Prioritizing disease-related rare variants by integrating gene expression data

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Rare genetic variants are crucial in disease etiology, often having greater impact than common variants.
• Prioritizing disease-related rare variants is challenging due to their low frequency and the complexity of genetic interactions.

Purpose of the Study:

• To develop and validate a statistical framework, 'carrier statistic', for prioritizing disease-associated rare variants.
• To integrate gene expression data to quantify the functional impact of rare variants on patient genes.

Main Methods:

• Developed the 'carrier statistic' framework to prioritize rare variants based on their impact on gene expression.
• Utilized simulation studies and real multi-omics datasets for validation.
• Applied the method to Alzheimer's disease patient data.

Main Results:

• Carrier statistic demonstrates high sensitivity and effectiveness, even with limited sample sizes (hundreds of individuals).
• Identified 16 rare variants in 15 genes with extreme carrier statistics in Alzheimer's disease.
• Observed a significant enrichment of rare variants in top-prioritized genes among patients compared to healthy individuals.

Conclusions:

• Carrier statistic is a powerful tool for identifying functional rare variants in complex diseases.
• The method is adaptable to various rare variant types and omics data, aiding in understanding disease mechanisms.
• Highlights the potential of rare variants in Alzheimer's disease pathogenesis.