{"doi":"10.1101/2022.12.20.521277","title":"DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network","abstract":"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 Alzheimer’s 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":302345,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9524,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":392587,"name":"Gang Li","orcid":"0000-0003-1725-6611","position":1,"is_corresponding":false},{"id":555502,"name":"Jiawen Chen","orcid":"0009-0003-6582-6559","position":2,"is_corresponding":false},{"id":11263,"name":"Quan Sun","orcid":"0000-0001-8324-2803","position":3,"is_corresponding":false},{"id":254659,"name":"Weifang Liu","orcid":"0000-0003-2278-8946","position":4,"is_corresponding":false},{"id":855151,"name":"Wyliena Guan","orcid":"0000-0002-7493-7985","position":5,"is_corresponding":false},{"id":631023,"name":"Ben Lai","orcid":"0000-0002-4201-6786","position":6,"is_corresponding":false},{"id":455423,"name":"Haibo Zhou","orcid":"0000-0002-2468-0093","position":7,"is_corresponding":false},{"id":28799,"name":"Jin Szatkiewicz","orcid":"0000-0002-4898-7401","position":8,"is_corresponding":false},{"id":104427,"name":"Xin He","orcid":"0000-0001-9011-5212","position":9,"is_corresponding":false},{"id":213,"name":"Patrick F. Sullivan","orcid":"0000-0002-6619-873X","position":10,"is_corresponding":false},{"id":24805,"name":"Yun Li","orcid":"0000-0002-9275-4189","position":11,"is_corresponding":false},{"id":254652,"name":"Jia Wen","orcid":"0000-0003-3273-7704","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:32:16.279991Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}