{"id":"W1942933593","doi":"10.1002/gepi.21845","title":"A Note on the Efficiencies of Sampling Strategies in Two‐Stage Bayesian Regional Fine Mapping of a Quantitative Trait","year":2014,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Canadian Institutes of Health Research; Wellcome Trust","keywords":"Genome-wide association study; Bayesian probability; Genetic association; Inference; Computer science; Bayesian inference; Quantitative trait locus; Trait; SNP; Sample size determination; SNP genotyping; Tag SNP; Posterior probability; Computational biology; Biology; Statistics; Genotyping; Single-nucleotide polymorphism; Genetics; Mathematics; Artificial intelligence; Genotype","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":[0.00311087,0.0001905226,0.0005927131,0.0001168346,0.00006275436,0.0000024321,0.0002884616,0.0001779055,0.00002300625],"category_scores_gemma":[0.005036814,0.000142959,0.0001567325,0.0001837844,0.0004499705,0.000001940958,0.00008084828,0.0001627486,0.000002405123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001520717,"about_ca_system_score_gemma":0.0001204217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003041708,"about_ca_topic_score_gemma":0.0005186955,"domain_scores_codex":[0.9967701,0.001410581,0.0009153167,0.000405012,0.00008575811,0.0004131712],"domain_scores_gemma":[0.9960098,0.002859344,0.0005642691,0.0004182299,0.0001021301,0.00004617276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002228893,0.0002443934,0.2160529,0.0001243592,0.0001786261,0.000001273655,0.001484484,0.3950066,0.2153902,0.1624473,0.0008007631,0.008046294],"study_design_scores_gemma":[0.001360786,0.001842378,0.8966928,0.0001207675,0.00003360163,0.00001377903,0.00214241,0.03303222,0.003855051,0.05552607,0.004930655,0.0004494613],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7420195,0.000442575,0.2553542,0.001394183,0.00006122945,0.0001690873,0.0000184412,0.000003834318,0.000536926],"genre_scores_gemma":[0.9347601,0.00008550956,0.0643378,0.0006339228,0.00006856201,0.00003067057,0.00002892645,0.00001313053,0.0000413791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.68064,"threshold_uncertainty_score":0.6029901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0743092191937171,"score_gpt":0.3537292724985391,"score_spread":0.279420053304822,"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."}}