  Genome-wide association studies (GWAS) provide a means of examining the
common genetic variation underlying a range of traits and disorders. In
addition, it is hoped that GWAS may provide a means of differentiating affected
from unaffected individuals. This has potential applications in the area of
risk prediction. Current attempts to address this problem focus on using the
polygene risk score (PRS) to predict case-control status on the basis of GWAS
data. However this approach has so far had limited success for complex traits
such as schizophrenia (SZ). This is essentially a classification problem.
Artificial neural networks (ANNs) have been shown in recent years to be highly
effective in such applications. Here we apply an ANN to the problem of
distinguishing SZ patients from unaffected controls. We compare the
effectiveness of the ANN with the PRS in classifying individuals by
case-control status based only on genetic data from a GWAS. We use the
schizophrenia dataset from the Psychiatric Genomics Consortium (PGC) for this
study. Our analysis indicates that the ANN is more sensitive to sample size
than the PRS. As larger and larger sample sizes become available, we suggest
that ANNs are a promising alternative to the PRS for classification and risk
prediction for complex genetic disorders.
