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Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene–disease associations. We find that mouse genotype-phenotype data is the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Data from other model organisms does not improve identification over that obtained by using the mouse alone, and therefore does not contribute significantly to this task. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation., King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No.URF/1/3790-01-01, URF/1/4355-01-01, and FCC/1/1976-34-01.

Here we report for the first time a laboratory challenge model for Enterocytozoon hepatopenaei (EHP) to determine the difference of two Specific Pathogen Free (SPF) lines of Penaeus vannamei shrimp. These lines were experimentally challenged using EHP-infected fecal strings as inoculum. Real-time PCR and histopathology assays were performed to confirm EHP infection and evaluate differences in EHP susceptibility in the two genetic lines screened. Although the histopathology of the hepatopancreas tissue showed EHP lesions in both challenged groups, the histological lesions were more pronounced in one of the SPF lines. Quantitative PCR results revealed that animals displaying less hepatocellular damage have lower EHP load compared to animals displaying more pronounced pathological changes. There was no significant difference in final survival at 36 days post-infection in these lines with survival ranging between 80 and 100%. The data showed that mortality as an endpoint metric is not a suitable parameter to determine genetic susceptibility to EHP. Instead, histopathological changes in hepatopancreas, EHP load of the same tissue, and growth retardation would be better metrics to screen EHP susceptibility in P. vannamei. The results show the feasibility of screening genetic lines of P. vannamei for EHP resistance/tolerance using fecal string as an inoculum and, assessing histopathological changes, EHP load, and weight as indicators of resistance.