{"id":"W2950522632","doi":"10.1093/bioinformatics/btz518","title":"Bayesian GWAS with Structured and Non-Local Priors","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; Takeda Pharmaceutical Company; IXICO; National Institute of General Medical Sciences; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Roche; University of Southern California; F. Hoffmann-La Roche; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Alzheimer's Association; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Prior probability; Bayesian probability; Computer science; Genome-wide association study; Statistical power; Covariate; Machine learning; Artificial intelligence; Statistics; Mathematics; Single-nucleotide polymorphism; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001064463,0.0001079165,0.0001355715,0.00002514753,0.00004435239,0.00001400071,0.00007867944,0.000138062,0.00001753245],"category_scores_gemma":[0.00002111922,0.00008123048,0.0000255413,0.00004504807,0.00006177265,0.000003869457,0.00005784148,0.00005571352,0.00002125619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008445477,"about_ca_system_score_gemma":0.00005107018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008203097,"about_ca_topic_score_gemma":0.00002438312,"domain_scores_codex":[0.9994341,0.00001344757,0.0001849031,0.0001120015,0.00006773378,0.0001878675],"domain_scores_gemma":[0.999574,0.000009764614,0.00009644192,0.0002156784,0.00003828349,0.00006584197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002065569,0.0000636814,0.9174806,0.0002879031,0.0003862714,0.000002543577,0.001649264,0.004397308,0.01588025,0.0004062134,0.01068021,0.04855912],"study_design_scores_gemma":[0.004232254,0.002570509,0.8263731,0.00005966116,0.0001024474,0.0001639102,0.00430926,0.09738095,0.008756708,0.0003357612,0.05450825,0.001207166],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9491427,0.00005620954,0.04732401,0.0001030877,0.00007636178,0.0001907354,0.00001074339,0.000007692859,0.003088439],"genre_scores_gemma":[0.9741585,0.00004223162,0.02492929,0.0003541541,0.00003747396,0.000004252965,0.00006781463,0.000009249416,0.0003970411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09298364,"threshold_uncertainty_score":0.3312483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003657611671958617,"score_gpt":0.2119490812847971,"score_spread":0.2082914696128385,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}