Spectrum and Frequency of the GJB2 Gene Pathogenic Variants in a Large Cohort of Patients with Hearing Impairment Living in a Subarctic Region of Russia (the Sakha Republic)
Bibliographic record
Abstract
Pathogenic variants in the GJB2 gene, encoding connexin 26, are known to be a major cause of hearing impairment (HI). More than 300 allelic variants have been identified in the GJB2 gene. Spectrum and allelic frequencies of the GJB2 gene vary significantly among different ethnic groups worldwide. Until now, the spectrum and frequency of the pathogenic variants in exon 1, exon 2 and the flanking intronic regions of the GJB2 gene have not been described thoroughly in the Sakha Republic (Yakutia), which is located in a subarctic region in Russia. The complete sequencing of the non-coding and coding regions of the GJB2 gene was performed in 393 patients with HI (Yakuts-296, Russians-51, mixed and other ethnicities-46) and in 187 normal hearing individuals of Yakut (n = 107) and Russian (n = 80) populations. In the total sample (n = 580), we revealed 12 allelic variants of the GJB2 gene, 8 of which were recessive pathogenic variants. Ten genotypes with biallelic recessive pathogenic variants in the GJB2 gene (in a homozygous or a compound heterozygous state) were found in 192 out of 393 patients (48.85%). We found that the most frequent GJB2 pathogenic variant in the Yakut patients was c.-23+1G>A (51.82%) and that the second most frequent was c.109G>A (2.37%), followed by c.35delG (1.64%). Pathogenic variants с.35delG (22.34%), c.-23+1G>A (5.31%), and c.313_326del14 (2.12%) were found to be the most frequent among the Russian patients. The carrier frequencies of the c.-23+1G>A and с.109G>A pathogenic variants in the Yakut control group were 10.20% and 2.80%, respectively. The carrier frequencies of с.35delG and c.101T>C were identical (2.5%) in the Russian control group. We found that the contribution of the GJB2 gene pathogenic variants in HI in the population of the Sakha Republic (48.85%) was the highest among all of the previously studied regions of Asia. We suggest that extensive accumulation of the c.-23+1G>A pathogenic variant in the indigenous Yakut population (92.20% of all mutant chromosomes in patients) and an extremely high (10.20%) carrier frequency in the control group may indicate a possible selective advantage for the c.-23+1G>A carriers living in subarctic climate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".