{"id":"W1974042140","doi":"10.1186/1471-2156-6-s1-s59","title":"Linkage and association analysis in pedigrees from different populations","year":2005,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; Population Health Research Institute; University of Toronto","funders":"Canadian Institutes of Health Research; Hospital for Sick Children; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Pedigree chart; Linkage (software); Population stratification; Population; Genetic linkage; Biology; Confusion; Genetic association; Nonparametric statistics; Genetics; Random effects model; Statistics; Evolutionary biology; Demography; Psychology; Meta-analysis; Mathematics; Medicine; Genotype; Gene","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":[0.0001856363,0.0001148062,0.0001989861,0.00008972917,0.00005235733,0.00001768421,0.00008238057,0.0002243283,0.00002724837],"category_scores_gemma":[0.0001770283,0.0001146767,0.00008467129,0.0001312265,0.00001604161,0.000001726997,0.00007258529,0.00006952856,0.000006642519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004686319,"about_ca_system_score_gemma":0.00002109328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001102876,"about_ca_topic_score_gemma":0.01428769,"domain_scores_codex":[0.9990103,0.000124247,0.0002876424,0.0002775561,0.00009566012,0.0002046204],"domain_scores_gemma":[0.9994992,0.00005138554,0.0001323639,0.0002169443,0.00004163819,0.00005845099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003726225,0.00003806326,0.9898134,0.000001415918,0.00008296582,8.853578e-8,0.00005009398,0.005754379,0.002584051,0.0000117137,0.0003257091,0.001334363],"study_design_scores_gemma":[0.0002952532,0.00003257512,0.9877259,0.000001516127,0.0001566407,2.032623e-7,0.00004216409,0.00855305,0.0005212227,0.000226882,0.002319857,0.0001247164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922742,0.001440988,0.005595081,0.0002923239,0.00006736887,0.00009073641,0.00006196403,0.000006674436,0.0001706742],"genre_scores_gemma":[0.9845164,0.000505754,0.01361669,0.0001493203,0.0002905237,0.00001306286,0.0004688443,0.00000971688,0.0004296715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0141774,"threshold_uncertainty_score":0.7972865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02334156986357013,"score_gpt":0.2824039125886958,"score_spread":0.2590623427251257,"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."}}