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Record W2059471477 · doi:10.3389/fgene.2012.00291

miRNA-mediated risk for schizophrenia in 22q11.2 deletion syndrome

2012· article· en· W2059471477 on OpenAlexafffund
Linda M. Brzustowicz, Anne S. Bassett

Bibliographic record

VenueFrontiers in Genetics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthUniversity Health Network
FundersNational Institute of Mental HealthNational Institute for Health and Care ResearchCanadian Institutes of Health ResearchCanada Research ChairsHealth CanadaNational Institutes of Health
KeywordsSchizophrenia (object-oriented programming)microRNABiologyGeneticsComputational biologyMedicinePsychiatryGene

Abstract

fetched live from OpenAlex

In humans, the most common genomic disorder is a hemizygous deletion of a 1.5-3 Mb region of chromosome 22q11.2. The resultant 22q11.2 deletion syndrome (22q11.2DS) can affect multiple organ systems, and most notably includes cardiac, craniofacial, and neurodevelopmental defects. Individuals with 22q11.2DS have a 20-25-fold risk of developing schizophrenia compared to individuals from the general population, making 22q11.2DS the strongest known molecular genetic risk factor for schizophrenia. Although the deleted region includes DGCR8, a gene coding for a miRNA processing protein, the exact mechanism by which this deletion increases risk is unknown. Importantly, several lines of evidence suggest that miRNAs may modulate risk for schizophrenia in other, non-22q11.2DS populations. Here we present a theory which mechanistically explains the link between 22q11.2DS, miRNAs, and schizophrenia risk. We outline the testable predictions generated by this theory and present preliminary data in support of our model. Further experimental validation of this model could provide important insights into the etiology of both 22q11.2DS and more common forms of schizophrenia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.263
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2012
Admission routes2
Has abstractyes

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