{"id":"W3211765453","doi":"10.1186/s12864-021-08064-5","title":"Identification of candidate genes and enriched biological functions for feed efficiency traits by integrating plasma metabolites and imputed whole genome sequence variants in beef cattle","year":2021,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Alberta","funders":"Genome Alberta; Alberta Innovates; Alberta Livestock and Meat Agency; University of Alberta; European Commission; Western Canada Research Grid; Compute Canada","keywords":"Biology; Metabolome; Candidate gene; Feed conversion ratio; Residual feed intake; Beef cattle; Genetics; Valine; Metabolomics; Gene; Population; Biochemistry; Amino acid; Bioinformatics; Endocrinology","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":[0.001280099,0.0004962405,0.0006387231,0.001337757,0.000314343,0.0007636735,0.0004610111,0.0004580251,0.001360162],"category_scores_gemma":[0.001502612,0.0002331997,0.001232082,0.00173345,0.0002786288,0.0001737064,0.0003915258,0.0004810516,0.0001201672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002093239,"about_ca_system_score_gemma":0.0002707596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003808251,"about_ca_topic_score_gemma":0.004107422,"domain_scores_codex":[0.9992362,0.0002389855,0.0000536955,0.0002601856,0.0001075362,0.0001032721],"domain_scores_gemma":[0.9990054,0.0005478879,0.0002181785,0.0000692328,0.00007186429,0.00008758734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00212098,0.0001395922,0.9089628,0.0001383211,0.003783644,0.0009049583,0.0001965609,0.002438631,0.06445895,0.0001281503,0.0001970115,0.01653044],"study_design_scores_gemma":[0.00003231343,0.0001627992,0.9896523,0.00001326474,0.0009321567,0.0004376833,0.00009935757,0.005841875,0.002353553,0.0001352102,0.0003249906,0.00001439767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956508,0.0005692633,0.002871913,0.00003952214,0.000008120666,0.000005612289,0.0007415503,0.00002566737,0.00008755903],"genre_scores_gemma":[0.9968208,0.0001003371,0.001971432,0.0000363693,0.00000992381,0.000008616497,0.0008711953,0.000008805939,0.0001723919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003808251,"threshold_uncertainty_score":0.007572174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825420372036384,"score_gpt":0.2460672959251631,"score_spread":0.2278130922047993,"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."}}