{"id":"W4321372059","doi":"10.3390/ani13040722","title":"Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for Growth Traits in Pigs","year":2023,"lang":"en","type":"article","venue":"Animals","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 British Columbia","funders":"Special Project for Research and Development in Key areas of Guangdong Province; Sun Yat-sen University","keywords":"Genome-wide association study; Biology; Single-nucleotide polymorphism; SNP; Genetics; Genetic association; Selection (genetic algorithm); Best linear unbiased prediction; Beef cattle; Computational biology; Genomic selection; Gene; Genotype; Computer science; Machine learning","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":[0.003502881,0.0008318527,0.0009826303,0.001192281,0.0002652394,0.0008851624,0.0005971504,0.0005324315,0.0007030898],"category_scores_gemma":[0.00243219,0.0003797449,0.001348151,0.00100353,0.0002467033,0.000395176,0.000608795,0.0004480181,0.0002467179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002383737,"about_ca_system_score_gemma":0.0006526498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00323421,"about_ca_topic_score_gemma":0.01057859,"domain_scores_codex":[0.9986816,0.000538564,0.00006597546,0.0004875633,0.0001523128,0.00007405379],"domain_scores_gemma":[0.9987913,0.0007536269,0.0001936406,0.0001153734,0.00008613073,0.00006004024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001610186,0.000379919,0.6334539,0.0002999353,0.004699624,0.0005233161,0.0001765773,0.0358847,0.1315353,0.00055044,0.000679515,0.1902066],"study_design_scores_gemma":[0.000222397,0.0007734456,0.7102504,0.00005604553,0.002692114,0.0005514172,0.0001482597,0.2665072,0.01557711,0.001412265,0.001713545,0.00009594978],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9132643,0.00202211,0.0828461,0.000201412,0.00003945508,0.00003955462,0.0005909754,0.0004751828,0.0005208048],"genre_scores_gemma":[0.9515768,0.0004104541,0.04635669,0.00007392201,0.00002610885,0.00004389431,0.0009286937,0.00007171172,0.0005118336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003502881,"threshold_uncertainty_score":0.01852518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02722979398127215,"score_gpt":0.3037241990382744,"score_spread":0.2764944050570022,"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."}}