A new method has been developed to better understand the genetic basis of complex traits by enhancing the analysis of single-cell expression quantitative trait loci summary statistics.

EXpression PREdiction with Summary Statistics Only (EXPRESSO) integrated 3D genomic data and epigenomic annotations to more effectively prioritize causal variants. Traditional transcriptome-wide association studies (TWAS) often relied on bulk tissue data, lacking the resolution to identify cell-type-specific gene targets.

EXPRESSO overcame this limitation by leveraging single-cell expression quantitative trait loci (eQTL) datasets, enabling the identification of gene-trait associations at the cellular level.

A recent study published in Nature Communications applied EXPRESSO to multi-ancestry genome-wide association studies datasets for 14 autoimmune diseases, identifying 958 novel gene-trait associations, with 492 unique to cell-type-specific analyses – a 26% increase over the next best method.

The researchers developed a pipeline called Cell type Aware Drug REpurposing (CADRE), using EXPRESSO results to identify drug compounds that could reverse disease gene expressions in relevant cell types.

The team's work identified drug compounds that could potentially reverse gene expression in cell types associated with autoimmune diseases. For example, vitamin K was identified as a potential treatment for ulcerative colitis, while metformin, a drug typically prescribed for type 2 diabetes, was found to have the potential to treat type 1 diabetes. The drugs, already approved by the FDA for other conditions, showed potential for the treatment of autoimmune diseases, subject to further research and clinical trials, according to a press release.

The authors declared no competing interests.