Identification of candidate intergenic risk loci in autism spectrum disorder
Bibliographic record
Abstract
BACKGROUND: Copy number variations (CNVs) and DNA sequence alterations affecting specific neuronal genes are established risk factors for Autism Spectrum Disorder (ASD). In what is largely considered a genetic condition, so far, these mutations account for ~20% of individuals having an ASD diagnosis. However, non-coding genomic sequence also contains functional elements introducing additional disease risk loci for investigation. RESULTS: We have performed genome-wide analyses and identified rare inherited CNVs affecting non-genic intervals in 41 of 1491 (3%) of ASD cases examined. Examples of such intergenic CNV regions include 16q21 and 2p16.3 near known ASD risk genes CDH8 and NRXN1 respectively, as well as novel loci contiguous with ZHX2, MOCS1, LRRC4C, SEMA3C, and other genes. CONCLUSIONS: Rare variants in intergenic regions may implicate new risk loci and genes in ASD and also present useful data for comparison with coming whole genome sequence datasets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".