A-G.42: End-to-end deep learning methods for genetic risk prediction of Schizophrenia
Schizophrenia is a highly heritable psychiatric disorder with a complex genetic basis. While recent Genome Wide Association Studies (GWAS) and Whole Exome Sequencing (WES) have identified numerous risk loci, existing clinical prediction models rely primarily on linear assumptions, limiting their ability to capture complex, nonlinear genetic effects such as epistasis.
In this study, we apply a new paradigm of end-to-end Genome Interpretation (GI) Neural Network (NN) models to predict Schizophrenia risk from WES data in a large cohort of 6,135 cases and 6,245 controls. We show that nonlinear NNs significantly outperform conventional additive models, when sufficient sample size is available. These findings further support the fact that high-order genetic interactions between alleles and variants should be considered by clinical and quantitative genetics models.
To investigate the decision process our models follow, we integrate Explainable AI (XAI) techniques and biological priors into our models, using them to identify predictive genes and pathways. Our approach recovers both known Schizophrenia risk genes and recommends BASP1 as a potential understudied Schizophrenia gene involved in neuronal development.
Co-authors: Yves Moreau, Daniele Raimondi
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