{"id":"W4391849160","doi":"10.1111/eva.13651","title":"Genomic prediction based on preselected single‐nucleotide polymorphisms from genome‐wide association study and imputed whole‐genome sequence data annotation for growth traits in Duroc pigs","year":2024,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Biology; Single-nucleotide polymorphism; Genome-wide association study; Best linear unbiased prediction; Quantitative trait locus; Computational biology; Genetics; Whole genome sequencing; Annotation; Linkage disequilibrium; Genetic association; Imputation (statistics); Genome; Genomics; Gene; Genotype; Statistics; Missing data; Computer science; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002452602,0.0006557573,0.0005773957,0.0009111458,0.0005280841,0.0008864593,0.0006268197,0.0004639253,0.0009498622],"category_scores_gemma":[0.002965058,0.0002742684,0.001331826,0.0009625825,0.000462896,0.0002117563,0.0005772377,0.0006805161,0.0002282417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000376236,"about_ca_system_score_gemma":0.0005999866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01801399,"about_ca_topic_score_gemma":0.0256051,"domain_scores_codex":[0.9989352,0.0003019536,0.00005709276,0.0004490925,0.0001195537,0.0001372174],"domain_scores_gemma":[0.9987745,0.0006736813,0.0001288945,0.0002283443,0.0001183125,0.00007617923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002026657,0.0001605612,0.8843143,0.0001163001,0.001451855,0.00160636,0.0006090425,0.01580462,0.04037958,0.0005936036,0.0007748865,0.05216227],"study_design_scores_gemma":[0.00009680558,0.0001367768,0.9350706,0.00002864079,0.0008646594,0.0003050857,0.0002298149,0.05728396,0.004086152,0.0005632747,0.001295956,0.00003818767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844721,0.0001798668,0.01409872,0.00004988547,0.00001104413,0.00002066389,0.0007662075,0.00006698697,0.0003345007],"genre_scores_gemma":[0.9876672,0.0001032391,0.00832933,0.00005626891,0.00001134142,0.00004034726,0.003337679,0.00004581904,0.0004088429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01801399,"threshold_uncertainty_score":0.03581828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777937386178037,"score_gpt":0.2463373810691144,"score_spread":0.228558007207334,"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."}}