# Gene Panel Approach to Screen for Hereditary Cerebral Small Vessel Disease: A Proof-of-Concept Study

## TL;DR

This study tests a gene panel to identify hereditary cerebral small vessel disease in patients, showing better results in those selected by a specific phenotype-based algorithm.

## Contribution

A high-throughput gene panel and phenotype-based algorithm were used to screen for hereditary cerebral small vessel disease with clinical relevance.

## Key findings

- Pathogenic CRGVs were found in 4 out of 22 high-probability group patients.
- VUSs were more common in the high-probability group compared to the low-probability group.
- The algorithm helps identify patients more likely to have monogenic disease.

## Abstract

Background: The predictive performance of pre-screening phenotype-based algorithms in selecting patients with cerebral small vessel disease (cSVD), one of the main causes of ischaemic and haemorrhagic stroke and dementia, more likely to harbor clinically relevant genetic variants (CRGVs) has to date been poorly defined, making it a clinical challenge to decide which patients to screen for hereditary cSVD (hcSVD). Methods: We designed a high-throughput gene panel to identify variants in 27 candidate genes associated with cSVD and screened patients selected by a specific phenotype-based algorithm at one comprehensive stroke center from 2020 to 2023. We categorized participants into two sub-groups defined by pre-screening likelihood of hcSVD (hcSVD; High-Probability Group, HPG vs. Low-Probability Group, LPG) and compared the results of molecular analysis. Results: Among 65 probands, we detected four (6.1%) pathogenic CRGVs and seven (10.7%) variants of unknown significance (VUSs) in 11 (16.9%) patients. Pathogenic CRGVs were exclusively detected in the HPG (4/22 probands), corresponding to an 18.2% prevalence of hcSVD in this group. Of the seven VUSs, five (22.7%) were detected in the HPG vs. two (4.6%) in the LPG. Conclusions: The pragmatic algorithm we are proposing has the potential to help clinicians in identifying patients who are more likely to harbor monogenic disease.