{"id":"W2973893144","doi":"10.1038/s41467-019-12131-7","title":"Discovering genetic interactions bridging pathways in genome-wide association studies","year":2019,"lang":"en","type":"article","venue":"Nature Communications","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; National Institute of General Medical Sciences; National Institute of Mental Health; Canadian Institute for Advanced Research; National Institute of Diabetes and Digestive and Kidney Diseases; University of Minnesota; National Cancer Institute; U.S. Department of Health and Human Services; National Institutes of Health; National Science Foundation","keywords":"Genome-wide association study; Genetic association; Computational biology; Genetic architecture; Biology; Disease; Model organism; Genome; Genetics; Single-nucleotide polymorphism; Phenotype; Gene; Genotype; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003865475,0.0001202317,0.0001908866,0.00008785581,0.0001360578,0.00001906281,0.0004455752,0.0002336154,0.00001037345],"category_scores_gemma":[0.001672467,0.0001260979,0.0000829443,0.0001835863,0.00003112777,0.000008511929,0.0004022586,0.0004805712,0.00004498137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002039806,"about_ca_system_score_gemma":0.00005767606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003053309,"about_ca_topic_score_gemma":0.001906538,"domain_scores_codex":[0.9989073,0.0002167103,0.0003086978,0.0002439655,0.00008803479,0.0002352912],"domain_scores_gemma":[0.998242,0.0004425705,0.0001981007,0.0009395954,0.000144951,0.00003277411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004454385,0.00005403242,0.9832218,0.000006548752,0.00010138,2.383892e-7,0.0002232026,0.00130311,0.0122786,0.0001695001,0.002308327,0.000328833],"study_design_scores_gemma":[0.0002684784,0.00003354307,0.9476997,0.0000185271,0.00001834053,0.00000222385,0.0005461583,0.0006350082,0.0002619332,0.0001978621,0.05015175,0.0001664468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845544,0.008538534,0.0002349161,0.003915393,0.0003128098,0.0002358809,0.00002087967,0.0000165263,0.002170665],"genre_scores_gemma":[0.9907864,0.002878336,0.004120847,0.001049334,0.00008423236,0.00006193375,0.0002074084,0.00001685862,0.0007946691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04784343,"threshold_uncertainty_score":0.5142123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127955058732211,"score_gpt":0.3092040864999742,"score_spread":0.287924535912652,"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."}}