{"id":"W2017753262","doi":"10.1371/journal.pone.0064343","title":"Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Wellcome Trust","keywords":"International HapMap Project; 1000 Genomes Project; Imputation (statistics); Genome-wide association study; Biology; Minor allele frequency; Genetics; Haplotype; Genome; Genetic association; Population; Genomics; Allele frequency; Computational biology; Population stratification; Linkage disequilibrium; Single-nucleotide polymorphism; Genotype; Gene; Missing data; Medicine; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.01610028,0.001531275,0.002780202,0.001891325,0.0007659007,0.002817988,0.001572265,0.001251314,0.009781012],"category_scores_gemma":[0.03429681,0.0008296687,0.002555256,0.004306499,0.0006047183,0.001425949,0.002041079,0.002084366,0.003156484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00038567,"about_ca_system_score_gemma":0.0006946311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003679067,"about_ca_topic_score_gemma":0.008219705,"domain_scores_codex":[0.9923718,0.003816098,0.0006331695,0.001954978,0.0008001766,0.0004238661],"domain_scores_gemma":[0.9728745,0.01836372,0.002145031,0.005118483,0.001113206,0.0003850397],"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":[0.002795508,0.0004045578,0.6842499,0.00151353,0.007842284,0.001990764,0.0007138755,0.01334624,0.03358659,0.004726412,0.02193591,0.2268944],"study_design_scores_gemma":[0.0007972716,0.000931272,0.8551679,0.0004696408,0.004994075,0.004692283,0.0003559932,0.04606938,0.01116391,0.02671416,0.04838062,0.000263562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.550292,0.006718351,0.3717203,0.003147716,0.0004443479,0.0004371803,0.05131653,0.004921697,0.01100183],"genre_scores_gemma":[0.7452917,0.001857009,0.199972,0.001979857,0.0003690459,0.0004897199,0.04561808,0.0009852106,0.00343733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01610028,"threshold_uncertainty_score":0.0851475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987587536804886,"score_gpt":0.2464118322331442,"score_spread":0.2265359568650953,"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."}}