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Record W1963495265 · doi:10.1002/ajmg.b.30380

Further evidence of MAO‐A gene variants associated with bipolar disorder

2006· article· en· W1963495265 on OpenAlexafffundabout
Daniel J. Müller, Alessandro Serretti, Tricia Sicard, Subi Tharmalingam, Nicole King, Paola Artioli, Laura Mandelli, Cristina Lorenzi, James L. Kennedy

Bibliographic record

VenueAmerican Journal of Medical Genetics Part B Neuropsychiatric Genetics · 2006
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsBipolar disorderHaplotypeAlleleMedicineGeneticsPsychiatryGeneBiology

Abstract

fetched live from OpenAlex

The aim of this study was to investigate MAOA gene variants in bipolar disorder by using a family-based association approach. The first sample included 331 nuclear families from Western and Central Canada with at least 1 offspring affected with bipolar disorder comprising a total of 1,044 individuals. All subjects were genotyped for MAOA-941T > G and -uVNTR gene variants using PCR techniques. Haplotype TDT was statistically significant (LRS = 12.17; df = 3; P = 0.0068; permutation global significance = 0.00098), with the T-4 haplotype significantly associated with bipolar disorder (OR = 1.63, 95% CI = 1.11-2.37). Single marker analysis evidenced a borderline association for MAOA-941T > G (P = 0.04), but not for the uVNTR. Pooling the Canadian sample with a second previously reported Italian sample genotyped for the uVNTR variant, negative results were obtained as well. No different results were detected when analyzing female subjects separately. In conclusion, our family-based association study gives mild but further support of the involvement of MAOA variants in bipolar disorder.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.267
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 source (direct Gemma or distilled Codex), 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

Citations30
Published2006
Admission routes3
Has abstractyes

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