{"id":"W2097255826","doi":"10.1002/gepi.21842","title":"A General Efficient and Flexible Approach for Genome-Wide Association Analyses of Imputed Genotypes in Family-Based Designs","year":2014,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Canadian Institutes of Health Research; McGill University; Fonds Québécois de la Recherche sur la Nature et les Technologies; Compute Canada; Government of Canada; Université Laval","keywords":"Imputation (statistics); Single-nucleotide polymorphism; Genome-wide association study; Genetics; Genetic association; Biology; Linkage (software); Genotype; Population stratification; Linkage disequilibrium; Population; Computational biology; Missing data; Statistics; Mathematics; Medicine; Gene","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":[0.00312429,0.0002365149,0.0007476425,0.0001728867,0.00007853258,0.000004492839,0.0001985769,0.0004802257,0.000003949804],"category_scores_gemma":[0.005398693,0.0002241827,0.0001730777,0.0001731516,0.0001138602,0.000001423087,0.00008149097,0.0001031718,0.000001439679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006163133,"about_ca_system_score_gemma":0.0001088262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001570484,"about_ca_topic_score_gemma":0.00002729234,"domain_scores_codex":[0.9966527,0.001143948,0.000882816,0.0006334329,0.00007952991,0.0006076234],"domain_scores_gemma":[0.9974394,0.001379044,0.0005441764,0.0003650825,0.0001678383,0.0001044899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007579209,0.00009352188,0.6109357,0.00004694011,0.0001104284,1.15069e-7,0.00002500601,0.3094829,0.07704094,0.000108671,0.0006371942,0.001442814],"study_design_scores_gemma":[0.001179879,0.000515645,0.8232899,0.000004918394,0.00007640913,0.000001429406,0.00002336914,0.1687482,0.003282055,0.001137497,0.001494518,0.0002462516],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5417616,0.001287609,0.4562431,0.0002237234,0.00005195515,0.0002902255,0.00001963822,0.000007764695,0.000114408],"genre_scores_gemma":[0.7356955,0.0001587924,0.2621932,0.001319364,0.0001403495,0.0001255806,0.0002555086,0.00002171003,0.00008995325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2123542,"threshold_uncertainty_score":0.9141905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05804536346300671,"score_gpt":0.3289049230196603,"score_spread":0.2708595595566536,"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."}}