{"id":"W2944491831","doi":"10.1101/632786","title":"Population genetic simulation study of power in association testing across genetic architectures and study designs","year":2019,"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":"McGill University","funders":"National Institutes of Health","keywords":"Genetic architecture; Statistical power; Trait; Genetic association; Quantitative trait locus; Biology; Genome-wide association study; Association mapping; Imputation (statistics); Genotyping; Population; Population stratification; Evolutionary biology; Computational biology; Genetics; Statistics; Computer science; Machine learning; Missing data; Genotype; Mathematics; Single-nucleotide polymorphism; Gene; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03843354,0.0005291213,0.00100798,0.0009923602,0.0006807029,0.001241334,0.00128601,0.001343413,0.002480414],"category_scores_gemma":[0.1100755,0.0004149041,0.001420653,0.0009278088,0.002112942,0.001126836,0.001284733,0.001917257,0.0002163701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000950408,"about_ca_system_score_gemma":0.001034518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006143502,"about_ca_topic_score_gemma":0.003513852,"domain_scores_codex":[0.9876674,0.01062234,0.0002489193,0.0008100261,0.0003780941,0.0002732399],"domain_scores_gemma":[0.7851145,0.1970804,0.004181131,0.009513094,0.003024996,0.001085785],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001345912,0.0001830096,0.05661276,0.00009331752,0.0007733746,0.0003096435,0.0002791207,0.8984913,0.001000673,0.02797128,0.001216107,0.01172341],"study_design_scores_gemma":[0.0002606742,0.0002443421,0.005591384,0.00003911162,0.0001431788,0.0001031566,0.00008171082,0.9721761,0.0007222652,0.02014255,0.0004706997,0.00002485159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8162761,0.0003702728,0.1776988,0.001077506,0.00009290683,0.0002189824,0.0005674463,0.0003062324,0.003391627],"genre_scores_gemma":[0.9721313,0.00006951961,0.02642578,0.0001856363,0.00001929513,0.0001969404,0.0003833667,0.00003938233,0.0005488197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9615664,"threshold_uncertainty_score":0.2032584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364087442970847,"score_gpt":0.2804381495090567,"score_spread":0.2567972750793482,"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."}}