Prioritizing disease-related rare variants by integrating gene expression data
Hanmin Guo1,2, Alexander Eckehart Urban2,3, Wing Hung Wong1,4
1Department of Statistics, Stanford University, Stanford, California 94305, USA.
Biorxiv : the Preprint Server for Biology
|April 2, 2024
Summary
Rare variants significantly impact diseases. Carrier statistic prioritizes these disease-related rare variants by analyzing gene expression, improving discovery in complex conditions like Alzheimer's disease.
Area of Science:
- Genetics and Genomics
- Computational Biology
- Molecular Medicine
Background:
- Rare genetic variants constitute the majority of human variations and are increasingly recognized for their significant impact on complex diseases.
- Identifying disease-causative rare variants is challenging due to their low frequency and the need for large sample sizes in traditional association studies.
Approach:
- We developed Carrier Statistic, a novel statistical framework integrating gene expression data to prioritize rare variants based on their functional impact in disease.
- This method quantifies the effect of rare variants on gene expression, identifying those with substantial functional consequences in affected individuals.
Key Points:
- Carrier Statistic demonstrates high sensitivity and applicability in studies with limited sample sizes (hundreds of individuals), outperforming existing rare variant association methods.
- Simulations and analysis of real multi-omics data validate the framework's robustness.
- Application to Alzheimer's disease identified 16 significant rare variants within 15 genes.
Conclusions:
- Carrier Statistic offers a powerful and adaptable tool for prioritizing disease-related rare variants by assessing their functional impact via gene expression.
- The method is versatile, applicable to various rare variant types and adaptable to different omics data modalities for studying complex diseases.
