System and method for personalized interpretation of genetic variants

A system for phenotyping, the system comprising:

• one or more processor;
• memory having a phenotyping neural network model stored thereon;
• and
• a training module configured to cause the one or more processor to:
• generate a plurality of individual-specific variant graphs, each individual-specific variant graph being associated with an individual and representing dependencies among variants in a genome of the individual based on biomedical domain knowledge;
• generate a training dataset comprising at least some of the plurality of individual-specific variants graphs labelled according to corresponding target phenotype values to be predicted;
• and
• train the phenotyping neural network model using the training dataset to generate a phenotype prediction from an input individual-specific variant graph.

A system for phenotyping, the system comprising:

• one or more processor;
• memory having a phenotyping neural network model stored thereon;
• and
• an inference module configured to cause the one or more processor to:
• generate an initial individual-specific variant graph for a target individual, the initial individual-specific variant graph representing dependencies among variants in a genome of the target individual based on biomedical domain knowledge;
• provide the initial individual-specific variant graph as input to the phenotyping neural network model, thereby generating a first phenotype prediction;
• modify the initial individual-specific variant graph to remove a genetic variant, thereby generating a modified individual-specific variant graph;
• provide the modified individual-specific variant graph as input to the phenotyping neural network model, thereby generating a second phenotype prediction;
• and
• calculate a difference between the first phenotype prediction and the second phenotype prediction, the difference providing a measure of a contribution of the genetic variant to a phenotype of the target individual.