Differential Domain Distribution of gnomAD- and Disease-Linked Connexin Missense Variants
Bibliographic record
Abstract
Twenty-one human genes encode connexins, a family of homologous proteins making gap junction (GJ) channels, which mediate direct intercellular communication to synchronize tissue/organ activities. Genetic variants in more than half of the connexin genes are associated with dozens of different Mendelian inherited diseases. With rapid advances in DNA sequencing technology, more variants are being identified not only in families and individuals with diseases but also in people in the general population without any apparent linkage to Mendelian inherited diseases. Nevertheless, it remains challenging to classify the pathogenicity of a newly identified connexin variant. Here, we analyzed the disease- and Genome Aggregation Database (gnomAD, as a proxy of the general population)-linked variants in the coding region of the four disease-linked α connexin genes. We found that the most abundant and position-sensitive missense variants showed distinct domain distribution preference between disease- and gnomAD-linked variants. Plotting missense variants on topological and structural models revealed that disease-linked missense variants are highly enriched on the structurally stable/resolved domains, especially the pore-lining domains, while the gnomAD-linked missense variants are highly enriched in the structurally unstable/unresolved domains, especially the carboxyl terminus. In addition, disease-linked variants tend to be on highly conserved residues and those positions show evolutionary co-variation, while the gnomAD-linked missense variants are likely on less conserved residue positions and on positions without co-variation. Collectively, the revealed distribution patterns of disease- and gnomAD-linked missense variants further our understanding of the GJ structure-biological function relationship, which is valuable for classifying the pathogenicity of newly identified connexin variants.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".