{"id":"W4385950151","doi":"10.3389/fgene.2023.1118308","title":"Feet and legs malformation in Nellore cattle: genetic analysis and prioritization of GWAS results","year":2023,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"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","keywords":"Biology; Single-nucleotide polymorphism; Candidate gene; Genome-wide association study; Genetics; Genetic architecture; Best linear unbiased prediction; Intramuscular fat; Genetic correlation; Quantitative trait locus; Beef cattle; Gene; Genetic variation; Selection (genetic algorithm); Genotype; Animal science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002312553,0.0005528469,0.0007403691,0.001738387,0.0005342053,0.0008874426,0.0005398387,0.0005635005,0.001476598],"category_scores_gemma":[0.002080471,0.0002121287,0.001023657,0.001869268,0.0003593606,0.0002468092,0.0004383355,0.0004532797,0.0001212139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000297153,"about_ca_system_score_gemma":0.0003868364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005196822,"about_ca_topic_score_gemma":0.01333205,"domain_scores_codex":[0.9983931,0.0004919341,0.000130205,0.000616917,0.0002235276,0.0001442691],"domain_scores_gemma":[0.9986272,0.0006646456,0.0003556969,0.00009661005,0.000102525,0.0001533264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001031562,0.00007241849,0.9673456,0.0001041135,0.001191533,0.001105332,0.0002950064,0.0003979367,0.01514052,0.0001421647,0.0002696687,0.0129042],"study_design_scores_gemma":[0.00002947216,0.00007240611,0.9962367,0.00001478198,0.0004459027,0.0005004672,0.0001054224,0.001762908,0.0004086175,0.00006289865,0.000351515,0.0000088521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977741,0.0004740962,0.001059336,0.00005976429,0.000009165833,0.00001206698,0.0002885869,0.00001912369,0.0003038189],"genre_scores_gemma":[0.9972019,0.0001251414,0.001981837,0.00003162716,0.00001333446,0.00001186491,0.0003851961,0.00001262007,0.0002364176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005196822,"threshold_uncertainty_score":0.0122301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006074339362986526,"score_gpt":0.2217584750132903,"score_spread":0.2156841356503038,"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."}}