{"id":"W2839299106","doi":"10.1371/journal.pone.0199692","title":"Fast score test with global null estimation regardless of missing genotypes","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; Bristol-Myers Squibb; University of California, San Diego; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; National Institute on Aging; Alzheimer's Association; Japan Society for the Promotion of Science; Foundation for the National Institutes of Health","keywords":"Score test; Likelihood-ratio test; Statistics; Wald test; Estimator; Missing data; Type I and type II errors; Statistical power; Logistic regression; Covariate; Genome-wide association study; Null (SQL); Statistical hypothesis testing; False discovery rate; Mathematics; Single-nucleotide polymorphism; Genotype; Computer science; Biology; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007373692,0.001740744,0.002320845,0.002104981,0.0006577621,0.001869255,0.002896533,0.001812675,0.005759307],"category_scores_gemma":[0.05025589,0.0004826811,0.001564247,0.001955484,0.001840442,0.003079665,0.003127018,0.00222392,0.001853394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005801768,"about_ca_system_score_gemma":0.002793215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001692037,"about_ca_topic_score_gemma":0.001430665,"domain_scores_codex":[0.9940653,0.002600135,0.0003535035,0.0009762825,0.00166059,0.0003442169],"domain_scores_gemma":[0.9738206,0.0201885,0.001286612,0.001615325,0.002618903,0.0004701157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001793905,0.0002780852,0.03634808,0.0007313013,0.0005189923,0.001138569,0.0003210356,0.1327548,0.01035747,0.1086245,0.01271403,0.6944193],"study_design_scores_gemma":[0.0005263835,0.0005678589,0.007231609,0.00007503902,0.0001704272,0.001443896,0.0001694144,0.8717133,0.007426098,0.1043121,0.006238319,0.0001255008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02016216,0.0003471518,0.9763614,0.000276933,0.00009820539,0.0001642263,0.0003198259,0.001083352,0.001186687],"genre_scores_gemma":[0.3803243,0.0005874953,0.6079279,0.000654411,0.0004206347,0.001430633,0.003225144,0.0004795153,0.004949909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007373692,"threshold_uncertainty_score":0.03899628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672879900724758,"score_gpt":0.2476748995427635,"score_spread":0.2209461005355159,"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."}}