{"id":"W3203415010","doi":"10.1038/s41467-021-26114-0","title":"Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thrombosis and Atherosclerosis Research Institute; McMaster University Medical Centre; Impact; McMaster University; Population Health Research Institute","funders":"Canadian Institutes of Health Research","keywords":"Exome sequencing; Exome; Rare disease; Mendelian inheritance; Genetic heterogeneity; Disease; Genetics; Population; Computational biology; Biology; Bioinformatics; Medicine; Gene; Internal medicine; Mutation; Phenotype","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":[0.01004258,0.0009848919,0.00113419,0.003869038,0.000461362,0.002065865,0.001542399,0.0012939,0.002005816],"category_scores_gemma":[0.04267224,0.0004745472,0.0009171855,0.00303941,0.0006938428,0.001418356,0.002459105,0.001559125,0.0007350018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005093041,"about_ca_system_score_gemma":0.0008709423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002010249,"about_ca_topic_score_gemma":0.002616568,"domain_scores_codex":[0.9951653,0.002312142,0.0003024491,0.001393818,0.0006689591,0.000157272],"domain_scores_gemma":[0.9843381,0.009776027,0.001867911,0.00275809,0.0009564987,0.0003035298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008863974,0.0003296676,0.322566,0.0003949344,0.002203887,0.0009673322,0.0004766628,0.3421902,0.009965475,0.01926179,0.009103139,0.2916545],"study_design_scores_gemma":[0.000179533,0.000192814,0.054044,0.0001290688,0.0002768695,0.0007142578,0.0001058834,0.882944,0.003877667,0.05260295,0.004805597,0.0001273257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2766108,0.0008837603,0.7113184,0.0005981843,0.0000903523,0.000243244,0.005363821,0.003080565,0.001810877],"genre_scores_gemma":[0.7682558,0.0003965902,0.2217311,0.0002774852,0.0001215204,0.0002986619,0.007906059,0.0002822966,0.0007304778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01004258,"threshold_uncertainty_score":0.05311084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04028420707618385,"score_gpt":0.328296585819495,"score_spread":0.2880123787433111,"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."}}