# SpliPath enhances disease gene discovery in case-control analyses of rare splice-altering genetic variants

## TL;DR

SpliPath is a new tool that helps find rare genetic variants linked to diseases by analyzing how they affect mRNA splicing.

## Contribution

SpliPath introduces a novel framework combining burden tests, RNA-seq, and AI models to detect disease associations from rare splicing variants.

## Key findings

- SpliPath identifies disease gene associations missed by conventional burden testing methods.
- The tool detects crsQTL in ALS patient tissue, linking rare variants to shared splice junctions.
- SpliPath outperforms simpler combinations of burden testing and AI models in real-world data.

## Abstract

We developed SpliPath as a generalizable framework to discover disease associations mediated by rare variants that induce experimentally supported mRNA splicing defects. Our approach integrates components of burden tests (BTs), traditional splicing quantitative trait locus (sQTL) analyses, and sequence-to-function AI models (SpliceAI and Pangolin). Central to the workings of SpliPath is our concept of collapsed rare variant splicing QTL (crsQTL). crsQTL groups rare variants that are predicted to alter splicing in the same way, specifically by linking them to shared splice junctions observed in independent (unpaired) RNA sequencing (RNA-seq) datasets. We demonstrate the utility of SpliPath through applications in amyotrophic lateral sclerosis (ALS). Through this, we showcase scenarios where SpliPath detects genetic associations that cannot be recovered by more simplistic combinations of BT and SpliceAI. We also nominate crsQTL for splice defects detected in large-scale analyses of ALS patient tissue.

•SpliPath is a tool to discover rare splicing defects associated with human disease•Combines rare variant burden testing, RNA-seq analyses, and sequence-to-function AI models•Detects disease gene associations missed by conventional gene burden testing•Identifies disease-associated collapsed rare variant splicing QTL (crsQTL) in ALS

SpliPath is a tool to discover rare splicing defects associated with human disease

Combines rare variant burden testing, RNA-seq analyses, and sequence-to-function AI models

Detects disease gene associations missed by conventional gene burden testing

Identifies disease-associated collapsed rare variant splicing QTL (crsQTL) in ALS

Bioinformatic analyses of RNA sequencing (RNA-seq) data can reveal rare splicing events in disease models and patient tissues, but it can be challenging to establish which events do and do not contribute to disease processes. Genetic analyses can be used to test for direct associations between disease risk and rare splice-altering DNA variants. For the latter, burden tests can aggregate statistical evidence across rare variants while sequence-to-function models (e.g., SpliceAI and Pangolin) can predict which rare variants disrupt splicing. However, here we demonstrate that simplistic combination of burden testing with SpliceAI can fail to recover important associations in real-world data. We therefore developed SpliPath, a tool that provides missing functionalities to interpret rare splicing events detected in RNA-seq by revealing rare variant disease associations in independent (unpaired) genetic datasets.

Wang et al. introduce SpliPath, a method to discover gene splicing defects associating with human disease using rare variant analyses. SpliPath combines independent genetic and transcriptomic datasets with sequence-to-function AI models, providing a tool to enhance discovery in rare variant disorders such as ALS.