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Discovering Root Causal Genes with High Throughput Perturbations.

Eric V Strobl¹, Eric R Gamazon²

¹University of Pittsburgh.

Biorxiv : the Preprint Server for Biology

|January 23, 2024

Summary

This summary is machine-generated.

Identifying root causal genes is crucial for early disease treatment. This study introduces a novel method using Perturb-seq to uncover these genes from RNA-sequencing data, improving disease understanding.

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Root causal genes initiate disease processes, making their identification key for early therapeutic intervention.
Current algorithms struggle to identify root causal genes from complex RNA-sequencing (RNA-seq) data due to noise and non-linearity.
Existing methods are insufficient for accurately estimating causal effects from RNA-seq data.

Purpose of the Study:

To develop a novel computational approach for identifying root causal genes.
To leverage Perturb-seq data to establish gene causal order.
To apply this causal order to bulk RNA-seq data for patient-specific root causal gene identification.

Main Methods:

Utilized Perturb-seq (high-throughput perturbations with single-cell RNA-seq) to determine the causal relationships between genes.
Transferred the learned causal order from Perturb-seq to bulk RNA-seq datasets.
Developed and applied a novel statistic to identify patient-specific root causal genes.

Main Results:

The novel method significantly improved the identification of root causal genes compared to existing approaches.
Successfully identified root causal genes in patient data for macular degeneration and multiple sclerosis.
Discovered root causal genes involved in known disease pathways and patient subgroup delineation.

Conclusions:

This study presents the first algorithm capable of identifying patient-specific root causal genes.
The findings provide a new understanding of disease etiology and potential therapeutic targets.
The developed method has implications for personalized medicine and understanding complex diseases.