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Record W2167812429 · doi:10.1093/hmg/ddi489

DNA copy-number analysis in bipolar disorder and schizophrenia reveals aberrations in genes involved in glutamate signaling

2006· article· en· W2167812429 on OpenAlexafffund
Gary M. Wilson, Stéphane Flibotte, Vikramjit Chopra, Brianna Leigh Melnyk, William G. Honer, Robert A. Holt

Bibliographic record

VenueHuman Molecular Genetics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity of British ColumbiaCanada's Michael Smith Genome Sciences Centre
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCStanley Medical Research Institute
KeywordsBiologyCopy-number variationGeneticsGeneBipolar disorderChromosomeGenomeNeuroscience

Abstract

fetched live from OpenAlex

Using bacterial artificial chromosome (BAC) array comparative genome hybridization (aCGH) at approximately 1.4 Mbp resolution, we screened post-mortem brain DNA from bipolar disorder cases, schizophrenia cases and control individuals (n=35 each) for DNA copy-number aberrations. DNA copy number is a largely unexplored source of human genetic variation that may contribute risk for complex disease. We report aberrations at four loci which were seen in affected but not control individuals, and which were verified by quantitative real-time PCR. These aberrant loci contained the genes encoding EFNA5, GLUR7, CACNG2 and AKAP5; all brain-expressed proteins with known or postulated roles in neuronal function, and three of which (GLUR7, CACNG2 and AKAP5) are involved in glutamate signaling. A second cohort of psychiatric samples was also tested by quantitative PCR using the primer/probe sets for EFNA5, GLUR7, CACNG2 and AKAP5, and samples with aberrant copy number were found at three of the four loci (GLUR7, CACNG2 and AKAP5). Further scrutiny of these regions may reveal insights into the etiology and genetic risk factors for these complex psychiatric disorders.

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 score0.385
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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

Citations170
Published2006
Admission routes2
Has abstractyes

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