Transpersonal Genetic Effects Among Older U.S. Couples: A Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Emerging social genetics research suggests one's genes may influence not just one's own outcomes but also those of close social alters. Health implications, particularly in late life, remain underexplored. Using combined genetic and survey data, this study examined such transpersonal genetic associations among older U.S. couples. METHOD: Data were from married or cohabiting couples in the 2006-2016 waves of the Health and Retirement Study, nationally representative of U.S. adults over 50. Measures included a polygenic score for educational attainment, and self-rated health. Analysis was through parallel process latent growth models. RESULTS: Women's and men's genetic scores for education had transpersonal linkages with their partner's health. Such associations were solely with life-course variations and not late-life change in outcomes. Moreover, they were indirect, mediated by educational attainment itself. Evidence also emerged for individual-level genetic effects mediated by the partner's education. DISCUSSION: In addition to the subject-specific linkages emphasized in extant genetics literature, relational contexts involve multiple transpersonal genetic associations. These appear to have consequences for a partner's and one's own health. Life-course theory indicates that a person is never not embedded in such contexts, suggesting that these patterns may be widespread. Research is needed on their implications for the life-course and gene-environment correlation literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".