Genetic parameters of personality traits in dogs based on behavioral assessment and questionnaire information
Bibliographic record
Abstract
A new behavior and personality assessment in dogs (BPH) was created in Sweden in 2012. Since the start of BPH, questionnaire data based on an extended version of C-BARQ have been collected to describe the everyday behavior of dogs. Our aim was to estimate genetic parameters for personality traits based on BPH or questionnaire data for eight breeds: American Staffordshire Terrier, Golden Retriever, Labrador Retriever, Lagotto Romagnolo, Nova Scotia Duck Tolling Retriever, Perro de Agua Español, Rhodesian Ridgeback, and Staffordshire Bullterrier. The number of BPH records ranged from 862 for Lagotto Romagnolo to 2462 for Labrador Retriever. Average heritability across all breeds was 0.22 for Sociability, 0.23 for Playfulness, 0.16 for Non-social fearfulness, 0.26 for Aggressiveness, and 0.22 for Boldness; all traits defined based on BPH. The genetic variation between breeds was larger than the genetic variation within breeds for Sociability, Playfulness, and Boldness. Estimates of heritability for questionnaire traits were more variable, possibly owing to fewer observations, and averages ranged from 0.06 to 0.28. Genetic correlations between BPH traits, on the one hand, and corresponding questionnaire traits, on the other hand, were consistently strong between Sociability and Stranger-directed interest (average 0.93) and Stranger-directed fear (-0.89); between Playfulness and Human-directed play interest (0.77); between Non-social fearfulness and Non-social fear (0.77); between Aggressiveness and Stranger-directed aggression (0.57); and between Boldness and Stranger-directed interest (0.78) and Stranger-directed fear (-0.80). Often, we could also see that a measure at BPH involving human interactions also extended to measure reactions to interactions with other dogs, e.g., a strong genetic correlation between Sociability and Dog-directed interest (0.58). In summary, selecting dogs based on traits defined at BPH is expected to result in behavioral changes not only when measured at BPH but also changes in everyday behavior in an expected way. • Are behavior traits at test heritable and correlated with everyday behavior? • We used mixed linear models to estimate heritability and genetic correlations. • Heritabilities for behavioral traits were high enough to make selection feasible. • Behavior at test reflects behavior in everyday environments. • Selection for favorable behavior at test will change behavior at home.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".