Design and rationale of a genetic cohort study on congenital cardiac disease: experiences from a multi-institutional platform in Quebec
Bibliographic record
Abstract
BACKGROUND: Congenital cardiac disease is the most common malformation, and a substantial source of mortality and morbidity in children and young adults. A role for genetic factors is recognised for these malformations, but overall few predisposing loci have been identified. Here we report the rationale, design, and first results of a multi-institutional congenital cardiac disease cohort, assembled mainly from the French-Canadian population of the province of Quebec and centred on families with multiple affected members afflicted by cardiac malformations. METHODS: Families were recruited into the study, phenotyped and sampled for DNA in cardiology clinics over the first 3 years of enrolment. We performed segregation analysis and linkage simulations in the subgroup of families with left ventricular outflow tract obstruction (LVOTO). RESULTS: A total of 1603 participants from 300 families were recruited, with 169 out of 300 (56.3%) families having more than one affected member. For the LVOTO group, we estimate heritability to be 0.46-0.52 in our cohort. Simulation analysis demonstrated sufficient power to carry out linkage analyses, with an expected mean log-of-odds (LOD) score of 3.8 in 67 pedigrees with LVOTO. CONCLUSION: We show feasibility and usefulness of a population-based biobank for genetic investigations into the causes of congenital cardiac disease. Heritability of LVOTO is high and could be accounted for by multiple loci. This platform is ideally suited for multiple analysis approaches, including linkage analysis and novel gene sequencing approaches, and will allow to establish segregation of risk alleles at family and population levels.
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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 | 0.024 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".