{"id":"W3010437724","doi":"10.1186/s12864-019-6362-1","title":"Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits","year":2020,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Crop Industry Development Fund; Agriculture Food and Rural Development; Agriculture and Agri-Food Canada","funders":"Alberta Livestock and Meat Agency; Compute Canada; Western Canada Research Grid; Alberta Agriculture and Forestry","keywords":"Biology; Genetic architecture; Genetics; Genome-wide association study; Whole genome sequencing; Quantitative trait locus; Genome; Genetic association; Computational biology; Beef cattle; Gene; Single-nucleotide polymorphism; Genotype","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":[0.002076875,0.0003372653,0.0004799322,0.001316298,0.0003549627,0.0005868303,0.0004271069,0.0004613817,0.001524074],"category_scores_gemma":[0.002483767,0.0002206552,0.001122937,0.001840545,0.0004804353,0.0001988668,0.0004151351,0.000462271,0.00008791785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002089911,"about_ca_system_score_gemma":0.0002078336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003242801,"about_ca_topic_score_gemma":0.003894943,"domain_scores_codex":[0.9986944,0.0004008806,0.0001201232,0.0004790094,0.0001710595,0.0001345352],"domain_scores_gemma":[0.9981217,0.0008889397,0.0005496269,0.0002217337,0.0001115641,0.0001064781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001331688,0.00007117583,0.9579107,0.00006934255,0.0023076,0.0005192953,0.0002715054,0.00175364,0.02463995,0.0002361631,0.0001124629,0.0107764],"study_design_scores_gemma":[0.00001549857,0.00007779944,0.9962571,0.000006417517,0.0003557951,0.0003569026,0.0000528767,0.002088182,0.0005115003,0.0001563406,0.0001141974,0.000007412153],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980679,0.0002439849,0.001356271,0.00002321944,0.000003224491,0.000002566658,0.0002107508,0.00001546363,0.00007662981],"genre_scores_gemma":[0.9990023,0.00004092731,0.0006002275,0.00001279808,0.00000453157,0.000003236541,0.0002875948,0.000004307964,0.00004419014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003242801,"threshold_uncertainty_score":0.01098371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433304938487793,"score_gpt":0.2553079053609233,"score_spread":0.2309748559760454,"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."}}