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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome.

Daniel Munro1,2, Alexander Gusev3, Abraham A Palmer1,4

  • 1Department of Psychiatry, UC San Diego, La Jolla, CA, USA.

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|February 27, 2026
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Summary

LaDDR, a new RNA phenotyping framework, enhances the discovery of genetic associations with complex traits by analyzing transcriptomic data without complete gene annotations. This method significantly increases the identification of regulatory variations missed by existing pipelines.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Transcriptomic diversity is shaped by RNA regulation, crucial for mapping genetic associations (xQTLs) and interpreting genome-wide association studies (GWAS).
  • Existing multimodal frameworks like Pantry integrate various RNA data but require complete gene annotations and face statistical complexity.
  • Limitations in current tools hinder comprehensive discovery of regulatory variations influencing complex traits.

Purpose of the Study:

  • To introduce LaDDR (Latent Data-Driven RNA phenotyping), a novel mechanism-agnostic framework for enhanced xQTL discovery and GWAS integration.
  • To enable xQTL discovery and GWAS interpretation without reliance on complete gene annotations.
  • To broaden the landscape of detectable trait-relevant transcriptomic regulation.

Main Methods:

  • LaDDR generates orthogonal, latent coverage features per gene, independent of specific RNA regulation mechanisms.
  • The framework was applied to GTEx data for xQTL discovery and integrated with transcriptome-wide association studies (TWAS).
  • LaDDR was evaluated against knowledge-driven phenotypes from the Pantry framework, with and without residualizing known modalities.

Main Results:

  • LaDDR identified an average of 95% more independent xQTLs per tissue compared to Pantry's six knowledge-driven modes.
  • Combining LaDDR with knowledge-driven phenotypes increased discovery by an additional 41% per tissue on average.
  • In TWAS of 114 complex traits, LaDDR uncovered an average of 11,790 unique gene-trait pairs per tissue, surpassing knowledge-driven phenotypes (8,579).

Conclusions:

  • LaDDR significantly expands the discovery of genetic associations by efficiently capturing regulatory variation missed by current pipelines.
  • The framework provides a powerful, annotation-agnostic approach for xQTL discovery and GWAS interpretation.
  • LaDDR broadens the detectable landscape of trait-relevant transcriptomic regulation, offering new insights into complex trait genetics.