{"id":"W2891487057","doi":"10.1002/gepi.22153","title":"Genetic association analysis with pedigrees: Direct inference using the composite likelihood ratio","year":2018,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Frequentist inference; Likelihood function; Inference; Statistics; Likelihood principle; Mathematics; Statistical inference; Bayesian probability; Bayes factor; Bayesian inference; Computer science; Econometrics; Artificial intelligence; Quasi-maximum likelihood; Estimation theory","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00191809,0.0003476075,0.0007497785,0.0001424925,0.0004692422,0.00002347019,0.0004699258,0.0004621371,0.00008107399],"category_scores_gemma":[0.002162311,0.0002471205,0.0002617864,0.0006402358,0.000377063,0.000004674498,0.0001918344,0.000209338,0.00004416589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001028339,"about_ca_system_score_gemma":0.0002098255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005264301,"about_ca_topic_score_gemma":0.001877623,"domain_scores_codex":[0.9954823,0.001761533,0.0008442769,0.0008181092,0.000178577,0.0009151775],"domain_scores_gemma":[0.9968178,0.0008729347,0.0007920078,0.0008904738,0.0004630505,0.0001637128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004130986,0.0000363311,0.9746707,0.000004411518,0.001127291,0.000001277178,0.00009159695,0.01385739,0.00699919,0.00002381588,0.00159462,0.001552041],"study_design_scores_gemma":[0.0004246421,0.0006617846,0.9678089,0.000007281039,0.001042187,0.00002229889,0.00008674787,0.02368326,0.0006870496,0.0006021835,0.004591701,0.0003819777],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7942185,0.001127446,0.2024859,0.0008841907,0.00023588,0.0002884231,0.0000397359,0.00002521895,0.0006947079],"genre_scores_gemma":[0.9410709,0.0003846211,0.05471908,0.002170954,0.001143336,0.00005796316,0.0001102993,0.00003216683,0.0003106915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1477668,"threshold_uncertainty_score":0.9999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0235082839816757,"score_gpt":0.307064453596918,"score_spread":0.2835561696152423,"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."}}