Using neural networks as an aid in the determination of disease status: comparison of clinical diagnosis to neural-network predictions in a pedigree with autosomal dominant limb-girdle muscular dystrophy.
Studies of the genetics of certain inherited diseases require expertise in the determination of disease status even for single-locus traits. For example, in the diagnosis of autosomal dominant limb-girdle muscular dystrophy (LGMD1A), it is not always possible to make a clear-cut determination of disease, because of variability in the diagnostic criteria, age at onset, and differential presentation of disease. Mapping such diseases is greatly simplified if the data present a homogeneous genetic trait and if disease status can be reliably determined. Here, we present an approach to determination of disease status, using methods of artificial neural-network analysis. The method entails "training" an artificial neural network, with input facts (based on diagnostic criteria) and related results (based on disease diagnosis). The network contains weight factors connecting input "neurons" to output "neurons," and these connections are adjusted until the network can reliably produce the appropriate outputs for the given input facts. The trained network can be "tested" with a second set of facts, in which the outcomes are known but not provided to the network, to see how well the training has worked. The method was applied to members of a pedigree with LGMD1A, now mapped to chromosome 5q. We used diagnostic criteria and disease status to train a neural network to classify individuals as "affected" or "not affected." The trained network reproduced the disease diagnosis of all individuals of known phenotype, with 98% reliability. This approach defined an appropriate choice of clinical factors for determination of disease status. Additionally, it provided insight into disease classification of those considered to have an "unknown" phenotype on the basis of standard clinical diagnostic methods.