{"id":"W4283020940","doi":"10.1038/s42003-022-03532-4","title":"Capturing additional genetic risk from family history for improved polygenic risk prediction","year":2022,"lang":"en","type":"article","venue":"Communications Biology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University Health Centre; McGill University; Jewish General Hospital","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Medical Research Council; Jewish General Hospital; Public Health Agency; Compute Canada; National Institutes of Health; Public Health Agency of Canada; Cancer Research UK; Government of Canada; Wellcome Trust","keywords":"Heritability; Polygenic risk score; Family history; Biobank; Trait; Demography; Cohort; Polygene; Medicine; Quantitative trait locus; Biology; Genetics; Internal medicine; Genotype; Computer science; 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":[0.005267946,0.000792433,0.0006687836,0.001163811,0.000365389,0.001481593,0.0006862365,0.0004662979,0.002419981],"category_scores_gemma":[0.01524186,0.0003268418,0.0008154321,0.001161535,0.0004632856,0.0009438499,0.001272806,0.0013462,0.0004311372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005259068,"about_ca_system_score_gemma":0.001054546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00968035,"about_ca_topic_score_gemma":0.01234008,"domain_scores_codex":[0.9978264,0.001549102,0.00008696656,0.0002839307,0.0001785119,0.00007508421],"domain_scores_gemma":[0.9933631,0.004778168,0.0005017846,0.0008166051,0.0003562995,0.000183987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004955613,0.0002887206,0.6159934,0.0002204852,0.001507174,0.0002975411,0.0005237156,0.1792545,0.007469469,0.01795791,0.003881625,0.1721099],"study_design_scores_gemma":[0.00004016566,0.0001816782,0.07669823,0.00009585785,0.000300035,0.0003003228,0.0001240316,0.8770562,0.001878104,0.04098594,0.002258736,0.00008062615],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3174755,0.0007679048,0.6759526,0.001339172,0.00009371711,0.00008523801,0.001408324,0.0009091134,0.001968457],"genre_scores_gemma":[0.9182166,0.0002137252,0.08010688,0.0001344218,0.00003365835,0.00004092553,0.0005898921,0.0000678616,0.0005961644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00968035,"threshold_uncertainty_score":0.02785993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02403202475002274,"score_gpt":0.2508757065434447,"score_spread":0.2268436817934219,"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."}}