{"id":"W3207881611","doi":"10.1093/g3journal/jkab278","title":"Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions","year":2021,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Japan Society for the Promotion of Science London; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; Bristol-Myers Squibb; University of California, San Diego; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association; Japan Society for the Promotion of Science; Foundation for the National Institutes of Health","keywords":"Univariate; Genome-wide association study; Multivariate statistics; Genetic association; Regression; Gene–environment interaction; Additive genetic effects; Biology; Regression analysis; Additive model; Computer science; Interaction; Statistics; Computational biology; Machine learning; Mathematics; Genetics; Heritability; Single-nucleotide polymorphism; Gene","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":[0.002968419,0.0009158053,0.001350262,0.0009705602,0.0003334377,0.0008929287,0.002188226,0.000906089,0.001829637],"category_scores_gemma":[0.008696047,0.0006231522,0.001466097,0.001878462,0.0006584235,0.001390422,0.001423352,0.001866075,0.0004546818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005310425,"about_ca_system_score_gemma":0.001410483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01086327,"about_ca_topic_score_gemma":0.01136166,"domain_scores_codex":[0.9990752,0.0003846033,0.00004118355,0.0002413772,0.0001699876,0.00008760532],"domain_scores_gemma":[0.9970553,0.002101582,0.0002592483,0.0002562695,0.0002253983,0.0001022705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000138667,0.00007665926,0.01044572,0.00007137079,0.0001996012,0.0002700718,0.00008309246,0.86698,0.001685677,0.02342731,0.001550144,0.09507162],"study_design_scores_gemma":[0.000007309564,0.00001577624,0.0004350159,0.000002853382,0.00001391894,0.00002259517,0.000003037125,0.9905752,0.0001049012,0.008618676,0.0001932222,0.000007395523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01368757,0.0001012533,0.9852238,0.0002134365,0.00002075787,0.00001881514,0.0001278322,0.0003978638,0.0002087529],"genre_scores_gemma":[0.6075454,0.0006498077,0.3866868,0.0004003673,0.0001564556,0.0002225228,0.001006888,0.000241336,0.003090553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01086327,"threshold_uncertainty_score":0.02160007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186617064906386,"score_gpt":0.2555203950469007,"score_spread":0.2368586885562621,"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."}}