{"id":"W4225716713","doi":"10.1016/j.xhgg.2022.100090","title":"Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients","year":2022,"lang":"en","type":"article","venue":"Human Genetics and Genomics Advances","topic":"Cystic Fibrosis Research Advances","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; University of Michigan; University of North Carolina at Chapel Hill; Cystic Fibrosis Foundation","keywords":"Imputation (statistics); Genome-wide association study; Genotyping; Genotype; Genetic association; 1000 Genomes Project; Computational biology; Genetics; Medicine; Biology; Missing data; Single-nucleotide polymorphism; Statistics; Gene; Mathematics","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":[0.01567016,0.001202027,0.002282574,0.002391311,0.001029891,0.002543003,0.003329794,0.001639139,0.006628011],"category_scores_gemma":[0.04291566,0.0009451583,0.002862412,0.002989718,0.0003753328,0.001100391,0.002633538,0.00262664,0.004009596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005607572,"about_ca_system_score_gemma":0.002122208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007030572,"about_ca_topic_score_gemma":0.01004012,"domain_scores_codex":[0.9943234,0.0025094,0.0005420574,0.001679718,0.0006748664,0.0002706008],"domain_scores_gemma":[0.9820908,0.007657777,0.0008241762,0.005208783,0.003741364,0.0004770853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002918293,0.0005013956,0.183732,0.0008507124,0.004380369,0.002624664,0.0009881357,0.2715237,0.01316296,0.01247411,0.1153534,0.3914901],"study_design_scores_gemma":[0.0006738168,0.0003178899,0.02976724,0.0002902417,0.001245981,0.001257045,0.0001481487,0.8817145,0.009453008,0.03271133,0.04214486,0.0002759221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05719266,0.001636824,0.8897229,0.001467237,0.0004900205,0.000224498,0.03603837,0.01070408,0.002523395],"genre_scores_gemma":[0.2960566,0.0008832878,0.5603735,0.002333451,0.0005339184,0.0007724621,0.1339518,0.001989951,0.003104934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01567016,"threshold_uncertainty_score":0.08287275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108922250732254,"score_gpt":0.2936597822772448,"score_spread":0.2725705597699223,"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."}}