Whole exome sequencing for the identification of novel susceptibility genes related to familial pulmonary fibrosis in a Newfoundland cohort
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a multifactorial, interstitial lung disease (ILD) which leads to the scarring and fibrosis of the alveolar interstitium. In the province of Newfoundland and Labrador, the prevalence of familial pulmonary fibrosis (FPF), in which two or more first degree relatives are affected, is high and consistent with strong genetic components segregating in this population. The study uses next generation sequencing to identify novel susceptibility genes for idiopathic pulmonary fibrosis. DNA samples from 24 patients from 14 different FPF families were analysed using whole exome sequencing. Of the 14 families sequenced, two families were selected for further analysis, R0942 and R1136. Using a filtering strategy that annotated genetic variants based on prevalence in variant databases and predicted phenotypic outcome using bioinformatics programs, a list of candidate gene variants was created. Furthermore, these variants were filtered based on functional gene annotation. Of interest were rare variants found in the genes CD109 and telomeric repeat-binding factor 1(TERF1) in families R1136 and R0942, respectively, that passed filtering criteria. The variants in CD109 (c.1474C>T; p.R492X) and TERF1, (c.311G>T; p.S104I) are thought to be involved in the regulation of the telomerase protein complex, whose reduced activity has been implicated in the development of IPF. Although neither variant completely segregated with the disease, several in silico programs support their pathogenicity and the variants appear to be rare in the general population. Functional assays of these variants will be required to accurately determine their phenotypic effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".