{"id":"W4205256416","doi":"10.1101/2022.01.06.22268853","title":"Capturing additional genetic risk from family history for improved polygenic risk prediction","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Medical Research Council; Compute Canada; Canadian Institutes of Health Research; Jewish General Hospital; University of Bristol; Public Health Agency; Fonds de Recherche du Québec - Santé; National Institutes of Health; McGill University; Public Health Agency of Canada; Cancer Research UK; Wellcome Trust","keywords":"Heritability; Polygenic risk score; Family history; Biobank; Trait; Demography; Cohort; Polygene; Medicine; Quantitative trait locus; Biology; Genetics; Internal medicine; Computer science; Genotype; Gene; Single-nucleotide polymorphism","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":[0.005156435,0.0007714222,0.0007221403,0.001059622,0.0003006442,0.00160663,0.0007365486,0.0005343018,0.002608397],"category_scores_gemma":[0.01303206,0.0003306258,0.0007923364,0.001049586,0.0005086959,0.0008392856,0.001269822,0.001369015,0.0005724279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005062831,"about_ca_system_score_gemma":0.0009664477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006242765,"about_ca_topic_score_gemma":0.006729434,"domain_scores_codex":[0.9981864,0.001260329,0.00006339342,0.0002627639,0.0001582107,0.00006884303],"domain_scores_gemma":[0.9949189,0.003643236,0.0003945682,0.0006672262,0.0002131266,0.0001629423],"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":[0.0005840124,0.000318664,0.4391311,0.0002960589,0.001646076,0.0003605617,0.0005507625,0.2891631,0.009550604,0.03453022,0.008224245,0.2156445],"study_design_scores_gemma":[0.00005288618,0.0001031754,0.03230945,0.00007256097,0.0001791497,0.000207893,0.00006834028,0.9001291,0.001705362,0.06260122,0.002512307,0.00005857641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2275716,0.000908225,0.7641643,0.001763291,0.0001100386,0.00005650855,0.001983639,0.001601051,0.001841429],"genre_scores_gemma":[0.8684438,0.0003354008,0.1285776,0.0002020625,0.00006660519,0.0000466987,0.001168364,0.0001620339,0.0009974894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006242765,"threshold_uncertainty_score":0.0272702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159659119057457,"score_gpt":0.2301370199370544,"score_spread":0.2141711080313087,"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."}}