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Record W2160153865 · doi:10.1038/srep00677

COMT Val158Met polymorphism interacts with stressful life events and parental warmth to influence decision making

2012· article· en· W2160153865 on OpenAlexaff
Qinghua He, Gui Xue, Chuansheng Chen, Zhong‐Lin Lu, Chunhui Chen, Xuemei Lei, Yuyun Liu, Jin Li, Bi Zhu, Robert K. Moyzis, Qi Dong, Antoine Bechara

Bibliographic record

VenueScientific Reports · 2012
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsDouglas Mental Health University Institute
FundersNational Cancer InstituteHigher Education Discipline Innovation ProjectNational Institute on Drug AbuseNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsIowa gambling taskPsychologyClinical psychologyEtiologyDevelopmental psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

Both genetic and environmental factors have been shown to influence decision making, but their relative contributions and interactions are not well understood. The present study aimed to reveal possible gene-environment interactions on decision making in a large healthy sample. Specifically, we examined how the frequently studied COMT Val(158)Met polymorphism interacted with an environmental risk factor (i.e., stressful life events) and a protective factor (i.e., parental warmth) to influence affective decision making as measured by the Iowa Gambling Task. We found that stressful life events acted as a risk factor for poor IGT performance (i.e., high reward sensitivity) among Met carriers, whereas parental warmth acted as a protective factor for good IGT performance (i.e., higher IGT score) among Val/Val homozygotes. These results shed some new light on gene-environment interactions in decision making, which could potentially help us understand the underlying etiology of several psychiatric disorders associated with decision making impairment.

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)0.002
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 score0.035
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.046
GPT teacher head0.413
Teacher spread0.368 · 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

Citations52
Published2012
Admission routes1
Has abstractyes

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