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• W4282936031 abstract "Abstract Augmenting traditional genome wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in available cohorts by providing complementary approaches to the existing methods. We introduce “Genome wide association neural networks (GWANN)”, a novel approach that uses neural networks (NNs) to account for nonlinear and SNP-SNP interaction effects. We applied GWANN to family history of Alzheimer’s disease (AD) in the UK Biobank. Our method identified 26 known AD genes, 2 target nominations and 67 novel genes, and validated the results against brain eQTLs, AD phenotype associations, biological pathways, disease associations and differentially expressed gene sets in the AD brain. Some drugs targeting novel GWANN hits are currently in clinical trials for AD. Applying NNs for GWAS, alongside existing methods, illustrates their potential to complement existing algorithms and methods, and enable the discovery of novel and tractable targets for AD." @default.
• W4282936031 created "2022-06-16" @default.
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• W4282936031 date "2022-06-14" @default.
• W4282936031 modified "2023-09-27" @default.
• W4282936031 title "Genome wide association neural networks (GWANN) identify novel genes linked to family history of Alzheimer’s disease in the UK Biobank" @default.
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• W4282936031 doi "https://doi.org/10.1101/2022.06.10.22276251" @default.
• W4282936031 hasPublicationYear "2022" @default.
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