{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01549988,0.0008117491,0.001613126,0.001450605,0.0007398366,0.002468545,0.002481015,0.001870282,0.006262578],"category_scores_gemma":[0.0554814,0.001094553,0.001509884,0.003149795,0.002064242,0.00273987,0.00215284,0.003156487,0.002132697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131175,"about_ca_system_score_gemma":0.002318873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008466853,"about_ca_topic_score_gemma":0.01008394,"domain_scores_codex":[0.9928347,0.004877602,0.0002813064,0.0009876514,0.0008517676,0.0001669842],"domain_scores_gemma":[0.9767367,0.01794831,0.001343058,0.002254082,0.001319957,0.0003979052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004991318,0.0001103125,0.02107944,0.0007794143,0.0006309401,0.0005979939,0.0006334141,0.237724,0.003945159,0.5241084,0.01686365,0.193028],"study_design_scores_gemma":[0.0001732705,0.00005779015,0.003625427,0.0002053373,0.0001722928,0.0005280834,0.00006183833,0.365358,0.001187504,0.614854,0.01368431,0.00009202996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003929501,0.0003093377,0.992954,0.0007907162,0.00002687302,0.00004905301,0.000466551,0.0005300682,0.0009439611],"genre_scores_gemma":[0.1633482,0.001273988,0.8286881,0.0010071,0.0002522454,0.0005141558,0.001661028,0.0003958565,0.002859269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01549988,"threshold_uncertainty_score":0.08197218,"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."}}