{"id":"W4308772246","doi":"10.1038/s41437-022-00570-w","title":"Dissecting the loci underlying maturation timing in Atlantic salmon using haplotype and multi-SNP based association methods","year":2022,"lang":"en","type":"article","venue":"Heredity","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nature; Helsingin Yliopisto; Norges Forskningsråd; Academy of Finland; China Scholarship Council; Norsk institutt for naturforskning; Norges Miljø- og Biovitenskapelige Universitet; Helsingin ja Uudenmaan Sairaanhoitopiiri","keywords":"Biology; Salmo; Locus (genetics); Genetic architecture; SNP; Genetics; Evolutionary biology; Haplotype; Allele; Genetic variation; Single-nucleotide polymorphism; Quantitative trait locus; Gene; Genotype; Fish <Actinopterygii>; Fishery","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001318328,0.0001551496,0.0002422157,0.0007095315,0.0002382571,0.0003241329,0.0002915438,0.0002990835,0.0008789142],"category_scores_gemma":[0.002049557,0.0001314542,0.0004007876,0.000725175,0.0001816135,0.0002297541,0.0003679717,0.0003788939,0.000189109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001495287,"about_ca_system_score_gemma":0.0002581457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0016267,"about_ca_topic_score_gemma":0.004965161,"domain_scores_codex":[0.9993939,0.0002311563,0.0000442786,0.0002111786,0.00007916462,0.00004034524],"domain_scores_gemma":[0.9980186,0.001061893,0.0005653782,0.0001488079,0.0001008235,0.0001045592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004628009,0.000106368,0.7364314,0.0001605371,0.0006737118,0.000275826,0.0004735437,0.00681718,0.194164,0.000896719,0.0001256394,0.05941219],"study_design_scores_gemma":[0.00003171275,0.0001736684,0.9503811,0.00002700463,0.000173146,0.0002847294,0.0001470459,0.03928369,0.007773805,0.0009824507,0.0007099879,0.00003161524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802583,0.0002446091,0.0188784,0.00002763761,0.000005559311,0.000008248018,0.0003115664,0.00002288625,0.0002427176],"genre_scores_gemma":[0.9867759,0.00007644331,0.01256893,0.00001946718,0.000005548922,0.00001166289,0.0003503101,0.000009482705,0.0001822371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0016267,"threshold_uncertainty_score":0.006972015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09442583106067821,"score_gpt":0.3624284755222986,"score_spread":0.2680026444616204,"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."}}