Critical exon indexing improves clinical interpretation of copy number variants in neurodevelopmental disorders
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Bibliographic record
Abstract
<h3>Objective</h3> To evaluate a new tool to aid interpretation of copy number variants (CNVs) in individuals with neurodevelopmental disabilities. <h3>Methods</h3> Critical exon indexing (CEI) was used to identify genes with critical exons (CEGs) from clinically reported CNVs, which may contribute to neurodevelopmental disorders (NDDs). The 742 pathogenic CNVs and 1,363 variants of unknown significance (VUS) identified by chromosomal microarray analysis in 5,487 individuals with NDDs were subjected to CEI to identify CEGs. CEGs identified in a subsequent random series of VUS were evaluated for relevance to CNV interpretation. <h3>Results</h3> CEI identified a total of 2,492 unique CEGs in pathogenic CNVs and 953 in VUS compared with 259 CEGs in 6,965 CNVs from 873 controls. These differences are highly significant (<i>p</i> < 0.00001) whether compared as frequency, average, or normalized by CNV size. Twenty-one percent of VUS CEGs were not represented in Online Mendelian Inheritance in Man, highlighting limitations of existing resources for identifying potentially impactful genes within CNVs. CEGs were highly correlated with other indices and known pathways of relevance. Separately, 136 random VUS reports were reevaluated, and 76% of CEGs had not been commented on. In multiple cases, further investigation yielded additional relevant literature aiding interpretation. As one specific example, we discuss <i>GTF2I</i> as a CEG, which likely alters interpretation of several reported duplication VUS in the Williams-Beuren region. <h3>Conclusions</h3> Application of CEI to CNVs in individuals with NDDs can identify genes of potential clinical relevance, aid laboratories in effectively searching the clinical literature, and support the clinical reporting of poorly annotated VUS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it