{"id":"W2437351093","doi":"10.1038/ng.3668","title":"Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps","year":2016,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Heart, Lung, and Blood Institute; National Institute on Aging; Medical Research Council; National Institutes of Health; European Commission; National Institute for Health and Care Research; British Heart Foundation; Wellcome Trust","keywords":"Imputation (statistics); Biology; Genome-wide association study; Quantitative trait locus; 1000 Genomes Project; Minor allele frequency; Genetic architecture; Genetic association; Genetics; Trait; Allele; Allele frequency; Computational biology; Genotype; Single-nucleotide polymorphism; Gene; Missing data; Statistics","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":[0.0002955207,0.000177637,0.0002670346,0.00007174579,0.00007346764,0.00001201067,0.0001041707,0.0004093296,0.000002487074],"category_scores_gemma":[0.000369523,0.0001225157,0.00006959373,0.0001477411,0.0001242865,0.000003478879,0.00009257715,0.0001160519,6.238921e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003147541,"about_ca_system_score_gemma":0.00009012076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002169169,"about_ca_topic_score_gemma":0.0000693584,"domain_scores_codex":[0.9987418,0.0001721478,0.0003005811,0.0003533108,0.0001673909,0.0002647761],"domain_scores_gemma":[0.999047,0.00005051925,0.0003346401,0.0002961184,0.0002112069,0.0000605114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006301132,0.00004861618,0.8790023,0.00001243283,0.0003955165,0.000002059408,0.00004881922,0.001325545,0.105651,0.00001314565,0.0005304154,0.01290712],"study_design_scores_gemma":[0.001209881,0.0003840649,0.9795082,0.00002686903,0.0002516877,0.00001634793,0.00004418359,0.0002178083,0.01570921,0.0003898304,0.001981144,0.0002607366],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813493,0.004665564,0.01340363,0.00008250872,0.00009885417,0.0001649662,0.0001845964,0.000006310864,0.00004419219],"genre_scores_gemma":[0.9868375,0.002111584,0.01058745,0.00008954628,0.0001338385,0.000007172522,0.00007182161,0.00002639738,0.0001346567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1005059,"threshold_uncertainty_score":0.4996046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006885454692681649,"score_gpt":0.2484621872781654,"score_spread":0.2415767325854837,"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."}}