The Genetics of Dilated Cardiomyopathy: A Prioritized Candidate Gene Study of <i><scp>LMNA</scp></i>, <i><scp>TNNT2</scp></i>, <i><scp>TCAP</scp></i>, and <scp><i>PLN</i></scp>
Bibliographic record
Abstract
BACKGROUND: Dilated cardiomyopathy (DCM), which is characterized by left ventricular enlargement and systolic dysfunction, is divided into cases with a clear predisposing condition (eg, hypothyroidism, chemotherapeutic agents, alcoholism, ischemia) and those of unknown cause (idiopathic DCM). Many cases (20%-35%) of DCM are familial, implicating a genetic contribution to the etiology. More than 30 genes have been identified, many involving "private" mutations not shared among families. Evidence suggests that nonfamilial cases also have a genetic predisposition, again involving many genes. The goal of this study was to identify mutations in genes associated with DCM in a Québec study sample including familial and nonfamilial DCM cases. HYPOTHESIS: A prioritized gene study conducted within a framework for the classification of identified genetic variants could yield etiological information even in the absence of family data. METHODS: We sequenced 4 previously identified genes: lamin A/C (LMNA), cardiac troponin T type 2 (TNNT2), titin-cap (TCAP), and phospholamban (PLN). RESULTS: We discovered a nonsense mutation in the LMNA gene and a frameshift mutation in the TNNT2 gene, as well as other clinically significant variants that were not observed in publicly available databases or in Québec-based controls. PLN was sequenced to investigate a previously published promoter variant. However, our data confirm that this variant does not have a causal role in DCM. CONCLUSIONS: Despite high locus and allele heterogeneity, we demonstrate that a prioritized gene study, combined with next-generation exome-sequencing data, can be fruitful for the identification of DCM mutations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".