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Record W2036894681 · doi:10.1159/000319948

Gender Differences in Cognitive Ability Associated with Genetic Variants of <i>NLGN4</i>

2010· article· en· W2036894681 on OpenAlexaff
Kejin Zhang, Xiaocai Gao, Hongbin Qi, Jing Li, Zijian Zheng, Fuchang Zhang

Bibliographic record

VenueNeuropsychobiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsHaplotypeAlleleCognitionGeneticsPsychologySingle-nucleotide polymorphismBiologyGeneGenotypeNeuroscience

Abstract

fetched live from OpenAlex

Neuroligin-4 (NL4), encoded by the NLGN4 gene on the X chromosome, is a neuronal-specific brain membrane protein which plays an important role in the formation of functional presynaptic elements and axon specialization. The genetic variants of NLGN4 affect the biological function of NL4, resulting in the manifestation of different psychiatric disorders. The present study investigates the influence of these genetic variants on cognitive performance. The cognitive abilities of 351 subjects were evaluated using the Chinese Wechsler Intelligence Scale Children. The haplotypes were assigned with the PHASE program. The ANOVA method was applied to investigate the relationship between single SNP, the identified target haplotypes and cognitive performance in a random sample. We observed that the X(C) allele of rs5916271 and X(A) allele of the re6638575 carriers had significantly higher cognitive ability performances than the noncarrier boys (p < 0.05). The target haplotype composed of 2 allele (X(CA+)) carriers also displayed a higher cognitive performance than that of the noncarriers boys. The genetic polymorphism of NLGN4 also had a significant effect on the boys' cognitive ability and other intelligence factors. Future research will involve determining the relationship between NLGN4 and personal cognitive ability.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.955

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 head0.014
GPT teacher head0.239
Teacher spread0.224 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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