Deleterious variants in <i><scp>DCHS</scp>1</i> are prevalent in sporadic cases of mitral valve prolapse
Bibliographic record
Abstract
BACKGROUND: A recent study identified DCHS1 as a causal gene for mitral valve prolapse. The goal of this study is to investigate the presence and frequency of known and novel variants in this gene in 100 asymptomatic patients with moderate to severe organic mitral regurgitation. METHODS: DNA sequencing assays were developed for two previously identified functional missense variants, namely p.R2330C and p.R2513H, and all 21 exons of DCHS1. Pathogenicity of variants was evaluated in silico. RESULTS: p.R2330C and p.R2513H were not identified in this cohort. Sequencing all coding regions revealed eight missense variants including six considered deleterious. This includes one novel variant (p.A2464P) and two rare variants (p.R2770Q and p.R2462Q). These variants are predicted to be deleterious with combined annotation-dependent depletion (CADD) scores greater than 25, which are in the same range as p.R2330C (CADD = 28.0) and p.R2513H (CADD = 24.3). More globally, 24 of 100 cases were carriers of at least one in silico-predicted deleterious missense variant in DCHS1, suggesting that this single gene may account for a substantial portion of cases. CONCLUSION: This study reveals an important contribution of germline variants in DCHS1 in unrelated patients with mitral valve prolapse and supports genetic testing of this gene to screen individuals at risk.
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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) | 0.002 | 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".