Genetics of monozygotic twins reveals the impact of environmental sensitivity on psychiatric and neurodevelopmental phenotypes
Bibliographic record
Abstract
Abstract Individual sensitivity to environmental exposures may be genetically influenced. This genotype-by-environment interplay implies differences in phenotypic variance across genotypes, but these variants have proven challenging to detect. Genome-wide association studies of monozygotic twin differences are conducted through family-based variance analyses, which are more robust to the systemic biases that impact population-based methods. We combined data from 21,792 monozygotic twins (10,896 pairs) from 11 studies to conduct one of the largest genome-wide association study meta-analyses of monozygotic phenotypic differences, in children, adolescents and adults separately, for seven psychiatric and neurodevelopmental phenotypes: attention deficit hyperactivity disorder symptoms, autistic traits, anxiety and depression symptoms, psychotic-like experiences, neuroticism and wellbeing. The proportions of phenotypic variance explained by single-nucleotide polymorphisms in these phenotypes were estimated ( h 2 = 0–18%), but were imprecise. We identified 13 genome-wide significant associations (single-nucleotide polymorphisms, genes and gene sets), including genes related to stress reactivity for depression, growth factor-related genes for autistic traits and catecholamine uptake-related genes for psychotic-like experiences. This is the largest genetic study of monozygotic twins to date by an order of magnitude, evidencing an alternative method to study the genetic architecture of environmental sensitivity. The statistical power was limited for some analyses, calling for better-powered future studies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".