{"id":"W3005673236","doi":"10.1186/s12711-020-0529-8","title":"Beyond large-effect loci: large-scale GWAS reveals a mixed large-effect and polygenic architecture for age at maturity of Atlantic salmon","year":2020,"lang":"en","type":"article","venue":"Genetics Selection Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Miljø- og Biovitenskapelige Universitet; Norges Forskningsråd; Academy of Finland","keywords":"Biology; Genetic architecture; Genome-wide association study; Selection (genetic algorithm); Maturity (psychological); Scale (ratio); Evolutionary biology; Polygenic risk score; Computational biology; Quantitative trait locus; Genetics; Gene; Single-nucleotide polymorphism; Cartography; Computer science; Genotype; Machine learning; Geography","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":[0.001873095,0.0003811983,0.0005181353,0.0008389473,0.0005112014,0.0007354813,0.0004780926,0.0004476396,0.001829072],"category_scores_gemma":[0.002095959,0.0002615918,0.001168445,0.001219345,0.0005238765,0.0002575584,0.000585172,0.0006031598,0.0001333239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001966411,"about_ca_system_score_gemma":0.0003155521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004636703,"about_ca_topic_score_gemma":0.01029531,"domain_scores_codex":[0.998719,0.0004142496,0.0001412775,0.0004708245,0.0001476534,0.0001070315],"domain_scores_gemma":[0.9974049,0.001273372,0.0005380634,0.0004112327,0.0001878334,0.0001846334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006701783,0.00005100352,0.9405153,0.00008443718,0.002359087,0.000473756,0.0002624746,0.0009720056,0.04563692,0.0003218593,0.0003487051,0.008304197],"study_design_scores_gemma":[0.00002121621,0.00004588683,0.9966497,0.000008816744,0.0003922834,0.000163795,0.00006221131,0.00165347,0.0005865203,0.0002110442,0.0001942247,0.00001071842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956838,0.0002860883,0.003214779,0.00008919345,0.000008258786,0.000006505645,0.0004198185,0.0000370233,0.000254592],"genre_scores_gemma":[0.9987041,0.00004422796,0.0008408959,0.00004156901,0.000009747816,0.0000062482,0.0002323033,0.00001375051,0.0001071689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004636703,"threshold_uncertainty_score":0.009905994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005871352035594731,"score_gpt":0.2254850313798542,"score_spread":0.2196136793442595,"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."}}