A Leaky Noisy-OR Bayesian Network Applied to Genetic Counseling in Dogs

Abstract

Simple Summary

Genetic disorders represent a serious health problem for companion animals and combating such disorders is a real challenge. Bayes networks facilitate the objective assessment of the risk of such disorders. We apply the methodology to answer two typical questions in genetic counselling, i.e., the risk for an animal of showing clinical signs of a genetic disease when the result at the genetic test is known and the risk of testing positive for the mutant allele when the genetic test is not made. Results showed the network is appropriate to answer objectively and transparently both questions under a variety of alternative scenarios. It can be updated automatically and can be represented visually so interactive discussion are easy between the veterinarian and his/her interlocutor.

Abstract

Genetic disorders are very frequent in dogs but evaluating individualized risks of their occurrence can be uncertain. Bayesian networks are tools to characterize and analyze such events. The paper illustrates their benefits and challenges in answering two typical questions in genetic counselling: (1) What is the probability of a test-positive animal showing clinical signs of the disease? (2) What is the risk of testing positive for the mutant allele when one parent presents clinical signs? Current limited knowledge on the hereditary mode of transmission of degenerative myelopathy and on the effects of sex, diet, exercise regimen and age on the occurrence of clinical signs concurrent with the finding of the deleterious mutation was retrieved from the scientific literature. Uncertainty on this information was converted into prior Beta distributions and leaky-noisy OR models were used to construct the conditional probability tables necessary to answer the questions. Results showed the network is appropriate to answer objectively and transparently both questions under a variety of scenarios. Once users of the network have agreed with its structure and the values of the priors, computations are straightforward. The network can be updated automatically and can be represented visually so interactive discussion are easy between the veterinarian and his/her interlocutor.