{"id":"W4286715690","doi":"10.1186/s12864-022-08747-7","title":"Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine","year":2022,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"West Fraser (Canada); University of British Columbia; University of Alberta","funders":"Genome Alberta; Alberta Innovates; Alberta Innovates Bio Solutions; University of Alberta; Genome British Columbia; Forest Resource Improvement Association of Alberta; Genome Canada; National Science Foundation","keywords":"Biology; Trait; Genome-wide association study; Productivity; Quantitative trait locus; Evolutionary biology; Genome; Genetic association; Association mapping; Pinus contorta; Genetics; Computational biology; Ecology; Single-nucleotide polymorphism; Genotype; Gene; Computer science","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":[0.003540483,0.0006117947,0.0003969071,0.0005321324,0.0002113658,0.0006552809,0.000338266,0.0003897484,0.001018728],"category_scores_gemma":[0.00524176,0.0001594369,0.0009185234,0.0005179496,0.0002778648,0.00047321,0.0004016874,0.0006974841,0.0001539718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002361869,"about_ca_system_score_gemma":0.0002448446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001931441,"about_ca_topic_score_gemma":0.003202687,"domain_scores_codex":[0.9987017,0.0005972856,0.00005607702,0.0004644265,0.0001202185,0.00006026856],"domain_scores_gemma":[0.9953763,0.003429791,0.0003959048,0.00045533,0.0002648867,0.00007773006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001763457,0.0002829017,0.516158,0.0004346122,0.002642184,0.0004918278,0.0004330905,0.07150641,0.1400849,0.001586709,0.0006445387,0.2639714],"study_design_scores_gemma":[0.00005017012,0.0004490715,0.7441009,0.00002692329,0.0008980891,0.000340382,0.0000718427,0.2324757,0.01785245,0.002377318,0.00131229,0.0000448276],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9305451,0.0006454841,0.06747874,0.0000880174,0.00001913829,0.00001244278,0.0004097359,0.0003568385,0.0004445236],"genre_scores_gemma":[0.9902561,0.00007954706,0.009220057,0.00001377826,0.000009054939,0.000007235169,0.0002086551,0.00002567832,0.0001798873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003540483,"threshold_uncertainty_score":0.01872408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153696252798186,"score_gpt":0.2477279994299739,"score_spread":0.2261910369019921,"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."}}