Genetic associations with parental investment from conception to wealth inheritance in six cohorts
Bibliographic record
Abstract
Genetic inheritance is not the only way parents' genes may affect children. It is also possible that parents' genes are associated with investments into children's development. We examined evidence for links between parental genetics and parental investments, from the prenatal period through to adulthood, using data from six population-based cohorts in the UK, US and New Zealand, together totalling 36,566 parents. Our findings revealed associations between parental genetics-summarized in a genome-wide polygenic score-and parental behaviour across development, from smoking in pregnancy, breastfeeding in infancy, parenting in childhood and adolescence, to leaving a wealth inheritance to adult children. Effect sizes tended to be small at any given time point, ranging from RR = 1.12 (95% confidence interval (95%CI) 1.09, 1.15) to RR = 0.76 (95%CI 0.72, 0.80) during the prenatal period and infancy; β = 0.07 (95%CI 0.04, 0.11) to β = 0.29 (95%CI 0.27, 0.32) in childhood and adolescence, and RR = 1.04 (95%CI 1.01, 1.06) to RR = 1.11 (95%CI 1.07, 1.15) in adulthood. There was evidence for accumulating effects across development, ranging from β = 0.15 (95%CI 0.11, 0.18) to β = 0.23 (95%CI 0.16, 0.29) depending on cohort. Our findings are consistent with the interpretation that parents pass on advantages to offspring not only via direct genetic transmission or purely environmental paths, but also via genetic associations with parental investment from conception to wealth inheritance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".