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A 285-SNP Model Distinguished Red Junglefowl, Domestic Chickens and Hybrids

Wild Red Junglefowl in Kaziranga National Park
Research · Genetics & genomics

A genomic study published in 2026 developed a deep-learning model for distinguishing Red Junglefowl (Gallus gallus) from domestic chickens and hybrids. Rather than relying on one diagnostic gene, the model learned combinations of DNA markers that distinguished known wild and domestic reference birds. The researchers built it from 1,960 genomes, including 164 Red Junglefowl and 1,796 domestic chickens. After first identifying thousands of DNA positions that differed between the groups, the pipeline reduced the information to a panel of 285 single-nucleotide polymorphisms, or SNPs. A SNP is a specific position in the genome where individuals can carry different DNA bases; together, these 285 positions contained enough ancestry information for the model to separate the groups. A model score above 95% was treated as strong support for Red Junglefowl ancestry, below 5% as domestic chicken, and intermediate scores as hybrid. The percentages are classification scores from the model, not a literal measurement of how many percent of a bird is wild.

The model was then tested on independent genomic datasets containing contemporary birds and historical museum specimens. It was able to classify Red Junglefowl, domestic chickens and hybrids, and reached 97.8% accuracy in the historical set even though some specimens lacked genotype data at part of the 285-marker panel. In practical terms, incomplete DNA information did not usually prevent the model from recognising the historical Red Junglefowl.

A reduced SNP panel retained useful discriminatory power

The researchers compared the approach with several existing tools for detecting hybrid ancestry and found that the reduced SNP panel retained useful discriminatory power while requiring far less genomic information than whole-genome sequencing. They also applied the same pipeline to wild boar, domestic pigs and hybrids as an independent validation of the method.

Hybridisation with domestic chickens is a major challenge when identifying genetically wild Red Junglefowl populations. The authors present the model as a tool for monitoring wild relatives of domestic animals and for conservation work where historical specimens or modern samples may contain incomplete genomic data.

Sources and image credit

  1. Cai, Z.-F., Liu, B., Yin, T.-T., Ma, C., Han, Z.-T., Gu, L.-H., Zhang, C., Wu, F., Liu, L.-S., Shi, X., Wang, X.-Q., Wu, R.-N., Xie, L.-Z., Bai, Y., Zhang, Y.-C., Zhang, S.-R., Gu, L.-J., Wang, M.-S., Xie, G.-L., … Peng, M.-S. (2026). Harnessing deep learning in searching wild relatives of domestic animals. Molecular Ecology Resources, 26(3), e70133. https://doi.org/10.1111/1755-0998.70133Primary open-access research article first published 20 April 2026. The Red Junglefowl identification model was developed from 1,960 genomes—164 Red Junglefowl and 1,796 domestic chickens—and used a selected 285-SNP panel. It classified Red Junglefowl, domestic chickens and hybrids and achieved 97.8% accuracy for historical samples with missing genotypes. The pipeline was independently validated with wild-boar and domestic-pig genomic data.

Image: Tisha Mukherjee / Wikimedia Commons — wild Red Junglefowl photographed in Kaziranga National Park, Assam, India; representative species image, not a study specimen; cropped to 16:9. · CC BY-SA 4.0 · Source

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