Reduced penetrance of <i>COL1A1/2</i> pathogenic variants linked with osteogenesis imperfecta: analysis of a large population cohort
Bibliographic record
Abstract
Osteogenesis imperfecta (OI) is under consideration for inclusion in several genomic newborn screening initiatives, but its penetrance in clinically-unselected populations is currently unknown. It is an exemplar condition for evaluating penetrance in adult cohorts due to its relatively low mortality, variable expressivity and link to several large genes. Using genome sequencing data from ~500,000 adults in UK Biobank, we curated a set of rare pathogenic/likely pathogenic (P/LP) variants in COL1A1, COL1A2 and IFITM5 using annotations from gnomAD, ClinVar and SpliceAI. Analysis of summed read-count data from genome and exome sequencing led to exclusion of 16 mosaic variants with consistently low allelic balance of 3.4-35.9%. We identified 61 likely constitutive heterozygous P/LP variants in COL1A1 and COL1A2 (29 loss-of-function or splice variants and 32 missense) in 115 participants, with a mean age at recruitment of 55.1 years; no P/LP variants were identified in IFITM5. Phenotypes were assessed using ICD-10 codes, self-reports and heel bone mineral density (BMD). Overall disease penetrance was lower than anticipated: 40.7% for COL1A1 and 21.3% for COL1A2, potentially due to depletion of severe early-onset disease. When considering only truncating variants in COL1A1, disease penetrance increased to 73.1%, and 90% of individuals had reduced levels of circulating COL1A1 protein. For COL1A2, BMD data supported the low penetrance, whereas for COL1A1, data suggested the possibility of a subclinical phenotype. Overall, for P/LP missense variants (including those altering Gly-Xaa-Yaa repeats), the low penetrance observed suggests reliance on current ClinVar assertions to support pathogenicity may overstate OI risk in population screening.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".