How RNA-seq quantification choices reshape eQTL, colocalization and TWAS results

Seemingly minor decisions made early in RNA-sequencing data processing can ripple through entire genetic association pipelines, according to a study published in Nature Genetics. Researchers led by S. Taylor Head and Arjun Bhattacharya examined how two upstream choices—the transcriptomic reference annotation used for quantifying short-read RNA-seq samples and the quantification method itself—affect expression quantitative trait locus (eQTL) mapping, colocalization analysis and transcriptome-wide association studies (TWAS). These three approaches are among the most widely used tools for interpreting genome-wide association study (GWAS) loci, helping researchers connect genetic variants to gene expression and identify candidate genes and tissues underlying disease associations.

Yet, the authors note, the impact of upstream processing decisions on these downstream analyses had remained critically unexplored. The team carried out a comprehensive analysis spanning 48 tissues from the Genotype-Tissue Expression (GTEx) project and seven widely studied and diverse phenotypes. The findings were substantial.

The choice of annotation and quantification method affected eQTL detection and gene expression prediction to a considerable degree, and those differences propagated into significant downstream impacts on colocalization and TWAS results. In other words, two research groups analyzing the same data with different but defensible processing choices could reach different conclusions about which genes drive a GWAS signal. The study also identified which results were more likely to agree across pipelines.

Concordant findings—those reproduced regardless of annotation or quantification method—were enriched for longer genes, genes with more isoforms, and genes with greater expression heritability. The technical implications are considerable. The transcriptomic reference annotation defines the composition and structure of transcripts against which sequencing reads are quantified, directly shaping estimated gene and isoform expression levels.

Because eQTL mapping, colocalization and TWAS all build on those expression estimates, any distortion introduced at the quantification stage can alter which variants appear associated with which genes, and ultimately which candidate genes emerge from a GWAS locus. The authors frame their results as a warning about reproducibility in genetic association studies. Decisions that appear minor—such as which annotation release to use or which quantification tool to apply—can substantially affect common analyses, and the paper highlights the need for standardized practices to ensure that results are comparable and reproducible across laboratories.

The work was published in Nature Genetics in October 2026, with the full analysis available in the journal's article on quantification method and transcriptomic annotation effects on eGene identification, gene expression prediction, GWAS colocalization and TWAS.

Subject of Research: Effects of transcriptomic annotation and quantification method on eQTL, colocalization and TWAS reproducibility

Article Title: Quantification method affects replicability of eQTL analysis, colocalization and TWAS

Article References: Head, S. T., Bresnahan, S. T., Chang, Y.-H., Shah, V. M., Cole, N. R., Wu, W., & Bhattacharya, A. (2026). Quantification method affects replicability of eQTL analysis, colocalization and TWAS. Nature Genetics. https://doi.org/10.1038/s41588-026-02827-0

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02827-0

Keywords: eQTL, TWAS, colocalization, RNA-seq, GTEx, transcriptomic annotation, quantification, replicability, genomics, GWAS, bioinformatics, Nature Genetics