Exploratory research on genetic polymorphisms associated with positive empathy and trait forgivingness among the Japanese.
Bibliographic record
Abstract
OBJECTIVES: Previous studies have indicated that good human relationships contribute significantly to subjective well-being. We recently focused on two important ways of developing good interpersonal relationships: positive empathy, which focuses on the happiness of other people, and trait forgivingness, a tendency to forgive others. We novelly conducted an exploratory genome-wide association study (GWAS) to identify candidate gene polymorphisms associated with positive empathy and trait forgivingness among the Japanese. MATERIAL AND METHODS: We for the first time identified several genetic polymorphisms associated with positive empathy and trait forgivingness through the GWAS based on a small sample population and relatively low threshold. We subsequently validated three genetic polymorphisms from these candidate genes using a real-time polymerase chain reaction system. RESULTS: The results demonstrated that polymorphism in the vomeronasal type-1 receptor 1 (VN1R1) (rs61744949), a putative human pheromone receptor, is associated with positive empathy. In addition, genetic polymorphisms in the 5-hydroxytryptamine (serotonin) receptor 7 (HTR7: rs77843021) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon (YWHAE: rs9908013), which are associated with dopamine and serotonin biosynthesis, are associated with trait forgivingness. CONCLUSION: This study novelly illustrated the influence of the genetic polymorphism in VN1R1 on positive empathy and that of genetic polymorphisms in HTR7 and YWHAE on trait forgivingness. It identified a relationship between previously unreported genetic polymorphisms and the necessary abilities for developing good human relationships. This will significantly impact future research on positive psychology and social psychology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".