Background: Bipolar disorder (BD) is a type of chronic emotional disorder
with a complex genetic structure. However, its genetic molecular mechanism is still
unclear, which makes it insufficient to be diagnosed and treated.
Methods and Results: In this paper, we proposed a model for predicting BD based on
single nucleotide polymorphisms (SNPs) screening by genome-wide association study
(GWAS), which was constructed by a convolutional neural network (CNN) that predicted
the probability of the disease. According to the difference of GWAS threshold, two sets
of data were named: group P001 and group P005. And different convolutional neural
networks are set for the two sets of data. The training accuracy of the model trained with
group P001 data is 96%, and the test accuracy is 91%. The training accuracy of the
model trained with group P005 data is 94.5%, and the test accuracy is 92%. At the same
time, we used gradient weighted class activation mapping (Grad-CAM) to interpret the
prediction model, indirectly to identify high-risk SNPs of BD. In the end, we compared
these high-risk SNPs with human gene annotation information.
Conclusion: The model prediction results of the group P001 yielded 137 risk genes, of
which 22 were reported to be associated with the occurrence of BD. The model
prediction results of the group P005 yielded 407 risk genes, of which 51 were reported to
be associated with the occurrence of BD.