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.
SILK_SILK_10E09
SILK_SILK_10E09 is a Silky Terrier from the Spatola research cohort. One of 14,478 dogs who built the atlas.
See SILK_SILK_10E09 in the atlasSILK_SILK_10E09 sits at the edge of the Silky Terrier cluster but still well within it.
- Predicted large by the six body-size genes the atlas reads (IGF1, HMGA2, SMAD2, LCORL, STC2, ADAMTS17).
- Carries both copies of the FGF4 chondrodysplasia retrogene - the short-leg variant in Dachshund, Pembroke Corgi, Basset Hound.
- Smooth coat. No wire-furnishings variant at RSPO2.
The five dogs in the atlas whose genomes sit closest to SILK_SILK_10E09's. Click any of them to keep exploring.
SILK_SILK_10E09 sits in the Labrador Retriever cluster, with genome overlap to Golden Retriever - sister breeds nearby in the atlas.
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.
- Labrador Retriever 57%
- Golden Retriever 43%
From the CanVAS (Spatola cohort) . Breed-page reference: Silky Terrier.
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 5.131
z 4.580
The 3 PCs on which SILK_SILK_10E09 scores most extreme, with the 3 highest-loading SNPs on each. Foundation for the future genome-ring visualization.
- chr13:43,330,325 loading 0.0255
- chr17:50,562,684 loading 0.0245
- chr19:34,146,978 loading 0.0244
- chr3:72,396,064 loading 0.0333
- chr10:25,699,634 loading 0.0321
- chr13:22,942,852 loading 0.0315
- chr22:1,996,400 loading 0.0303
- chr22:2,231,766 loading 0.0280
- chr22:3,165,486 loading 0.0275