Transcriptomic signatures across human tissues identify functional rare genetic variation.

Area of Science:

• Genomics
• Transcriptomics
• Human Genetics

Background:

• Rare genetic variants (RVs) are widespread but challenging to functionally characterize.
• Gene expression analysis has identified some functional RVs, but a comprehensive approach is needed.

Purpose of the Study:

• To expand the detection of genetically driven transcriptome abnormalities.
• To develop a model integrating multiple signals for predicting variant function.
• To assess the impact of RVs on molecular effects and human traits.

Main Methods:

• Analyzed gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data.
• Developed Watershed, a probabilistic model integrating genomic and transcriptomic signals.
• Validated predictions using additional cohorts and experimental assays across large biobanks.

Main Results:

• Identified unique classes of RVs informed by distinct transcriptomic signals.
• Watershed successfully predicted variant function, linking thousands of RVs to diverse molecular effects.
• Demonstrated association between transcriptome-affecting RVs and human traits.

Conclusions:

• Integrated genomic and transcriptomic analysis provides a powerful framework for understanding rare variant function.
• The Watershed model enhances the identification of genetically driven molecular and phenotypic changes.
• This work links a significant number of rare variants to biological effects and human health.