(Epi)genomics and neurodevelopment in schizophrenia: monozygotic twins discordant for schizophrenia augment the search for disease-related (epi)genomic alterationsBased on a lecture during the joint meeting of the Genetics Society of Canada and the Society of Developmental Biology, 1 March 2008, at Banff Centre, Banff, Alberta, Canada, in recognition of the William F. Grant and Peter B. Moens Award of Excellence (2008) of the Genetics Society of Canada to Professor Shiva M. Singh.
Bibliographic record
Abstract
Unlike stunning breakthroughs in the identification of genes for Mendelian disorders during the last three decades, gene identification in most complex disorders has been full of twists and turns and little progress. Doing more of the same will not guarantee success. The lessons learned argue for a need to reconsider genetic models that are appropriate for the disorder in question along with an interdisciplinary, systematic approach using genomic methods that have now become possible. We will use schizophrenia as an example to review the genetic progress to date that has been disappointing. We will argue that the causation of this complex disease may involve heterogeneous genomic changes of major effect. We will provide three approaches, retroviral transpositions, methylation, and copy number variations, to test this hypothesis. We will present arguments to suggest that such experiments will be most effective if undertaken on monozygotic twins. It will include our experience with associated experiments on the monozygotic twins discordant for schizophrenia. The results support that (epi)genomic changes of major effect, rather than accumulation of mutations of small effect, underlie the causation of this complex disease. More important, this experimental strategy will be an effective strategy for studies on other complex (behavioural) disorders as well.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".