{"id":"W3163330069","doi":"10.1101/2021.05.10.21256880","title":"Characterization of direct and/or indirect genetic associations for multiple traits in longitudinal studies of disease progression","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Hospital for Sick Children; Public Health Ontario; University of Toronto; Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"University of Toronto; Canadian Institutes of Health Research; Government of Ontario; National Institute of Diabetes and Digestive and Kidney Diseases; Compute Canada","keywords":"SNP; Single-nucleotide polymorphism; Genetic architecture; Quantitative trait locus; Genetic association; Random effects model; Statistics; Biology; Genetics; Mathematics; Medicine; Internal medicine; Meta-analysis; Gene","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":[0.05002439,0.0005677419,0.001074197,0.001125808,0.0005656206,0.001810654,0.001285062,0.001267605,0.001900958],"category_scores_gemma":[0.1080101,0.0004762146,0.001916688,0.001237828,0.001427179,0.001732776,0.002005996,0.001780781,0.000166734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006022407,"about_ca_system_score_gemma":0.001379393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234027,"about_ca_topic_score_gemma":0.003803217,"domain_scores_codex":[0.9843895,0.01229492,0.0007224279,0.001395819,0.0008997446,0.0002976899],"domain_scores_gemma":[0.8629014,0.1178511,0.006192153,0.009847727,0.00235355,0.0008540221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001379534,0.0002878403,0.6683345,0.0004370097,0.003249685,0.0005131797,0.0008476385,0.1591211,0.009126288,0.05582214,0.001016049,0.09986509],"study_design_scores_gemma":[0.0001201104,0.0006282216,0.1114087,0.0001179022,0.0006421765,0.0003112032,0.000184584,0.8123128,0.003609871,0.06917325,0.00142696,0.00006421425],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.323934,0.0004183208,0.6732845,0.0006303436,0.00003722863,0.000113647,0.0003482504,0.0001768131,0.001056862],"genre_scores_gemma":[0.9215082,0.0001247971,0.07685766,0.0001702002,0.00003526932,0.0002463234,0.0003705305,0.00006185698,0.0006251478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05002439,"threshold_uncertainty_score":0.2645574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04980671762673387,"score_gpt":0.3355464317281519,"score_spread":0.285739714101418,"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."}}