Mapping whole-organism genetic comorbidities across model Species using unified ontologies

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not specified in abstract

• Primary Outcome: Most NOA genes have conserved orthologs across model species with frequent non-reproductive phenotypic associations

• Nnt Or Sensitivity: Comorbidity patterns partially predictable from gene expression and ontology data

• Confidence Interval: Not reported

Clinical Application

High - computational resource available for clinical genetics applications Relevant to Australian fertility clinics and genetic counselling services; may inform TGA considerations for genetic testing panels and support RACGP guidelines for male infertility assessment Men with non-obstructive azoospermia and clinicians managing male infertility

Abstract

Understanding how genetic variation contributes to organism-wide phenotypes is critical for identifying mechanisms of disease. Here, we present a computational approach to analyze whole-organism comorbidities associated with genes underlying non-obstructive azoospermia (NOA), the most severe form of male infertility. We curated 204 mouse genes with experimentally validated spermatogenic failure and mapped their orthologs and phenotype annotations across humans, M. musculus, D. rerio, D. melanogaster, and C. elegans using a unified cross-species phenotype structure. This framework integrates a newly developed reproductive phenotype ontology with standardized whole-body phenotype categories to enable direct cross-species comparisons, using thousands of genotype-phenotype associations stored in model organism databases. Our analysis shows that most NOA genes have conserved orthologs across models, and that perturbation of these genes is frequently associated with non-reproductive phenotypes. In particular, integumentary defects and neoplastic phenotypes recur across species, supporting a shared genetic basis for epidemiological links between male infertility and systemic disease. Gene-level comorbidity patterns are partially predictable from single-cell RNA-seq, whole-body gene expression, and Gene Ontology annotations, suggesting that fundamental biological constraints shape the systemic consequences of reproductive gene dysfunction. Clustering genes by comorbidity profiles further distinguished genes with isolated reproductive effects from those with broad organismal consequences. This work demonstrates the power of ontology-based cross-species analysis for identifying pleiotropic effects of Mendelian disease mutations, and provides a resource, CoMorbidity DataBase Mapper (CoMo DBM), for joint analysis of genotype-phenotype associations in model organism databases.

References

1. 1.Peaslee, C., Held, M., Fadial, E., & Conrad, D. F. (2026). Mapping whole-organism genetic comorbidities across model Species using unified ontologies. Genetics. https://doi.org/10.1093/genetics/iyag038