Breeding values and index creation for health and behavior traits in Labrador Retriever guide dogs
Bibliographic record
Abstract
Introduction: Genomic breeding values and multi-trait selection indices have significantly advanced genetic improvement in livestock but remain underutilized in guide dog breeding. This study developed a genomically informed selection framework for a population of Labrador Retrievers by integrating health (e.g., dental, ocular, and dermatological conditions) and behavioral (e.g., trainability, distraction level, pace) traits into a "Behavior Score," "Health Score," and "Total Score" index by applying Genomic Best Linear Unbiased Prediction (GBLUP) to estimate breeding values. Results: Phenotypic and genotypic data were collected from 844 dogs over 26 years at The Seeing Eye guide dog school. Predictive performance was evaluated via five-fold cross-validation and correlation-based metrics. Results showed that some dentition related health traits exhibited moderate to high Area Under Receiving Operating Characteristic (AUROC) values (0.79-0.87), indicating potential for immediate use for genetic improvement. In contrast, most other health traits demonstrated weak to moderate predictive accuracy. Behavioral traits exhibited lower predictive accuracy but showed a stronger association with training success. Models were commonly unable to correctly classify individuals for binary or ordinal traits yet performed well in ranking individuals, likely due to lower heritability or strong environmental influences of traits or limitations of the dataset itself. The behavior-focused Total Score (AUROC ~0.72) outperformed health-based indices as a fixed effect in predicting breeding success despite the weaker predictive ability of individual behavioral traits. Incorporating parental scores as fixed effects modestly improved breeding values for success, indicating the importance of integrating additional data sources where available. Discussion: While these findings underscore the utility of genomic selection for guide dog breeding, they also highlight constraints stemming from small, genetically homogeneous populations and variable phenotyping. Ultimately, we provide the first usable individual and multi-trait genomic approaches to enhance both health and performance outcomes in working dog programs and a foundation to expand upon the reference population and behavioral trait assessment to improve prediction accuracy in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".