{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006916554,0.0004751175,0.000499856,0.0001271674,0.0002161729,0.00006873638,0.0003237345,0.001153639,0.00002250631],"category_scores_gemma":[0.0003251787,0.0004731429,0.0001480274,0.0001284712,0.0002024562,0.000006270319,0.0006599864,0.0003957605,0.00001850317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007441325,"about_ca_system_score_gemma":0.0002514223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004270099,"about_ca_topic_score_gemma":0.000005971555,"domain_scores_codex":[0.9974083,0.0002508165,0.000538039,0.001100718,0.0001810992,0.0005209778],"domain_scores_gemma":[0.9978811,0.00002315944,0.0004875169,0.001042543,0.0003134827,0.0002522036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003844847,0.0000685544,0.144376,0.00008982725,0.0002039765,0.000006604803,0.000008127884,0.00001254953,0.8523686,0.00001841345,0.002792819,0.00001611996],"study_design_scores_gemma":[0.000770674,0.0003829677,0.8718781,0.0002052077,0.0001957778,1.886787e-7,0.000006357559,0.0009328459,0.1130166,0.00001155062,0.01172898,0.0008706961],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846108,0.002281633,0.01102903,0.0001910298,0.001056558,0.0005168896,0.0001917498,0.00009968913,0.00002260955],"genre_scores_gemma":[0.9912748,0.001017435,0.006154099,0.0002147773,0.001103639,0.0001023355,0.000005459411,0.00009281166,0.00003464588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.739352,"threshold_uncertainty_score":0.999772,"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."}}