{"id":"W2028339065","doi":"10.1371/journal.pone.0116487","title":"Performance of Genotype Imputation for Low Frequency and Rare Variants from the 1000 Genomes","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University","funders":"National Key Research and Development Program of China; Canadian Institutes of Health Research; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China; Wellcome Trust","keywords":"Imputation (statistics); International HapMap Project; 1000 Genomes Project; Genome-wide association study; Minor allele frequency; Biology; Single-nucleotide polymorphism; Genetics; Allele frequency; Concordance; Genotype; Genome; Statistics; Computational biology; Missing data; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02970832,0.001386365,0.002773929,0.00178706,0.0008082318,0.002653555,0.00212756,0.001445379,0.008574598],"category_scores_gemma":[0.07354693,0.0008909692,0.003363742,0.003785069,0.0004049782,0.001214189,0.001525078,0.002088692,0.004439247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007163368,"about_ca_system_score_gemma":0.001896599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008115196,"about_ca_topic_score_gemma":0.00479891,"domain_scores_codex":[0.9816119,0.01040137,0.001683666,0.004023232,0.001739189,0.0005406212],"domain_scores_gemma":[0.9786164,0.01294862,0.001249406,0.004767566,0.002153184,0.0002647702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006423552,0.0006266751,0.3712811,0.001554973,0.009279982,0.0006952503,0.0008504314,0.1580244,0.006126389,0.008067876,0.0835445,0.3535249],"study_design_scores_gemma":[0.001394992,0.001207706,0.231974,0.0005610983,0.003508296,0.002058752,0.0002534177,0.6471382,0.01406965,0.03066014,0.06662732,0.0005464492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2190527,0.004785055,0.6682178,0.001480685,0.0005531581,0.0008668306,0.08109468,0.01447032,0.009478793],"genre_scores_gemma":[0.4895234,0.001011393,0.3479394,0.001067777,0.0001813948,0.001509676,0.1498393,0.003743314,0.005184325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02970832,"threshold_uncertainty_score":0.1571145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03178770023813087,"score_gpt":0.2370774173080431,"score_spread":0.2052897170699122,"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."}}