High diagnostic yield in skeletal ciliopathies using massively parallel genome sequencing, structural variant screening and RNA analyses
Bibliographic record
Abstract
Skeletal ciliopathies are a heterogenous group of disorders with overlapping clinical and radiographic features including bone dysplasia and internal abnormalities. To date, pathogenic variants in at least 30 genes, coding for different structural cilia proteins, are reported to cause skeletal ciliopathies. Here, we summarize genetic and phenotypic features of 34 affected individuals from 29 families with skeletal ciliopathies. Molecular diagnostic testing was performed using massively parallel sequencing (MPS) in combination with copy number variant (CNV) analyses and in silico filtering for variants in known skeletal ciliopathy genes. We identified biallelic disease-causing variants in seven genes: DYNC2H1, KIAA0753, WDR19, C2CD3, TTC21B, EVC, and EVC2. Four variants located in non-canonical splice sites of DYNC2H1, EVC, and KIAA0753 led to aberrant splicing that was shown by sequencing of cDNA. Furthermore, CNV analyses showed an intragenic deletion of DYNC2H1 in one individual and a 6.7 Mb de novo deletion on chromosome 1q24q25 in another. In five unsolved cases, MPS was performed in family setting. In one proband we identified a de novo variant in PRKACA and in another we found a homozygous intragenic deletion of IFT74, removing the first coding exon and leading to expression of a shorter message predicted to result in loss of 40 amino acids at the N-terminus. These findings establish IFT74 as a new skeletal ciliopathy gene. In conclusion, combined single nucleotide variant, CNV and cDNA analyses lead to a high yield of genetic diagnoses (90%) in a cohort of patients with skeletal ciliopathies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".