{"id":"W2015790384","doi":"10.1186/1753-6561-8-s1-s107","title":"An exploration of heterogeneity in genetic analysis of complex pedigrees: linkage and association using whole genome sequencing data in the MAP4 region","year":2014,"lang":"en","type":"article","venue":"BMC Proceedings","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto; Mount Sinai Hospital","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research; National Institutes of Health; Texas Biomedical Research Institute","keywords":"Pedigree chart; Linkage (software); Genetics; Genetic linkage; Genetic association; Allele frequency; Minor allele frequency; Whole genome sequencing; Replicate; Imputation (statistics); Biology; Allele; Computational biology; Evolutionary biology; Genome; Single-nucleotide polymorphism; Computer science; Statistics; Missing data; Gene; Mathematics; Genotype; Machine learning","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":[0.001453295,0.00008473777,0.000241552,0.0001464501,0.00003459525,0.00001337383,0.0002327387,0.0001372101,5.439915e-7],"category_scores_gemma":[0.0004186134,0.00007567836,0.00003566865,0.0003450982,0.00003343575,0.00002645069,0.00008219249,0.00005617427,9.869164e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004211401,"about_ca_system_score_gemma":0.00003240525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000204588,"about_ca_topic_score_gemma":0.001189212,"domain_scores_codex":[0.9989437,0.0001245183,0.0003864904,0.0002871067,0.0001124055,0.0001458107],"domain_scores_gemma":[0.999194,0.00003898497,0.0003936644,0.0002434366,0.0001088677,0.00002109347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007794109,0.00002434063,0.7692276,0.00002702719,0.00003225612,5.858356e-8,0.0004945002,0.00246328,0.2276102,0.00001697988,0.000008481997,0.00008742666],"study_design_scores_gemma":[0.0002361318,0.0001052636,0.889744,0.000008681913,0.0001234502,0.000001317944,0.0009605465,0.1077588,0.0007672398,0.0001245343,0.00008574875,0.00008423553],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871934,0.00009474784,0.01245558,0.00005420866,0.000009802568,0.0001344219,0.00002147181,0.000002156006,0.00003423282],"genre_scores_gemma":[0.9937057,0.00005985408,0.005824035,0.00005929001,0.00005404893,0.000006349776,0.0002804042,0.000006377455,0.000003951016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.226843,"threshold_uncertainty_score":0.3086074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1350079508666474,"score_gpt":0.3355229877490814,"score_spread":0.200515036882434,"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."}}