Assessment of the ExAC data set for the presence of individuals with pathogenic genotypes implicated in severe Mendelian pediatric disorders
Bibliographic record
Abstract
PurposeWe analyzed the Exome Aggregation Consortium (ExAC) data set for the presence of individuals with pathogenic genotypes implicated in Mendelian pediatric disorders.MethodsClinVar likely/pathogenic variants supported by at least one peer-reviewed publication were assessed within the ExAC database to identify individuals expected to exhibit a childhood disorder based on concordance with disease inheritance modes: heterozygous (for dominant), homozygous (for recessive) or hemizygous (for X-linked recessive conditions). Variants from 924 genes reported to cause Mendelian childhood disorders were considered.ResultsWe identified ExAC individuals with candidate pathogenic genotypes for 190 previously published likely/pathogenic variants in 128 genes. After curation, we determined that 113 of the variants have sufficient support for pathogenicity and identified 1,717 ExAC individuals (~2.8% of the ExAC population) with corresponding possible/disease-associated genotypes implicated in rare Mendelian disorders, ranging from mild (e.g., due to SCN2A deficiency) to severe pediatric conditions (e.g., due to FGFR1 deficiency).ConclusionLarge-scale sequencing projects and data aggregation consortia provide unprecedented opportunities to determine the prevalence of pathogenic genotypes in unselected populations. This knowledge is crucial for understanding the penetrance of disease-associated variants, phenotypic variability, somatic mosaicism, as well as published literature curation for variant classification procedures and predicted clinical outcomes.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".