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Genome-wide association studies (GWAS) provide a means of examining thecommon genetic variation underlying a range of traits and disorders. Inaddition, it is hoped that GWAS may provide a means of differentiating affectedfrom unaffected individuals. This has potential applications in the area ofrisk prediction. Current attempts to address this problem focus on using thepolygene risk score (PRS) to predict case-control status on the basis of GWASdata. However this approach has so far had limited success for complex traitssuch as schizophrenia (SZ). This is essentially a classification problem.Artificial neural networks (ANNs) have been shown in recent years to be highlyeffective in such applications. Here we apply an ANN to the problem ofdistinguishing SZ patients from unaffected controls. We compare theeffectiveness of the ANN with the PRS in classifying individuals bycase-control status based only on genetic data from a GWAS. We use theschizophrenia dataset from the Psychiatric Genomics Consortium (PGC) for thisstudy. Our analysis indicates that the ANN is more sensitive to sample sizethan the PRS. As larger and larger sample sizes become available, we suggestthat ANNs are a promising alternative to the PRS for classification and riskprediction for complex genetic disorders.

Can artificial neural networks supplant the polygene risk score for risk prediction of complex disorders given very large sample sizes?