{"id":"W4304695497","doi":"10.1016/j.xgen.2022.100192","title":"Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease","year":2022,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":407,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Medical Research Council; Novo Nordisk Fonden; Wellcome Trust; Cooley's Anemia Foundation; National Human Genome Research Institute; Biogen; U.S. Department of Veterans Affairs","keywords":"Biobank; Genome-wide association study; Genetic association; Disease; Meta-analysis; Data science; Biology; Computational biology; Genetics; Computer science; Medicine; Gene; Genotype; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374409,0.0002096605,0.0004399494,0.00003983207,0.0005127713,0.00004703531,0.0003345145,0.00007373228,0.0001912643],"category_scores_gemma":[0.00003257052,0.0002171897,0.0008638806,0.0002975301,0.00007535859,0.000003551971,0.0006635555,0.00009138769,0.000008481299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001173366,"about_ca_system_score_gemma":0.00010747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006087181,"about_ca_topic_score_gemma":0.00009398213,"domain_scores_codex":[0.998303,0.0002391077,0.0003620329,0.0005510811,0.0001294057,0.0004153685],"domain_scores_gemma":[0.9990067,0.00001721729,0.0002215723,0.0005738441,0.00004405916,0.0001366277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001774362,0.0006205075,0.4247608,0.0000361095,0.06152662,0.00005272536,0.0004496659,0.4319165,0.07393893,0.0002166096,0.006045912,0.000258124],"study_design_scores_gemma":[0.001260813,0.0005975853,0.8443766,4.595126e-7,0.06680826,0.00001609542,0.001760738,0.001419977,0.002286823,0.00187276,0.07805843,0.001541474],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841177,0.004536595,0.008965546,0.0001438876,0.0001398968,0.0001640387,0.00124486,0.00001184533,0.00067566],"genre_scores_gemma":[0.9956372,0.00009984419,0.001247691,0.0008413162,0.0001016628,0.00009389187,0.0007920412,0.00002177733,0.001164534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4304966,"threshold_uncertainty_score":0.885674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837515664813503,"score_gpt":0.308371599648952,"score_spread":0.269996443000817,"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."}}