{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00241538,0.0003274651,0.0008626701,0.0001603703,0.0001099223,0.00000999623,0.0002365365,0.0004221103,0.00001983655],"category_scores_gemma":[0.00488514,0.00032603,0.00008206759,0.0002974522,0.00005156751,0.000003887182,0.0002320131,0.0002286774,0.000009258715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008442855,"about_ca_system_score_gemma":0.00004821604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268165,"about_ca_topic_score_gemma":0.001278557,"domain_scores_codex":[0.9942737,0.002431738,0.001445349,0.000949335,0.0001875064,0.00071238],"domain_scores_gemma":[0.9967352,0.001499646,0.0008696202,0.0006121704,0.000183285,0.0001000658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004614074,0.0003128411,0.7204681,0.00001168829,0.00007806884,0.000001626662,0.0006220474,0.272292,0.002065542,8.144884e-7,0.00001111545,0.004090049],"study_design_scores_gemma":[0.001740205,0.003003195,0.9802626,0.00001052599,0.00005050586,0.000008511264,0.001069704,0.01293308,0.00002680258,0.0005903612,0.00002119379,0.0002833478],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951947,0.0006798066,0.002134095,0.0000502397,0.0002212702,0.001646999,0.00001147283,0.00001705241,0.00004434554],"genre_scores_gemma":[0.9888206,0.00002996341,0.01064846,0.0001666173,0.0001048145,0.0000896374,0.00002510705,0.00004023714,0.00007453831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2597945,"threshold_uncertainty_score":0.9999192,"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."}}