{"id":"W3172096945","doi":"10.1186/s12864-021-07745-5","title":"Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study","year":2021,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"National Heart, Lung, and Blood Institute; U.S. Department of Health and Human Services; National Institutes of Health; National Center for Research Resources; American Diabetes Association; National Human Genome Research Institute","keywords":"Genome-wide association study; Genetic architecture; Biology; Genetic association; Genetics; Genetic genealogy; Genomics; Population; Genetic epidemiology; Human genetics; White (mutation); Genome; Single-nucleotide polymorphism; Quantitative trait locus; Gene; Genotype; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003966799,0.0005320185,0.0004780165,0.0008126484,0.0008960133,0.001099846,0.0006073224,0.000523262,0.001892247],"category_scores_gemma":[0.004765216,0.000287131,0.001340121,0.001350255,0.0003444397,0.0003460256,0.001373737,0.0009448224,0.0001828505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001930099,"about_ca_system_score_gemma":0.0006148398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003271169,"about_ca_topic_score_gemma":0.005347236,"domain_scores_codex":[0.9971465,0.001528538,0.0002146819,0.0006807224,0.0002458571,0.0001836375],"domain_scores_gemma":[0.9974602,0.0009493902,0.0005326658,0.000550776,0.0002445026,0.0002624371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008054924,0.00007236211,0.9840094,0.0000634212,0.001937516,0.0005695804,0.0003606623,0.000304272,0.003772161,0.0003801592,0.0005357488,0.007189227],"study_design_scores_gemma":[0.00009934868,0.0003068805,0.9930664,0.00003280856,0.001520779,0.0007594794,0.0002729842,0.001139759,0.001038271,0.0005106631,0.001237112,0.00001549246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935973,0.0003575993,0.004286817,0.0001571365,0.00002432083,0.00004182966,0.0007624688,0.00002671813,0.0007460109],"genre_scores_gemma":[0.9966478,0.0001183643,0.002318019,0.00007795273,0.00001972528,0.00005040563,0.0005094533,0.00001407805,0.0002442172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003966799,"threshold_uncertainty_score":0.02097869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06456241527469171,"score_gpt":0.3237424329780246,"score_spread":0.2591800177033329,"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."}}