{"id":"W2951172702","doi":"10.1002/gepi.22155","title":"Inferring disease risk genes from sequencing data in multiplex pedigrees through sharing of rare variants","year":2018,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Institute of Dental and Craniofacial Research; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Pedigree chart; Phenocopy; Genetics; Biology; Computational biology; Exome sequencing; Haplotype; Statistic; Gene; Mutation; Statistics; Phenotype; Mathematics; Genotype","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007025996,0.0007458296,0.0007554535,0.003371885,0.0005312152,0.001115394,0.0005133594,0.0004625824,0.0009696993],"category_scores_gemma":[0.02208529,0.0005347063,0.0007035044,0.002436748,0.0005374966,0.0008397371,0.0014208,0.0004495972,0.0001616424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003189724,"about_ca_system_score_gemma":0.000395853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002915258,"about_ca_topic_score_gemma":0.004298394,"domain_scores_codex":[0.9952305,0.002818986,0.0003281063,0.001061444,0.0003702413,0.00019066],"domain_scores_gemma":[0.9853477,0.01069529,0.002158207,0.00123113,0.000261693,0.000305911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009595051,0.00009173872,0.8304195,0.0003000392,0.002150337,0.003885294,0.001458283,0.0531852,0.01902802,0.005575568,0.0005349536,0.08241154],"study_design_scores_gemma":[0.0002304556,0.0005566581,0.4337284,0.0002141465,0.001785339,0.007419483,0.0009289317,0.4813165,0.01281849,0.05595918,0.004893962,0.0001483925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9206402,0.0003585649,0.07749525,0.0000702386,0.0000120191,0.00005017065,0.0006171428,0.0001433395,0.000613105],"genre_scores_gemma":[0.9752834,0.0001608444,0.02378643,0.00003894901,0.00001701389,0.00003587538,0.0004987967,0.00002295289,0.0001557745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007025996,"threshold_uncertainty_score":0.03715748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0961852205843236,"score_gpt":0.3469074023304707,"score_spread":0.2507221817461471,"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."}}