{"id":"W3003832363","doi":"10.1101/2020.02.03.931519","title":"Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thrombosis and Atherosclerosis Research Institute; McMaster University; Population Health Research Institute","funders":"","keywords":"Exome sequencing; Exome; Mendelian inheritance; Rare disease; Genetic heterogeneity; Disease; Genetics; Biology; Rare events; Population; Computational biology; Inheritance (genetic algorithm); Sequence (biology); Gene; Bioinformatics; Medicine; Internal medicine; Mutation; Phenotype; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008393314,0.0009430571,0.001056158,0.003704147,0.0003726631,0.001994654,0.001259409,0.00124763,0.001657013],"category_scores_gemma":[0.03082734,0.0004571686,0.0007726453,0.002649811,0.0006037367,0.001180217,0.002015044,0.001502713,0.000720642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005145862,"about_ca_system_score_gemma":0.0007708457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044007,"about_ca_topic_score_gemma":0.002163501,"domain_scores_codex":[0.9958502,0.00219734,0.0001889438,0.001090516,0.0005342073,0.0001387568],"domain_scores_gemma":[0.9872494,0.007893668,0.001535038,0.002257442,0.0007635957,0.0003009122],"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":[0.001014705,0.0003371767,0.2799962,0.0004144679,0.002002842,0.0007594387,0.0003275416,0.4054044,0.012342,0.01620932,0.01202737,0.2691644],"study_design_scores_gemma":[0.0001111601,0.0001044961,0.02967446,0.00007270532,0.000124479,0.0003264034,0.00006185721,0.929848,0.003859241,0.03287571,0.002872503,0.00006900414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2774484,0.0009316132,0.7081662,0.0007491818,0.0001299838,0.0001683322,0.006444588,0.004460533,0.001501097],"genre_scores_gemma":[0.7672698,0.0002909569,0.2235891,0.0002170675,0.0001406576,0.0001808775,0.007305388,0.0002809384,0.0007251624],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008393314,"threshold_uncertainty_score":0.04438865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03241086081255389,"score_gpt":0.2584642928748964,"score_spread":0.2260534320623425,"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."}}