DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

Wen, J.; Li, G.; Chen, j.; Sun, Q.; Liu, W.; Guan, W.; Lai, B.; Szatkiewicz, J. P.; He, X.; Sullivan, P. F.; Li, Y.

doi:10.1101/2022.12.20.521277

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bioRxiv · 10.1101/2022.12.20.521277

DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

Wen, J.·Li, G.·Chen, j.·Sun, Q.·Liu, W.·Guan, W.·Lai, B.·Szatkiewicz, J. P.·He, X.·Sullivan, P. F.·Li, Y.

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Abstract

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by [~]3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimers disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.

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Wen, J., Li, G., Chen, j., Sun, Q., Liu, W., Guan, W., Lai, B., Szatkiewicz, J. P., He, X., Sullivan, P. F., Li, Y.. 2022-12-21. DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network. https://doi.org/10.1101/2022.12.20.521277

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