{"id":"W4308957951","doi":"10.1089/cmb.2022.0067","title":"Genome-Wide Association with Uncertainty in the Genetic Similarity Matrix","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Genome-wide association study; Generalized linear mixed model; Genetic association; Similarity (geometry); Population stratification; Population; Markov chain Monte Carlo; Bayesian probability; Computational biology; Biology; Mathematics; Genetics; Statistics; Computer science; Artificial intelligence; Genotype; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059033,0.00007416439,0.0001294655,0.00005521071,0.0001166849,0.000009157915,0.0002162583,0.00003895821,0.00001356073],"category_scores_gemma":[0.00008122675,0.00005161393,0.00006081102,0.00009872628,0.00003329069,5.921024e-7,0.00007095709,0.0001582688,5.935532e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006042006,"about_ca_system_score_gemma":0.0001412254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007997898,"about_ca_topic_score_gemma":0.00001621115,"domain_scores_codex":[0.9990857,0.0002641504,0.000259399,0.0001039782,0.0001594878,0.0001272818],"domain_scores_gemma":[0.9992808,0.0001895606,0.00029563,0.00006958567,0.0001453654,0.00001906936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001766103,0.00008523973,0.3905149,0.000002852801,0.0001336995,0.00001001779,0.0002063687,0.6003523,0.007562354,0.0001350961,0.0005999858,0.0002204891],"study_design_scores_gemma":[0.0009858712,0.001390012,0.9373366,0.000001468687,0.00002794514,0.00014448,0.0003601861,0.0002967541,0.00007338658,0.007226666,0.05203643,0.0001202576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954973,0.001111803,0.001053417,0.002020619,0.0001095053,0.00008478382,0.00002518245,4.915505e-7,0.0000969086],"genre_scores_gemma":[0.9972748,0.00006486333,0.001386894,0.001062117,0.0001409523,0.000008021523,0.00003315245,0.000005107814,0.00002405737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6000556,"threshold_uncertainty_score":0.2104755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00852950180874421,"score_gpt":0.2490989920726065,"score_spread":0.2405694902638623,"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."}}