Phenotypic Heterogeneity of Genomic Disorders and Rare Copy-Number Variants
Bibliographic record
Abstract
BACKGROUND: Some copy-number variants are associated with genomic disorders with extreme phenotypic heterogeneity. The cause of this variation is unknown, which presents challenges in genetic diagnosis, counseling, and management. METHODS: We analyzed the genomes of 2312 children known to carry a copy-number variant associated with intellectual disability and congenital abnormalities, using array comparative genomic hybridization. RESULTS: Among the affected children, 10.1% carried a second large copy-number variant in addition to the primary genetic lesion. We identified seven genomic disorders, each defined by a specific copy-number variant, in which the affected children were more likely to carry multiple copy-number variants than were controls. We found that syndromic disorders could be distinguished from those with extreme phenotypic heterogeneity on the basis of the total number of copy-number variants and whether the variants are inherited or de novo. Children who carried two large copy-number variants of unknown clinical significance were eight times as likely to have developmental delay as were controls (odds ratio, 8.16; 95% confidence interval, 5.33 to 13.07; P=2.11×10(-38)). Among affected children, inherited copy-number variants tended to co-occur with a second-site large copy-number variant (Spearman correlation coefficient, 0.66; P<0.001). Boys were more likely than girls to have disorders of phenotypic heterogeneity (P<0.001), and mothers were more likely than fathers to transmit second-site copy-number variants to their offspring (P=0.02). CONCLUSIONS: Multiple, large copy-number variants, including those of unknown pathogenic significance, compound to result in a severe clinical presentation, and secondary copy-number variants are preferentially transmitted from maternal carriers. (Funded by the Simons Foundation Autism Research Initiative and the National Institutes of Health.).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".