  Investigating the genetic architecture of complex diseases is challenging due
to the highly polygenic and interactive landscape of genetic and environmental
factors. Although genome-wide association studies (GWAS) have identified
thousands of variants for multiple complex phenotypes, conventional statistical
approaches can be limited by simplified assumptions such as linearity and lack
of epistasis models. In this work, we trained artificial neural networks for
predicting complex traits using both simulated and real genotype/phenotype
datasets. We extracted feature importance scores via different post hoc
interpretability methods to identify potentially associated loci (PAL) for the
target phenotype. Simulations we performed with various parameters demonstrated
that associated loci can be detected with good precision using strict selection
criteria, but downstream analyses are required for fine-mapping the exact
variants due to linkage disequilibrium, similarly to conventional GWAS. By
applying our approach to the schizophrenia cohort in the Estonian Biobank, we
were able to detect multiple PAL related to this highly polygenic and heritable
disorder. We also performed enrichment analyses with PAL in genic regions,
which predominantly identified terms associated with brain morphology. With
further improvements in model optimization and confidence measures, artificial
neural networks can enhance the identification of genomic loci associated with
complex diseases, providing a more comprehensive approach for GWAS and serving
as initial screening tools for subsequent functional studies.
  Keywords: Deep learning, interpretability, genome-wide association studies,
complex diseases
