{"id":"W2979220318","doi":"10.1002/gepi.22264","title":"Population genetic simulation study of power in association testing across genetic architectures and study designs","year":2019,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Human Genome Research Institute; National Institutes of Health","keywords":"Genetic architecture; Statistical power; Biology; Genetic association; Trait; Quantitative trait locus; Genome-wide association study; Imputation (statistics); Association mapping; Genetics; Population; Genotyping; Genetic variation; Type I and type II errors; Computational biology; Evolutionary biology; Statistics; Computer science; Missing data; Genotype; Single-nucleotide polymorphism; Gene; Mathematics","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":[0.06570721,0.000681946,0.001250554,0.001207661,0.0008014281,0.00148471,0.001521372,0.001739999,0.002051681],"category_scores_gemma":[0.1852985,0.0004980944,0.001623078,0.001220132,0.002160127,0.001815597,0.001459969,0.00228411,0.0002144368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182162,"about_ca_system_score_gemma":0.001459482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00541035,"about_ca_topic_score_gemma":0.003444537,"domain_scores_codex":[0.9751874,0.02181583,0.000453111,0.001484559,0.0006567193,0.0004023357],"domain_scores_gemma":[0.6772718,0.3026558,0.00515944,0.01117413,0.002757939,0.0009808486],"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":[0.001287467,0.0002242566,0.06898648,0.0001523626,0.001463205,0.0003843945,0.0004858338,0.8607297,0.001134234,0.04463218,0.001064414,0.01945542],"study_design_scores_gemma":[0.0002295095,0.0002991999,0.005601968,0.00004292555,0.0001918979,0.00014024,0.00009278127,0.9629316,0.0005295646,0.02927224,0.0006391208,0.00002892756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6408696,0.0006538508,0.3505407,0.001711496,0.0001215479,0.0004011006,0.0005163561,0.0003893071,0.004796123],"genre_scores_gemma":[0.9478647,0.0001435749,0.05030536,0.0002853437,0.0000254464,0.0003755783,0.000357235,0.00004873198,0.0005940778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9342928,"threshold_uncertainty_score":0.3474971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04356574642480059,"score_gpt":0.3492927215759647,"score_spread":0.3057269751511641,"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."}}