{"id":"W2895768879","doi":"10.1186/s12711-018-0405-y","title":"Genome-wide association scan for heterotic quantitative trait loci in multi-breed and crossbred beef cattle","year":2018,"lang":"en","type":"article","venue":"Genetics Selection Evolution","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Ministry of Agriculture and Forestry; Agriculture Food and Rural Development; Agriculture and Agri-Food Canada; Canadian Natural Resources; University of Alberta","funders":"Alberta Innovates; Alberta Innovates Bio Solutions; Beef Cattle Research Council; University of Alberta; Agriculture and Agri-Food Canada; Alberta Livestock and Meat Agency; Agriculture Funding Consortium; Genome Alberta; Alberta Agriculture and Forestry; Genome Canada","keywords":"Biology; Single-nucleotide polymorphism; Crossbreed; Purebred; Beef cattle; Genome-wide association study; Breed; Genetics; Genetic association; Quantitative trait locus; Marbled meat; SNP; Heterosis; Candidate gene; Genotype; Gene; Agronomy","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":[0.002144548,0.0002712141,0.0005415858,0.001240119,0.0005396491,0.000523727,0.0004319435,0.0004053927,0.002103568],"category_scores_gemma":[0.001715309,0.0001841531,0.000781725,0.001528522,0.0002619689,0.0001448066,0.0004995039,0.0004859579,0.0001474721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002377051,"about_ca_system_score_gemma":0.0002816606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003257866,"about_ca_topic_score_gemma":0.005105315,"domain_scores_codex":[0.9986266,0.0002926774,0.00008257456,0.0006753943,0.0001916539,0.0001310549],"domain_scores_gemma":[0.998128,0.0006934217,0.0005396108,0.0002030166,0.0001804372,0.0002555955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001411999,0.0001082716,0.9598717,0.00009985027,0.002300502,0.0004902182,0.0002728111,0.0004099438,0.02591097,0.0001386999,0.0003807899,0.008604299],"study_design_scores_gemma":[0.0000354523,0.0000996923,0.9975168,0.000007467602,0.0004037076,0.0003297144,0.00004479211,0.0006312187,0.0005134268,0.00005558866,0.000356267,0.000005870238],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980084,0.0003752098,0.0007454196,0.00002576559,0.00000634385,0.000005463892,0.0006865317,0.0000165268,0.0001304213],"genre_scores_gemma":[0.9973134,0.0000663613,0.001080949,0.0000331888,0.000009476061,0.00001738344,0.00132326,0.00001063515,0.0001452927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003257866,"threshold_uncertainty_score":0.01134157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897868594871903,"score_gpt":0.2725054292982926,"score_spread":0.2535267433495735,"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."}}