Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics

Daniel Munro, Nava Ehsan, Seyed Mehdi Esmaeili-Fard, Alexander Gusev (), Abraham A. Palmer () and Pejman Mohammadi ()
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Daniel Munro: UC San Diego
Nava Ehsan: Scripps Research
Seyed Mehdi Esmaeili-Fard: Seattle Children’s Research Institute
Alexander Gusev: Dana-Farber Cancer Institute and Harvard Medical School
Abraham A. Palmer: UC San Diego
Pejman Mohammadi: Seattle Children’s Research Institute

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract RNA sequencing has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, but studies on gene regulation are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry, a framework to efficiently generate diverse RNA phenotypes from RNA sequencing data and perform downstream integrative analyses with genetic data. Pantry generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We apply Pantry to Geuvadis and GTEx data, finding that 4768 of the genes with no identified eQTL in Geuvadis have QTL in at least one other transcriptional modality, resulting in a 66% increase in genes over eQTL mapping. We further found that the QTL exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs.

Date: 2024
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DOI: 10.1038/s41467-024-54840-8

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