{"id":"W1973559979","doi":"10.1371/journal.pone.0101544","title":"Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq; Agriculture Food and Rural Development; Agriculture and Agri-Food Canada; University of Guelph; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; College of Engineering, Michigan State University; Michigan State University","keywords":"Imputation (statistics); Genomic selection; Single-nucleotide polymorphism; Statistics; Genotype; Bayesian probability; Best linear unbiased prediction; SNP; Biology; Genetics; Mathematics; Selection (genetic algorithm); Computer science; Artificial intelligence; Gene; Missing data","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":[0.03849337,0.001198272,0.001761315,0.0009696938,0.0007960151,0.001870672,0.001726191,0.001786228,0.001314423],"category_scores_gemma":[0.08914042,0.0007470413,0.00130773,0.001274105,0.0007623248,0.001948833,0.001957558,0.002401553,0.0008577756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006977574,"about_ca_system_score_gemma":0.001536159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00873708,"about_ca_topic_score_gemma":0.006770367,"domain_scores_codex":[0.9747584,0.02054428,0.0007450818,0.001587678,0.001954878,0.0004096724],"domain_scores_gemma":[0.8906108,0.09387532,0.003119297,0.006164069,0.005560234,0.0006704208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003078531,0.0002630685,0.1006104,0.0004842841,0.002287739,0.0002062563,0.0005029787,0.5873807,0.006495717,0.005688166,0.002598403,0.2904038],"study_design_scores_gemma":[0.0001490613,0.0003459118,0.02475106,0.0001535718,0.000337171,0.0001637185,0.00009305919,0.9631881,0.004067537,0.00508874,0.001562588,0.00009960456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2099234,0.003242956,0.7792538,0.001076255,0.0001203231,0.0001319979,0.001075573,0.002084608,0.003091146],"genre_scores_gemma":[0.6807363,0.0009127453,0.3128015,0.0006905458,0.00007944894,0.0002327735,0.002686479,0.0004356074,0.00142465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03849337,"threshold_uncertainty_score":0.2035748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02066570148880977,"score_gpt":0.2744048542148585,"score_spread":0.2537391527260487,"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."}}