{"id":"W2891242822","doi":"10.1101/416859","title":"Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Government of Canada; Cancer Research UK; National Institutes of Health; European Commission; Canadian Institutes of Health Research; Genome Canada","keywords":"Biobank; Prior probability; Bayesian probability; Regression; Computer science; Linkage disequilibrium; Multivariate statistics; Sample size determination; Genome-wide association study; Lasso (programming language); Polygenic risk score; Statistics; Posterior probability; Artificial intelligence; Machine learning; Mathematics; Bioinformatics; Biology; Single-nucleotide polymorphism; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00855139,0.0008958986,0.001152813,0.001279159,0.0003975387,0.0009764521,0.001451834,0.001079548,0.002160863],"category_scores_gemma":[0.02192321,0.0005647024,0.001048938,0.001573396,0.001304664,0.001246111,0.001320937,0.002541157,0.0007557336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005719989,"about_ca_system_score_gemma":0.001326284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007400274,"about_ca_topic_score_gemma":0.00614566,"domain_scores_codex":[0.9973503,0.001633533,0.00009136066,0.0005484659,0.0002704556,0.0001058767],"domain_scores_gemma":[0.9866714,0.01052439,0.0008050622,0.001029426,0.0007747319,0.000194939],"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":[0.0003839518,0.0001419417,0.02272803,0.0001299438,0.0003579999,0.0001835023,0.0001593289,0.7857423,0.0033323,0.03283592,0.005113649,0.1488911],"study_design_scores_gemma":[0.00003468254,0.00001777689,0.001425549,0.00001319403,0.00001627984,0.00002095269,0.000006217056,0.9825292,0.0004489547,0.01504791,0.0004255714,0.00001376216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05622438,0.0003121372,0.9405593,0.0006595096,0.00003276192,0.00005573564,0.0003387345,0.0007626435,0.001054758],"genre_scores_gemma":[0.7177439,0.0003917876,0.2766002,0.0004751103,0.000180053,0.000214856,0.001347479,0.0001883724,0.002858183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00855139,"threshold_uncertainty_score":0.04522461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007167045890183553,"score_gpt":0.2218801174452537,"score_spread":0.2147130715550702,"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."}}