VarScan2 analysis of de novo variants in monozygotic twins discordant for schizophrenia
Bibliographic record
Abstract
OBJECTIVES: Monozygotic twins with near-identical genotypes and discordance for complex diseases represent an exceptional resource to ascertain disease etiology. This strategy has been particularly effective with the availability of high-resolution complete individual genome sequencing. The challenge is using effective approaches to identify relevant differences that may cause or contribute toward disease discordance. PARTICIPANTS AND METHODS: This study carried out a VarScan2 bioinformatic analysis and a pathway analysis on whole-genome sequences from two sets of monozygotic twins. RESULTS: Variants were identified that were present in the affected twin, but not found in the unaffected twin. Such variations are expected to be de novo and originate during the independent development of the twins and may make them discordant for the disease. The genes and de novo variants identified in this experiment are compatible with their involvement in schizophrenia. Further analysis of the variants identified pathways including glutamate receptor signaling that have been implicated in this neurodevelopmental disease. CONCLUSION: The results support the polygenic nature of schizophrenia and the threshold model for its development. The results also show the effectiveness of VarScan2 to identify 'the needle in the hay stack' that may cause schizophrenia, specifically in the two patients. It offers a proof of principle for assessment of the genetic etiology of complex disorders where discordance of monozygotic twins is an established phenomenon.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".