Evaluating the impact of compound heterozygosity involving microdeletions and sequence-level variants: findings in autism
Bibliographic record
Abstract
Abstract Compound heterozygous events involving a chromosome deletion and on the remaining allele a functional DNA sequence-level variant can underpin a range of medical conditions. Most large-scale genetic studies do not include a systematic analysis of such compound heterozygous deletion (DelCH) events. We developed three frameworks: i) traditional burden analysis; ii) deletion-matched burden analysis; and iii) transmission disequilibrium test (TDT), to examine the possible contribution of DelCH to clinical presentations, and report results of their implementation in 9,766 families of autistic individuals. Across the three strategies, we observed enrichment of rare DelCH events in autistic individuals at a nominal significance level for individual tests. Collectively, six genes; CFHR4 , HSDL1 , MYO15A , NEFH , and three olfactory receptor genes; OR1A2 , OR4P2 , were affected by DelCH events in at least two unrelated autistic individuals (and not in unaffected family members), while the reverse analyses identified no genes (p<2.2 x 10 -16 ). Gene set enrichment analysis of the extended network of candidate genes showing a remarkable convergence to processes related to neurogenesis. Our findings suggest a modest role for DelCH events in ASD. The strategies described here are available via a GitHub repository, allowing the research community to examine the role of DelCH in other genome sequencing cohorts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".