Identifying genetic interactions related to diabetes using phenotype

Document Type


Publication Date

Summer 2018

JAX Location

In: Student Reports, Summer 2018, The Jackson Laboratory


The prevalence of diabetes has significantly increased over the past 30 years. Treatments are available, however; their effectiveness for patients has been in question as treatments rrey have a benefit, adverse or no effect on symptoms. We hypothesized that we can identify potential diabetes related gene interactions from a large database of mouse models. In this study, we mine data in the Mouse Genome lnformatics database to see which genes interact to cause and modify diabetes diseases or key diagnostic phenotypes of diabetes in mouse models. To do this, a list of human diabetes phenotypes was collected from literature and records in the Online Mendelian Inheritance in Man database. Definitions in the Hurren Phenotype and Mammalian Phenotype (MP) ontologies on Mouse Genome Informatics were used to translate these human phenotypes into MP terms. After, a template query was used on MouseMine ( to data mine for genotypes related to the set of MP terms. A list of genes that modify diabetes and key diabetes phenotypes was derived from this data. Interestingly, we saw no interacting genes overlapped between the gene lists for Type 1 and Type 2 diabetes. Many genes with mutations that improved diabetes phenotypes were identified from studies using the NOD strain model. Knowing the effects of gene interactions may help doctors prescribe the correct dosage or combination of medicine based on the type of diabetes and the genotype of the patient.

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