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Autism severity is associated with child and maternal MAOA genotypes

2010· article· en· W2028469253 on OpenAlexafffund
IL Cohen, X Liu, M. E. Suzanne Lewis, Albert E. Chudley, Cynthia Forster‐Gibson, Maripaz G. Gonzalez, EC Jenkins, WT Brown, JJA Holden

Bibliographic record

VenueClinical Genetics · 2010
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of ManitobaOntario GenomicsChild and Family Research InstituteUniversity of British ColumbiaQueen's University
FundersCanadian Institutes of Health Research
KeywordsMonoamine oxidase AAggressionAutismGenotypeAlleleOffspringAutism spectrum disorderPsychologyNeurodevelopmental disorderGeneticsPsychiatryClinical psychologyBiologyPregnancyGene

Abstract

fetched live from OpenAlex

Autism severity is associated with child and maternal MAOA genotypes. We replicated and extended a previously reported association between autism severity and a functional polymorphism in the monoamine oxidase A (MAOA) promoter region, MAOA-uVNTR, in a sample of 119 males, aged 2-13 years, with autism spectrum disorder from simplex families. We demonstrated that (i) boys with the low activity 3-repeat MAOA allele had more severe sensory behaviors, arousal regulation problems, and aggression, and worse social communication skills than males with the high activity allele; and (ii) problems with aggression, as well as with fears and rituals, were modified by the mothers' genotype. Boys with the 4-repeat high activity allele who had homozygous 4-repeat mothers showed increased severity of these behaviors relative to those born to heterozygous mothers. These findings indicate the importance of considering maternal genotype in examining associations of MAOA and other genes with behavior in male offspring.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.348
Teacher spread0.306 · 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

Citations74
Published2010
Admission routes2
Has abstractyes

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