The natural history and genotype-phenotype correlations of TMPRSS3 hearing loss: an international, multi-center, cohort analysis.

TMPRSS3-related hearing loss presents challenges in correlating genotypic variants with clinical phenotypes due to the small sample sizes of previous studies. We conducted a cross-sectional genomics study coupled with retrospective clinical phenotype analysis on 127 individuals. These individuals were from 16 academic medical centers across 6 countries. Key findings revealed 47 unique TMPRSS3 variants with significant differences in hearing thresholds between those with missense variants versus those with loss-of-function genotypes. The hearing loss progression rate for the DFNB8 subtype was 0.3 dB/year. Post-cochlear implantation, an average word recognition score of 76% was observed. Of the 51 individuals with two missense variants, 10 had DFNB10 with profound hearing loss. These 10 all had at least one of 4 TMPRSS3 variants predicted by computational modeling to be damaging to TMPRSS3 structure and function. To our knowledge, this is the largest study of TMPRSS3 genotype-phenotype correlations. We find significant differences in hearing thresholds, hearing loss progression, and age of presentation, by TMPRSS3 genotype and protein domain affected. Most individuals with TMPRSS3 variants perform well on speech recognition tests after cochlear implant, however increased age at implant is associated with worse outcomes. These findings provide insight for genetic counseling and the on-going design of novel therapeutic approaches.

Using multi-scale genomics to associate poorly annotated genes with rare diseases.

BACKGROUND: Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes. METHODS: EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool. RESULTS: In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes. CONCLUSIONS: We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .