Post‐zygotic genomic changes in glutamate and dopamine pathway genes may explain discordance of monozygotic twins for schizophrenia
Bibliographic record
Abstract
BACKGROUND: Monozygotic twins are valuable in assessing the genetic vs environmental contribution to diseases. In the era of complete genome sequences, they allow identification of mutational mechanisms and specific genes and pathways that offer predisposition to the development of complex diseases including schizophrenia. METHODS: We sequenced the complete genomes of two pairs of monozygotic twins discordant for schizophrenia (MZD), including one representing a family tetrad. The family specific complete sequences have allowed identification of post zygotic mutations between MZD genomes. It allows identification of affected genes including relevant network and pathways that may account for the diseased state in pair specific patient. RESULTS: We found multiple twin specific sequence differences between co-twins that included small nucleotides [single nucleotide variants (SNV), small indels and block substitutions], copy number variations (CNVs) and structural variations. The genes affected by these changes belonged to a number of canonical pathways, the most prominent ones are implicated in schizophrenia and related disorders. Although these changes were found in both twins, they were more frequent in the affected twin in both pairs. Two specific pathway defects, glutamate receptor signaling and dopamine feedback in cAMP signaling pathways, were uniquely affected in the two patients representing two unrelated families. CONCLUSIONS: We have identified genome-wide post zygotic mutations in two MZD pairs affected with schizophrenia. It has allowed us to use the threshold model and propose the most likely cause of this disease in the two patients studied. The results support the proposition that each schizophrenia patient may be unique and heterogeneous somatic de novo events may contribute to schizophrenia threshold and discordance of the disease in monozygotic twins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".