A machine learning classifier reveals hidden rare biallelic heterozygous variants in the coding regions of Meniere disease genome

Meniere disease (MD) is a rare inner ear disorder with a significant genetic contribution. We developed the MD classifier, an ML tool to distinguish familial from sporadic MD cases using rare variants in coding regions. We have generated a disease-specific gene variant score incorporating gene, variant, and disease-specific features....

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No actionable change yet — the classifier is a research tool for genetic discovery and has not been validated for clinical diagnostic use in Meniere's disease patients.

Identifying genetic variants underlying Meniere's disease through machine learning could open pathways to targeted therapies and more precise genetic counseling for affected families.

1. 01A machine learning classifier was trained to distinguish familial from sporadic Meniere's disease.
2. 02The classifier identified rare biallelic heterozygous variants (paired mutations on both gene copies) in coding DNA regions.
3. 03Findings suggest a hidden genetic architecture contributing to Meniere's disease.
4. 04This is a genomic discovery study; clinical diagnostic validation has not yet been performed.
5. 05Could accelerate gene-therapy or precision-medicine approaches for Meniere's disease in the future.

PMID

42921002

DOI

10.1016/j.isci.2026.117838.

Journal

iScience

Publication type

research_article

Evidence level

4

Population

Patients with familial and sporadic Meniere's disease with available genomic data

Intervention

Machine learning classifier applied to Meniere's disease genome coding regions

Comparator

Sporadic Meniere's disease cases