Phenologs at knowledge-graph scale: a mouse-to-slime-mold catalog of candidate genes for thousands of human diseases
- 1Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
- 2Semanticly Ltd, Athens, Greece.
- 3Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Abstract
Model organisms carry decades of systematic gene-phenotype annotation, but connecting those annotations to human disease is rarely straightforward. Phenologs offer a principled way to bridge this gap. A human disease and a model-organism phenotype are phenologs when their associated genes, linked by orthology, overlap more than expected by chance, so the genes of one become candidates for the other. Here we compute phenologs from the Monarch Knowledge Graph, which integrates gene-disease, gene-phenotype and orthology data for human and seven model species. Using PANTHER protein families as orthogroups and a 5% false discovery rate estimated from 1,000 randomized networks, we identify 61,243 phenologs between 3,752 human diseases and 9,700 model-organism phenotypes. We introduce a hypergeometric scheme for ranking the candidate genes a disease's phenologs nominate and compare it with the naive Bayes scheme of the original phenolog studies. Both methods recover held-out genes within a disease at similar rates, but only the hypergeometric score can be compared across diseases. Using a one-year-older release of the graph, the top 100 candidates per disease recovered 9 of 73 gene-disease associations added since, 23.5 times the random-gene expectation. Mouse accounts for most of these, with zebrafish, worm and fission yeast each contributing associations that mouse does not. We release per-disease gene candidate rankings, with known and novel associations flagged, and a dashboard for exploration of our results.
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BibTeX
@misc{odell2026phenologs,
title = {{Phenologs at knowledge-graph scale: a mouse-to-slime-mold catalog of candidate genes for thousands of human diseases}},
author = {Aaron Odell and Daniel R. Korn and Bryan J. Laraway and Phillip A. Richmond and Nicolas Matentzoglu and Kevin Schaper and Christopher J. Mungall and Melissa A. Haendel},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.23.761438},
url = {https://www.biorxiv.org/content/10.64898/2026.10.23.761438v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Phenologs at knowledge-graph scale: a mouse-to-slime-mold catalog of candidate genes for thousands of human diseases
AU - Aaron Odell
AU - Daniel R. Korn
AU - Bryan J. Laraway
AU - Phillip A. Richmond
AU - Nicolas Matentzoglu
AU - Kevin Schaper
AU - Christopher J. Mungall
AU - Melissa A. Haendel
PY - 2026
DA - 2026/10/23
PB - bioRxiv
DO - 10.64898/2026.10.23.761438
UR - https://www.biorxiv.org/content/10.64898/2026.10.23.761438v1
AB - Model organisms carry decades of systematic gene-phenotype annotation, but connecting those annotations to human disease is rarely straightforward. Phenologs offer a principled way to bridge this gap. A human disease and a model-organism phenotype are phenologs when their associated genes, linked by orthology, overlap more than expected by chance, so the genes of one become candidates for the other. Here we compute phenologs from the Monarch Knowledge Graph, which integrates gene-disease, gene-phenotype and orthology data for human and seven model species. Using PANTHER protein families as orthogroups and a 5% false discovery rate estimated from 1,000 randomized networks, we identify 61,243 phenologs between 3,752 human diseases and 9,700 model-organism phenotypes. We introduce a hypergeometric scheme for ranking the candidate genes a disease's phenologs nominate and compare it with the naive Bayes scheme of the original phenolog studies. Both methods recover held-out genes within a disease at similar rates, but only the hypergeometric score can be compared across diseases. Using a one-year-older release of the graph, the top 100 candidates per disease recovered 9 of 73 gene-disease associations added since, 23.5 times the random-gene expectation. Mouse accounts for most of these, with zebrafish, worm and fission yeast each contributing associations that mouse does not. We release per-disease gene candidate rankings, with known and novel associations flagged, and a dashboard for exploration of our results.
SN - 2692-8205
ER -