{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002475636,0.0001639476,0.0001441631,0.00008979765,0.000191941,0.00003220189,0.00008929684,0.0002400687,0.000009568339],"category_scores_gemma":[0.0002398582,0.0001911814,0.00005228264,0.0001713877,0.0001144932,0.000006815751,0.00003279264,0.00008326869,0.000006435621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001696291,"about_ca_system_score_gemma":0.0001222116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007264937,"about_ca_topic_score_gemma":0.001810848,"domain_scores_codex":[0.9987797,0.00008639089,0.0002690534,0.0004142835,0.0001178279,0.0003327162],"domain_scores_gemma":[0.99933,0.00003977606,0.0001444564,0.0001289484,0.0002865827,0.00007027014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004842549,0.0003108473,0.3730047,0.0000702738,0.0001621655,7.439557e-8,0.001130182,0.008445584,0.6126845,0.0007934052,0.001389671,0.001524293],"study_design_scores_gemma":[0.001461585,0.0013749,0.9708453,0.000007129116,0.00003311776,0.000003137579,0.0001034497,0.002624371,0.02002146,0.001618211,0.001669214,0.0002381669],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7261836,0.0004630031,0.2725229,0.00008646213,0.000199771,0.0004326423,0.00003869709,0.00001405856,0.00005888437],"genre_scores_gemma":[0.9505406,0.00002747916,0.04814849,0.00009145493,0.0002292216,0.00007117206,0.0001353264,0.00002647075,0.0007297865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5978405,"threshold_uncertainty_score":0.7796153,"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."}}