Work

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

Year: 2026

Type: article

Abstract: 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. A gradient boosting classifier achieved the highest performance, with a test AUC of 0.843 (95% CI, 0.702-0.948) and an accuracy of 81.5%. Reclassification of sporadic cases into the familial group ranged from 16.6% to 28.3%. The model identified a burden of biallelic heterozygous variants suggesting hidden recessive inheritance that may affect up to 38% of MD patients. Gene-enrichment analysis using major classifier features revealed distinct biological pathways between familial and sporadic MD. This ML-based framework integrates unique genetic features that may improve the diagnostic yield of rare disorders and is universally applicable to conditions with both familial and sporadic phenotypes, enabling the identification of compound heterozygous recessive inheritance.

Source: iScience

DOI: 10.1016/j.isci.2026.117838

Authors: Sayedali A. Mohseni, Muhammad Hamza B. Khan, Pablo Cruz-Granados, Kiana Bagheri-Lotfabad, Amin Beheshti, Jose A. Lopez-Escamez