{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01354087,0.001428366,0.002356666,0.002818067,0.001420963,0.005893191,0.00288949,0.00357567,0.02509819],"category_scores_gemma":[0.1101274,0.001147536,0.002439733,0.002968528,0.001372765,0.006360074,0.003271593,0.01086903,0.004201202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007521061,"about_ca_system_score_gemma":0.002492102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00687975,"about_ca_topic_score_gemma":0.007614555,"domain_scores_codex":[0.9915349,0.003733627,0.001178152,0.002012542,0.001100249,0.0004405134],"domain_scores_gemma":[0.8708087,0.1074838,0.003272684,0.01064116,0.005521288,0.002272458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003831487,0.001026255,0.4258085,0.001958459,0.003728729,0.003781973,0.0007890133,0.0166055,0.009676413,0.03395595,0.03880805,0.4600298],"study_design_scores_gemma":[0.001919369,0.00214908,0.1889624,0.001980813,0.004212698,0.01023194,0.001484572,0.183163,0.007147492,0.5411565,0.05701514,0.000577125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2717213,0.01794112,0.5659963,0.09104019,0.004055379,0.0005731261,0.03428561,0.003350477,0.01103639],"genre_scores_gemma":[0.7553542,0.005726348,0.1968466,0.01306183,0.003603515,0.0003182366,0.01623512,0.001185968,0.007668154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02509819,"threshold_uncertainty_score":0.08396184,"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."}}