{"id":"W4362507221","doi":"10.1007/s00439-023-02548-y","title":"Predicting ExWAS findings from GWAS data: a shorter path to causal genes","year":2023,"lang":"en","type":"article","venue":"Human Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill Genome Centre; McGill University Health Centre; McGill University; Jewish General Hospital","funders":"Lady Davis Institute for Medical Research; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; McGill University; Génome Québec; Cancer Research UK; Public Health Agency of Canada; Fondation de l'Hôpital général juif","keywords":"Genome-wide association study; Biology; Locus (genetics); Computational biology; Genetics; Genetic association; Genetic architecture; Exome; Gene; Quantitative trait locus; Exome sequencing; Single-nucleotide polymorphism; Phenotype; Genotype","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":[0.0004013834,0.0002091411,0.0002282026,0.00007622669,0.0002326901,0.0000425075,0.0005835201,0.0002376656,0.00006016886],"category_scores_gemma":[0.0001671598,0.0002181832,0.00006383906,0.0001707473,0.00005178983,0.000003157854,0.0008891225,0.0001037308,0.0001701275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001805955,"about_ca_system_score_gemma":0.00005667194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001058273,"about_ca_topic_score_gemma":0.0004449656,"domain_scores_codex":[0.9981503,0.00009393578,0.0003772509,0.0007166769,0.0001699129,0.0004919068],"domain_scores_gemma":[0.998637,0.00003931451,0.00007940738,0.001007915,0.00007415464,0.0001622214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000111227,0.0000342141,0.6694351,0.000009461776,0.0001292808,0.000008550915,0.0002704124,0.0006751919,0.2573464,0.00001281564,0.06957949,0.002487946],"study_design_scores_gemma":[0.0004895304,0.0003624898,0.9087602,0.00002042948,0.00008817559,0.00000566085,0.0002853173,0.003765841,0.007819241,0.0003790607,0.07750688,0.0005171929],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964311,0.000500082,0.001495397,0.0002492215,0.0003345669,0.0002150124,0.000406123,0.00005706096,0.0003114087],"genre_scores_gemma":[0.9891034,0.0003072525,0.004188579,0.0005706512,0.001035175,0.00003698168,0.003388527,0.00005124004,0.001318191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2495271,"threshold_uncertainty_score":0.8897253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05843268201479789,"score_gpt":0.3245122584440486,"score_spread":0.2660795764292507,"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."}}