  Diagnosing an inherited disease often requires identifying the pattern of
inheritance in a patient's family. We represent family trees with genetic
patterns of inheritance using hypergraphs and latent state space models to
provide explainable inheritance pattern predictions. Our approach allows for
exact causal inference over a patient's possible genotypes given their
relatives' phenotypes. By design, inference can be examined at a low level to
provide explainable predictions. Furthermore, we make use of human intuition by
providing a method to assign hypothetical evidence to any inherited gene
alleles. Our analysis supports the application of latent state space models to
improve patient care in cases of rare inherited diseases where access to
genetic specialists is limited.
