Mapping and modification of gene network endophenotypes

A method for predicting endophenotypes of interacting partner genes, the method comprising, by one or more computing devices:

• obtaining one or more endophenotype profiles each corresponding to a genotype, wherein each endophenotype profile comprises a plurality of endophenotypes, each endophenotype corresponding to a gene;
• partitioning the plurality of endophenotypes of each of the one or more endophenotype profiles into a first set of endophenotypes corresponding to a first set of genes and a second set of endophenotypes corresponding to a second set of genes;
• receiving an input to modify each of the endophenotypes in the first set of endophenotypes to a desired level to generate a modified first set of endophenotypes;
• and
• inputting the modified first set of endophenotypes and unmodified second set of endophenotypes into a trained machine-learning model to obtain a prediction of an updated second set of endophenotypes, wherein the updated second set of endophenotypes represents an updated version of the second set of endophenotypes after interacting with the modified subset of the first set of endophenotypes and comprises updated levels of endophenotypes corresponding to one or more interacting partner genes in the second set of genes.

A system including one or more computing devices, comprising:

• one or more non-transitory computer-readable storage media including instructions;
• and
• one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions to:
• obtain one or more endophenotype profiles each corresponding to a genotype, wherein each endophenotypes profile comprises a plurality of endophenotypes, each endophenotype corresponding to a gene;
• partition the plurality of endophenotypes of each of the one or more endophenotype profiles into a first set of endophenotypes corresponding to a first set of genes and a second set of endophenotypes corresponding to a second set of genes;
• receive an input to modify each of the endophenotypes in the first set of endophenotypes to a desired level to generate a modified first set of endophenotypes;
• and
• input the modified first set of endophenotypes and unmodified second set of endophenotypes into a trained machine-learning model to obtain a prediction of an updated second set of endophenotypes, wherein the updated second set of endophenotypes represents an updated version of the second set of endophenotypes after interacting with the modified subset of the first set of endophenotypes and comprises updated levels of endophenotypes corresponding to one or more interacting partner genes in the second set of genes.

A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of one or more computing devices, cause the one or more processors to:

• obtain one or more endophenotype profiles each corresponding to a genotype, wherein each endophenotype profile comprises a plurality of endophenotypes, each endophenotype corresponding to a gene;
• partition the plurality of endophenotypes of each of the one or more endophenotype profiles into a first set of endophenotypes corresponding to a first set of genes and a second set of endophenotypes corresponding to a second set of genes;
• receive an input to modify each of the endophenotypes in the first set of endophenotypes to a desired level to generate a modified first set of endophenotypes;
• and
• input the modified first set of endophenotypes and unmodified second set of endophenotypes into a trained machine-learning model to obtain a prediction of an updated second set of endophenotypes, wherein the updated second set of endophenotypes represents an updated version of the second set of endophenotypes after interacting with the modified subset of the first set of endophenotypes and comprises updated levels of endophenotypes corresponding to one or more interacting partner genes in the second set of genes.