The gene expression landscape of disease genes

TL;DR

This study maps where disease-related genes are expressed in the body, revealing new tissue and cell type connections for various diseases and aiding drug development.

Contribution

A novel framework directly analyzing gene expression of disease genes across tissues and cell types using GWAS and RNA-seq data.

Findings

Disease genes show higher and more specific expression in tissues and cell types relevant to their associated diseases.

Elevated gene expression was detected in tissues and cell types not previously linked to specific diseases.

The study identifies factors influencing disease gene expression and highlights tissue-disease pairs relevant to drug development.

Abstract

Fine-mapping and gene-prioritisation techniques applied to the latest genome-wide association study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate, providing a more direct approach than previous studies, which have primarily relied on the enrichment of GWAS signals among genes with cell- or tissue-specific expression.

By integrating GWAS summary statistics, gene prioritisation results, and RNA-seq data from 46 tissues and 204 cell types, we directly analyse the gene expression of putative disease genes across the body in relation to 11 major diseases and cancers. In tissues and cell types with established disease relevance, disease genes show higher and more specific gene expression compared to control genes. Moreover, we detect elevated expression in tissues and cell types without previous links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer’s disease (P-values < 10–3), the cause for others is unclear and warrants further investigation. To support functional follow-up studies of disease genes, we identify technical and biological factors influencing their expression. Finally, we highlight tissue-disease pairs in which significantly elevated expression is associated with increased odds of inclusion in drug development programmes.

We provide our systematic testing framework as an open-source, publicly available tool that can be utilised to offer novel insights into the genes, tissues and cell types involved in any disease, with the potential for informing drug development and delivery strategies.

The online version contains supplementary material available at 10.1186/s13059-026-03958-7.

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