An investigation into DNA methylation patterns associated with risk preference in older individuals
Bibliographic record
Abstract
Risk preference is a complex trait governed by psycho-social, environmental and genetic determinants. We aimed to examine how an individual’s risk preference associates with their epigenetic profile.Risk preferences were ascertained by asking participants of the Northern Ireland COhort for the Longitudinal study of Ageing to make a series of choices between hypothetical income scenarios. From these, four risk preference categories were derived, ranging from risk-averse to risk-seeking. Illumina’s Infinium High-Density Methylation Assay was used to evaluate the status of 862,927 CpGs.Risk preference and DNA methylation data were obtained for 1,656 individuals. The distribution of single-site DNA methylation levels between risk-averse and risk-seeking individuals was assessed whilst adjusting for age, sex and peripheral white cell counts. In this discovery cohort, 55 CpGs were identified with significantly different levels of methylation (p≤x10−5) between risk-averse and risk-seeking individuals when adjusting for the maximum number of covariates. No CpGs were significantly differentially methylated in any of the risk preference groups at an epigenome-wide association level (p<9x10−8) following covariate adjustment.Protein-coding genes NWD1 and LRP1 were among the genes in which the top-ranked dmCpGs were located for all analyses conducted. Mutations in these genes have previously been linked to neurological conditions.Epigenetic modifications have not previously been linked to risk-aversion using a population cohort, but may represent important biomarkers of accumulated, complex determinants of this trait. Several striking results from this study support further analysis of DNA methylation as an important link between measurable biomarkers and health behaviours.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".