Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes.

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Differential expression (DE) analysis is standard for identifying genes linked to phenotypes.
• Current DE methods favor highly variable genes, potentially overlooking biologically relevant changes in less variable genes.
• Existing approaches assume equal gene sensitivity to transcript dosage changes.

Purpose of the Study:

• To develop a novel method for recalibrating gene DE fold changes using population genetic expression variance.
• To improve the biological relevance of DE analysis by accounting for natural gene expression variation.
• To enhance the identification of functionally relevant genes and disease-associated pathways.

Main Methods:

• Recalibration of DE fold change for each gene based on observed human genetic expression variance.
• Ranking differentially expressed genes by relative change compared to natural dosage variation.
• Application to RNA sequencing data from in vitro stimulus response and neuropsychiatric disease studies.

Main Results:

• The new method adjusts for bias towards highly variable genes found in standard DE analysis.
• Enriched pathways and biological processes relate to metabolic and regulatory activity, indicating prioritization of driver genes.
• Tissue-specific recalibration improved the detection of known disease-relevant processes.

Conclusions:

• The recalibrated DE metric offers a novel perspective, bridging statistical and biological significance.
• This approach aids in identifying disease-causing molecular processes more effectively.
• The method is expected to enhance the discovery of novel therapeutic targets.