Genetic analysis of the modern Australian labradoodle dog breed reveals an excess of the poodle genome
Bibliographic record
Abstract
The genomic diversity of the domestic dog is an invaluable resource for advancing understanding of mammalian biology, evolutionary biology, morphologic variation, and behavior. There are approximately 350 recognized breeds in the world today, many established through hybridization and selection followed by intense breeding programs aimed at retaining or enhancing specific traits. As a result, many breeds suffer from an excess of particular diseases, one of many factors leading to the recent trend of "designer breed" development, i.e. crossing purebred dogs from existing breeds in the hope that offspring will be enriched for desired traits and characteristics of the parental breeds. We used a dense panel of 150,106 SNPs to analyze the population structure of the Australian labradoodle (ALBD), to understand how such breeds are developed. Haplotype and admixture analyses show that breeds other than the poodle (POOD) and Labrador retriever (LAB) contributed to ALBD formation, but that the breed is, at the genetic level, predominantly POOD, with all small and large varieties contributing to its construction. Allele frequency analysis reveals that the breed is enhanced for variants associated with a poodle-like coat, which is perceived by breeders to have an association with hypoallergenicity. We observed little enhancement for LAB-specific alleles. This study provides a blueprint for understanding how dog breeds are formed, highlighting the limited scope of desired traits in defining new breeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".