{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001287867,0.0001266541,0.0001066548,0.00003415877,0.000031136,0.00002890372,0.0001570348,0.0001275307,0.000003000462],"category_scores_gemma":[0.00002525961,0.0001327128,0.00004270464,0.00006215154,0.00005072871,0.000002431423,0.00005092172,0.00004380892,0.00001032396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000136535,"about_ca_system_score_gemma":0.0002858056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000169605,"about_ca_topic_score_gemma":0.0002070463,"domain_scores_codex":[0.9992083,0.00003942618,0.0001904376,0.0002503337,0.00009469775,0.0002168519],"domain_scores_gemma":[0.9995168,0.00001477392,0.00005122648,0.000217948,0.0001103602,0.0000888434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001171289,0.000612431,0.1185817,0.0002729418,0.0001257939,0.00000118565,0.003175022,0.6120381,0.2013246,0.03036734,0.01531571,0.01701391],"study_design_scores_gemma":[0.007768404,0.004110662,0.7047369,0.00003649676,0.0001173301,0.00003523901,0.003757034,0.004027463,0.08685703,0.1269456,0.06012195,0.001485923],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8170002,0.001581599,0.1795996,0.0000258723,0.0002439186,0.0002943427,0.00002180445,0.000009391641,0.001223349],"genre_scores_gemma":[0.8369219,0.00001306281,0.1623185,0.00008272983,0.0002197528,0.0000349607,0.0001375878,0.0000210903,0.0002503534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6080106,"threshold_uncertainty_score":0.5411872,"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."}}