AI portrait
This portrait was algorithmically built from this dog's genome: their genotype at 8 morphology loci (coat length, curl, color, ear set, body size, head shape, skull, furnishings) plus their position within the 14,478-dog atlas. The same dog always reproduces the same portrait. A different dog with different alleles gets a different portrait.
PFZ5E06_PFZ5E06
PFZ5E06_PFZ5E06 is labelled Village Dog Peru Cusco in the Hayward2016 cohort - a catch-all CanVAS label, not a literal ancestry call. One of 14,478 dogs who built the atlas.
See PFZ5E06_PFZ5E06 in the atlasPFZ5E06_PFZ5E06 sits well inside the Village Dog Peru Cusco cluster - a fairly typical example.
- Predicted large by the six body-size genes the atlas reads (IGF1, HMGA2, SMAD2, LCORL, STC2, ADAMTS17).
- Carries one copy of the FGF4 chondrodysplasia retrogene.
- Carrier of the RSPO2 wire-coat variant (single copy).
The five dogs in the atlas whose genomes sit closest to PFZ5E06_PFZ5E06's. Click any of them to keep exploring.
PFZ5E06_PFZ5E06's genome decomposes mostly into German Shepherd, with additional weight on Golden Retriever.
Breed similarity from non-negative least squares against 91 breed centroids in PCA-256 space, corrected for atlas sample-size imbalance. Without correction, Goldens (22% of the atlas) leak into every dog's raw NNLS breakdown; with it, the bias falls out. Raw fractions stay in the dataset for re-derivation. Methodology.
- German Shepherd 69%
- Golden Retriever 32%
From the CanVAS (Hayward2016 cohort) . Breed-page reference: Village Dog Peru Cusco.
Full genotype detail click to expand
The actual allele call at each locus's representative SNP for this dog. Each gene name links to its page where you can see the per-breed frequency table and the direction of effect.
Technical details click to expand
The numbers behind the placement. Useful for researchers reproducing the math or debugging an unexpected position; not interesting to most readers.
y 1.441
z 1.653
The 3 PCs on which PFZ5E06_PFZ5E06 scores most extreme, with the 3 highest-loading SNPs on each. Foundation for the future genome-ring visualization.
- chr19:1,162,032 loading 0.0555
- chr19:1,420,658 loading 0.0489
- chr19:1,685,081 loading -0.0433
- chr29:25,388,461 loading 0.0320
- chr9:34,293,305 loading 0.0320
- chr6:50,176,215 loading 0.0305
- chr9:27,289,940 loading 0.0354
- chr25:21,796,415 loading 0.0335
- chr20:38,277,512 loading -0.0333