were limited. We derived genotypes from the RNA-seq reads and imputed non-coding variants. In a joint analysis on 1,262 samples combined, we identified cis-eQTLs effects for 8,034 unique genes. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become relevant for studying tissue-specific effects of rare pathogenic genetic variants.