Quantification method affects replicability of eQTL analysis, colocalization and TWAS
Open accessAbstract
Expression quantitative trait loci (eQTL) mapping, colocalization and transcriptome-wide association studies (TWAS) are widely used to contextualize genome-wide association study loci, yet the impact of upstream RNA-sequencing processing choices on these analyses remains critically unexplored. Two poorly characterized decisions include the transcriptomic reference annotation used for quantification of short-read RNA-sequencing samples, which defines transcript composition and structure, and the quantification method itself. Here, through a comprehensive analysis spanning 48 tissues in GTEx and 7 widely studied and diverse phenotypes, we find that these choices substantially affect eQTL detection and gene expression prediction with significant downstream impacts on colocalization and TWAS results. Concordant results across pipelines are further enriched for longer genes, genes with more isoforms, and greater expression heritability. Our findings demonstrate that seemingly minor methodological decisions substantially affect these common analyses, highlighting the need for standardized practices to ensure reproducible genetic association studies.
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- Authors
- Head, S. T., Bresnahan, S. T., Chang, Y.-H., Shah, V. M., Cole, N. R., Wu, W., & Bhattacharya, A.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Rice University (US)
- Corresponding author
- Bhattacharya, Arjun
- Venue
- Nature Genetics
- Published
- 2026-10-26
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC12393437
- OpenAlex
- W4413486707
- Cited by
- 4
- References
- 6
- Citations by year
- 2026: 4
- FWCI
- 12.91
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (R21CA293419, P30CA016672)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- eQTL mapping, transcriptome-wide association study, RNA-seq quantification, reference transcriptome, colocalization, gene expression prediction, replicability, reproducibility, genome-wide association study
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