# Prioritizing disease-related rare variants by integrating gene expression data

## TL;DR

This paper introduces a new method to identify rare genetic variants linked to diseases by combining genetic and gene expression data.

## Contribution

The novel contribution is the development of the carrier statistic framework for prioritizing disease-related rare variants.

## Key findings

- The carrier statistic method achieves higher sensitivity in identifying rare variants with functional consequences.
- Application to Alzheimer’s disease identified 16 rare variants in 15 genes with extreme carrier statistics.
- Top prioritized genes showed a strong excess of rare variants in diseased patients compared to healthy individuals.

## Abstract

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer’s disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.