Prioritizing disease-related rare variants by integrating gene expression data

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Rare genetic variants significantly contribute to human diseases, often with greater impact than common variants.
• Prioritizing disease-related rare variants is crucial for understanding complex genetic disorders.
• Existing methods struggle with limited sample sizes and accurately assessing variant impact.

Approach:

• Developed carrier statistic, a statistical framework integrating gene expression data to prioritize rare variants.
• Quantified the functional consequence of rare variants by their impact on gene expression in patients.
• Validated the method using simulations and real multi-omics datasets, demonstrating applicability with small sample sizes.

Key Points:

• Carrier statistic achieves higher sensitivity than existing methods for rare variant association.
• The method is effective even with limited sample sizes (hundreds of individuals).
• Applied to Alzheimer's disease, identifying 16 significant rare variants in 15 genes.

Conclusions:

• Carrier statistic offers a powerful tool for investigating complex disease mechanisms by prioritizing functionally significant rare variants.
• The method is adaptable to various rare variant types and other omics data modalities.
• Demonstrated a significant enrichment of rare variants in prioritized genes among diseased individuals.