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Record W2886944964 · doi:10.1017/s0954579418000378

Polygenic differential susceptibility to prenatal adversity

2018· article· en· W2886944964 on OpenAlexaff
Jay Belsky, Irina Pokhvisneva, Anu Sathyan Sathyapalan Rema, Birit F. P. Broekman, Michael Pluess, Kieran J. O’Donnell, Michael J. Meaney, Patrícia Pelufo Silveira

Bibliographic record

VenueDevelopment and Psychopathology · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsDiathesisDiathesis–stress modelPsychologyDevelopmental psychologyGene–environment interactionClinical psychologyPrenatal stressDifferential (mechanical device)Serotonin transporterPregnancyGeneMedicineOffspringGeneticsGenotype

Abstract

fetched live from OpenAlex

A recent article in this journal reported a number of gene × environment interactions involving a serotonin transporter-gene network polygenic score and a composite index of prenatal adversity predicting several problem behavior outcomes at 48 months (e.g., anxious/depressed, pervasive developmental problems) and at 60 months (e.g., withdrawal, internalizing problems), yet did not illuminate the nature or form these genetic × environment interactions took. Here we report results of six additional analyses to evaluate whether these interactions reflected diathesis-stress or differential-susceptibility related processes. Analyses of the regions of significance and proportion of interaction index are consistent with the diathesis-stress model, seemingly because of the truncated nature of the adversity score (which did not extend to supportive/positive prenatal experiences/exposures); in contrast, the proportion (of cases) affected index favors the differential-susceptibility model. These results suggest the need for future studies to extend measurement of the prenatal environment to highly supportive experiences and exposures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.999

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)0.0020.002

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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2018
Admission routes1
Has abstractyes

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