Epigenetic variance in dopamine D2 receptor: a marker of IQ malleability?
Bibliographic record
Abstract
Genetic and environmental factors both contribute to cognitive test performance. A substantial increase in average\nintelligence test results in the second half of the previous century within one generation is unlikely to be explained by\ngenetic changes. One possible explanation for the strong malleability of cognitive performance measure is that\nenvironmental factors modify gene expression via epigenetic mechanisms. Epigenetic factors may help to understand\nthe recent observations of an association between dopamine-dependent encoding of reward prediction errors and\ncognitive capacity, which was modulated by adverse life events. The possible manifestation of malleable biomarkers\ncontributing to variance in cognitive test performance, and thus possibly contributing to the\n“\nmissing heritability\n”\nbetween estimates from twin studies and variance explained by genetic markers, is still unclear. Here we show in 1475\nhealthy adolescents from the IMaging and GENetics (IMAGEN) sample that general IQ (gIQ) is associated with (1)\npolygenic scores for intelligence, (2) epigenetic modi\nfi\ncation of\nDRD2\ngene, (3) gray matter density in striatum, and (4)\nfunctional striatal activation elicited by temporarily surprising reward-predicting cues. Comparing the relative\nimportance for the prediction of gIQ in an overlapping subsample, our results demonstrate neurobiological correlates\nof the malleability of gIQ and point to equal importance of genetic variance, epigenetic modi\nfi\ncation of DRD2 receptor\ngene, as well as functional striatal activation, known to in\nfl\nuence dopamine neurotransmission. Peripheral epigenetic\nmarkers are in need of con\nfi\nrmation in the central nervous system and should be tested in longitudinal settings\nspeci\nfi\ncally assessing individual and environmental factors that modify epigenetic structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".