{"id":"W2340793955","doi":"10.1101/030783","title":"Using genotype data to distinguish pleiotropy from heterogeneity: deciphering coheritability in autoimmune and neuropsychiatric diseases","year":2015,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Diabetes and associated disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; National Human Genome Research Institute; National Institutes of Health; National Institute of Allergy and Infectious Diseases; Fulbright Canada; Fondation Brain Canada; Juvenile Diabetes Research Foundation International; Doris Duke Charitable Foundation","keywords":"Pleiotropy; Genetic heterogeneity; Genetic architecture; Biology; Genotype; Genetics; Autoimmunity; Rheumatoid arthritis; Schizophrenia (object-oriented programming); Phenotype; Immunology; Medicine; Gene; Psychiatry; Immune system","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":[0.01206959,0.0006438632,0.00112634,0.003408463,0.0006868486,0.001567572,0.0006557976,0.0007102658,0.002183865],"category_scores_gemma":[0.02941524,0.0004690246,0.001207314,0.002788261,0.001281218,0.001070022,0.001847272,0.001054123,0.0003266456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003106102,"about_ca_system_score_gemma":0.0005381567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079249,"about_ca_topic_score_gemma":0.001490252,"domain_scores_codex":[0.9912974,0.005491261,0.0005690622,0.0015884,0.0007587358,0.0002951231],"domain_scores_gemma":[0.9698392,0.02107834,0.002895541,0.005150032,0.0004393583,0.0005974737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001160908,0.00009198723,0.8909182,0.0002222403,0.002728614,0.000498717,0.0007697039,0.007846175,0.02805126,0.002755579,0.001058493,0.06389827],"study_design_scores_gemma":[0.000166005,0.0004151331,0.8892646,0.00006259078,0.0006863578,0.0007998024,0.0004837439,0.07752842,0.01031567,0.01781615,0.002361913,0.00009972138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448297,0.0002664456,0.05198967,0.0001631735,0.00002468857,0.00003624456,0.001627586,0.0003659814,0.0006966195],"genre_scores_gemma":[0.9787664,0.00004556524,0.01973938,0.00005039227,0.00002075823,0.00006434326,0.001054715,0.000100063,0.0001583585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01206959,"threshold_uncertainty_score":0.06383091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03169386436709047,"score_gpt":0.2643629625887759,"score_spread":0.2326690982216854,"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."}}