{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001776817,0.0003004685,0.0007024207,0.00008475896,0.0001323536,0.00000613416,0.001022494,0.0003825881,0.0000870678],"category_scores_gemma":[0.00733902,0.0002941348,0.000112147,0.0001601091,0.0003639199,0.0000120991,0.001215823,0.0002017772,0.00002447269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004314111,"about_ca_system_score_gemma":0.0002034665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005279174,"about_ca_topic_score_gemma":0.00321442,"domain_scores_codex":[0.9958622,0.0009125878,0.001174015,0.001251391,0.00009949809,0.0007003504],"domain_scores_gemma":[0.9965245,0.0005998543,0.0006353231,0.001947187,0.0001322989,0.0001608203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007167853,0.00003562832,0.9800789,0.00001666165,0.0001050281,0.000005104278,0.0001548357,0.002837325,0.010734,0.00005393918,0.0004101596,0.005496779],"study_design_scores_gemma":[0.0006809488,0.0001480684,0.9642077,0.00004090414,0.00007487529,0.00000739395,0.0001465485,0.02314146,0.0007317692,0.00906996,0.001443344,0.0003070034],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508054,0.006248615,0.04142188,0.0001961307,0.0003821934,0.0002499014,0.000464225,0.000017279,0.0002143858],"genre_scores_gemma":[0.91242,0.002612034,0.08278808,0.0004257672,0.0008917465,0.00002872752,0.0007480897,0.00003484029,0.00005065992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0413662,"threshold_uncertainty_score":0.9999511,"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."}}