{"id":"W3101071812","doi":"10.1038/s41598-020-75758-3","title":"Genome-wide association study to identify genomic regions and positional candidate genes associated with male fertility in beef cattle","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agriculture and Agri-Food Canada; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Beef Farmers of Ontario; Ontario Ministry of Agriculture, Food and Rural Affairs; Beef Cattle Research Council; Alberta Beef Producers","keywords":"Biology; Fertility; Beef cattle; Genetics; Candidate gene; Gene; Genome-wide association study; Genome; Male fertility; Genetic association; Computational biology; Biotechnology; Bioinformatics; Single-nucleotide polymorphism; Genotype; Medicine; Population; Environmental health","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":[0.00105344,0.0003191446,0.0004489971,0.0009932877,0.0004233579,0.0004420449,0.0002623739,0.0003619808,0.001634357],"category_scores_gemma":[0.0008871031,0.0001522604,0.001159678,0.001687562,0.0001834755,0.0001489569,0.0002818908,0.0004763061,0.0001276829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711276,"about_ca_system_score_gemma":0.0003397558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004977131,"about_ca_topic_score_gemma":0.006950691,"domain_scores_codex":[0.999392,0.0001667914,0.00004698604,0.0002171167,0.00009062258,0.00008639559],"domain_scores_gemma":[0.9994016,0.0002538044,0.0001440184,0.00004321372,0.00006100555,0.00009640359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001161848,0.0001381788,0.9441523,0.0001459301,0.002844319,0.000856123,0.0002528386,0.0004121095,0.03652693,0.0001919299,0.0004089446,0.01290864],"study_design_scores_gemma":[0.00002333129,0.000125102,0.9978022,0.000007045649,0.0003872998,0.0002207699,0.00007648823,0.000645076,0.0003463735,0.0000438782,0.0003182943,0.000004201091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968855,0.001121164,0.001186844,0.00006612389,0.00001567835,0.000009018944,0.0004227324,0.0000172419,0.0002757091],"genre_scores_gemma":[0.9974909,0.0002552186,0.001230162,0.00004420854,0.00001420811,0.000013598,0.0006341338,0.000007226956,0.00031048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004977131,"threshold_uncertainty_score":0.009896338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281016662558981,"score_gpt":0.2492372116918443,"score_spread":0.2364270450662545,"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."}}