Data from: Image feature embedding with a deep learning framework improves genome-wide association studies on dog endophenotypes

Domestic dogs exhibit remarkable morphological diversity, making
quantitative characterization of their phenotypes challenging. Traditional
phenotyping methods often rely on manual measurements, which are limited
in their ability to capture complex visual traits. Deep learning provides
a new opportunity to automatically extract informative and biologically
meaningful features from images. In this study, we constructed a dataset
of 13,254 dog images across multiple breeds and employed ResNet and ViT
models to automatically extract 256-dimensional image embeddings. After
dimensionality reduction using UMAP, we performed a GWAS on the extracted
features and breed-level genotype data. We identified 15 genes previously
reported to be associated with dog traits such as hair length and body
size, as well as novel candidate genes related to body development and
hair growth, including EIF2S2, TRHR, and TCF25, which harbor variants with
potential functional relevance. This approach is validated by known
genetic associations and can reveal new genotype-phenotype links. Building
on these capabilities, this approach provides a scalable framework for
phenotype extraction that enables population genetic studies in domestic
dogs and can facilitate breeding in other economically important species.