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.
Sci_LU1473_Sci_LU1473
Sci_LU1473_Sci_LU1473 is a Schipperke from the Hayward2016 research cohort. One of 14,478 dogs who built the atlas.
See Sci_LU1473_Sci_LU1473 in the atlasSci_LU1473_Sci_LU1473 sits at the edge of the Schipperke cluster but still well within it.
- Predicted small by the six body-size genes the atlas reads (IGF1, HMGA2, SMAD2, LCORL, STC2, ADAMTS17).
- Carries one copy of the FGF4 chondrodysplasia retrogene.
- Wire-coat furnishings - the eyebrow + beard variant at RSPO2.
The five dogs in the atlas whose genomes sit closest to Sci_LU1473_Sci_LU1473's. Click any of them to keep exploring.
Sci_LU1473_Sci_LU1473 sits in the Schipperke cluster, with genome overlap to Golden Retriever, and Labrador 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.
- Schipperke 84%
- Golden Retriever 9%
- Labrador Retriever 7%
From the CanVAS (Hayward2016 cohort) . Breed-page reference: Schipperke.
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 -3.965
z 5.587
The 3 PCs on which Sci_LU1473_Sci_LU1473 scores most extreme, with the 3 highest-loading SNPs on each. Foundation for the future genome-ring visualization.
- chr22:1,808,281 loading 0.0402
- chr22:2,157,682 loading 0.0366
- chr22:2,072,452 loading 0.0364
- chr20:37,137,374 loading 0.0345
- chr37:21,909,127 loading -0.0321
- chr23:49,705,023 loading 0.0289
- chr22:15,601,204 loading 0.0364
- chr22:2,850,748 loading 0.0330
- chr22:15,670,985 loading -0.0317