{"id":"W3006646039","doi":"10.1016/j.ajhg.2020.01.016","title":"Influence of Genetic Ancestry on Human Serum Proteome","year":2020,"lang":"en","type":"article","venue":"The American Journal of Human Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thrombosis and Atherosclerosis Research Institute; McMaster University; Population Health Research Institute","funders":"Canadian Institutes of Health Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Sanofi","keywords":"Biomarker; Genome-wide association study; Biology; Odds ratio; Disease; Biomarker discovery; Confidence interval; Genotype; Genetic architecture; Genetics; Medicine; Internal medicine; Oncology; Bioinformatics; Quantitative trait locus; Single-nucleotide polymorphism; Gene; Proteomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006951275,0.0003410168,0.0003295235,0.0007565435,0.0004531723,0.0007648304,0.0001784155,0.0003721549,0.002106811],"category_scores_gemma":[0.003237172,0.0002000684,0.0004189494,0.0009992884,0.0003604297,0.0002443045,0.0004602914,0.0003593716,0.0003060793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178668,"about_ca_system_score_gemma":0.0002239628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00424693,"about_ca_topic_score_gemma":0.002511695,"domain_scores_codex":[0.9991407,0.0004425245,0.00004909083,0.0001794186,0.0001010993,0.00008706817],"domain_scores_gemma":[0.9989043,0.0004164086,0.0002272913,0.0001721408,0.0001259408,0.0001537464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002445883,0.00006721184,0.9327207,0.00002969039,0.0007447584,0.0006052949,0.0004067092,0.0004245588,0.05469052,0.0003176771,0.0002731011,0.007273861],"study_design_scores_gemma":[0.000007930558,0.00007936282,0.9966964,0.000002753103,0.0001097571,0.000374457,0.00007006684,0.0004559712,0.001904444,0.0001200848,0.0001736082,0.000005175426],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982475,0.0002760698,0.0003229612,0.00007941298,0.00001522873,0.000001926967,0.000265578,0.00001187909,0.0007794915],"genre_scores_gemma":[0.9994319,0.00006251427,0.00009546836,0.00002546781,0.00001085573,0.00000120475,0.0001267624,0.000008860467,0.0002369705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00424693,"threshold_uncertainty_score":0.008444428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248305730007847,"score_gpt":0.3164212771690282,"score_spread":0.2839382198689497,"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."}}