{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004096266,0.0001510488,0.000195509,0.0000740188,0.0007556991,0.000004695381,0.0007618191,0.0001841915,0.0004814025],"category_scores_gemma":[0.0005432799,0.0001715504,0.0001736401,0.00006481171,0.0002038153,0.000002573896,0.0006643234,0.0002989841,0.00001030316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002049541,"about_ca_system_score_gemma":0.0002080373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000656476,"about_ca_topic_score_gemma":0.0002508645,"domain_scores_codex":[0.998025,0.0008291144,0.0004200823,0.0004105953,0.00004232157,0.0002729094],"domain_scores_gemma":[0.9977489,0.0003681061,0.0004203658,0.001295219,0.0001048552,0.00006256352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003837461,0.0008065477,0.2490452,0.000009350826,0.001524115,3.167934e-7,0.000640517,0.00284956,0.2099592,0.0009527934,0.4807774,0.05305128],"study_design_scores_gemma":[0.0007031249,0.0003716204,0.1499695,0.000001266857,0.0001030507,0.000004280312,0.0002370258,0.008882833,0.00005766478,0.003813386,0.8356521,0.0002041183],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8534639,0.03149201,0.03898015,0.001158759,0.001352245,0.001090362,0.07105889,0.00009699188,0.001306733],"genre_scores_gemma":[0.9209244,0.003183649,0.04088627,0.0006123318,0.0003221011,0.00162887,0.03203257,0.00003227188,0.0003775837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3548747,"threshold_uncertainty_score":0.6995624,"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."}}