{"id":"W2968316706","doi":"10.1186/s13072-019-0296-3","title":"Accurate ethnicity prediction from placental DNA methylation data","year":2019,"lang":"en","type":"article","venue":"Epigenetics & Chromatin","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; BC Children's Hospital; Canada Research Chairs; University of British Columbia","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; National Institutes of Health","keywords":"Population stratification; Biology; Ancestry-informative marker; Confounding; Population; Ethnic group; Single-nucleotide polymorphism; Genotyping; SNP; Genetics; Genetic association; DNA methylation; Evolutionary biology; Bioinformatics; Computational biology; Genotype; Demography; Gene; Medicine; Internal medicine","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":[0.002547543,0.0005853684,0.0005173841,0.001611198,0.0003502115,0.0008754953,0.0005528103,0.0005485778,0.00149206],"category_scores_gemma":[0.01046661,0.0002516641,0.000482392,0.0007545308,0.0002099101,0.0004472847,0.001041388,0.0006132404,0.0008210646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004666534,"about_ca_system_score_gemma":0.0004838122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004960075,"about_ca_topic_score_gemma":0.007715839,"domain_scores_codex":[0.9990603,0.0003457443,0.00006121559,0.000290593,0.0001804544,0.00006164653],"domain_scores_gemma":[0.9969329,0.001594983,0.0004667123,0.0003623125,0.0005371795,0.0001059098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005722488,0.00005391251,0.8140292,0.000131591,0.0003232629,0.0004638465,0.0002283227,0.019043,0.01852607,0.000598748,0.004775049,0.1412549],"study_design_scores_gemma":[0.00006841688,0.0001530547,0.4410995,0.0001210721,0.0002773826,0.001541178,0.0004690782,0.5033702,0.0360782,0.006407929,0.01034415,0.00006981906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7949615,0.0008210386,0.1890103,0.0004627996,0.00007020235,0.0001342796,0.008694064,0.001801718,0.004044079],"genre_scores_gemma":[0.9319094,0.0002641846,0.0600091,0.0001566483,0.00003931232,0.00009434672,0.006041874,0.0001292587,0.001355806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004960075,"threshold_uncertainty_score":0.01347286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02726001531824641,"score_gpt":0.2858376523860335,"score_spread":0.2585776370677871,"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."}}