{"id":"W1700776399","doi":"10.1186/s12863-015-0251-7","title":"Strategies for genotype imputation in composite beef cattle","year":2015,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; BIO (Canada)","funders":"Universidade Estadual Paulista; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; University of Guelph","keywords":"Imputation (statistics); Biology; Crossbreed; Zebu; Genetics; Genotyping; Linkage disequilibrium; Sire; Population; Genotype; SNP genotyping; SNP; Beef cattle; Genome-wide association study; Single-nucleotide polymorphism; Animal science; Statistics; Missing data; Gene; Demography","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":[0.01758366,0.0009274905,0.001379634,0.001975769,0.0008387743,0.001732119,0.003097059,0.001207987,0.006372227],"category_scores_gemma":[0.03337275,0.0007440633,0.001590114,0.003002948,0.0006335673,0.001434478,0.001982524,0.001979591,0.001233776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007960947,"about_ca_system_score_gemma":0.001633423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004703492,"about_ca_topic_score_gemma":0.00445959,"domain_scores_codex":[0.9958522,0.002546324,0.000186726,0.0007924741,0.0004345314,0.0001877418],"domain_scores_gemma":[0.9846528,0.01128826,0.0007558098,0.001377738,0.00160888,0.0003164826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001588233,0.0003013043,0.03477618,0.0004257915,0.001160801,0.0005056636,0.0007458129,0.5151293,0.0023136,0.02609395,0.006799054,0.4101604],"study_design_scores_gemma":[0.0000968506,0.0001382576,0.006041721,0.00005851306,0.0001168747,0.0001622973,0.00009896408,0.9541622,0.001200577,0.03517982,0.002705941,0.0000379352],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02400981,0.000320892,0.9731419,0.0002352611,0.00004063504,0.0001593406,0.0004883509,0.0005212442,0.001082532],"genre_scores_gemma":[0.1970051,0.0002559629,0.7968867,0.0002029711,0.00007179366,0.0004967679,0.003041275,0.000219937,0.001819443],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01758366,"threshold_uncertainty_score":0.09299237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03196956242618279,"score_gpt":0.2845641036133206,"score_spread":0.2525945411871378,"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."}}