{"id":"W3110025348","doi":"10.1371/journal.pone.0239189","title":"Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Cystic Fibrosis Research Advances","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Institute of General Medical Sciences; North Carolina State University; Vanderbilt University; Cystic Fibrosis Foundation","keywords":"Genome-wide association study; Biology; Candidate gene; Expression quantitative trait loci; Genetics; Genetic association; Population; Imputation (statistics); Gene expression profiling; 1000 Genomes Project; Gene; Gene expression; Single-nucleotide polymorphism; Genotype; 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":[0.01078986,0.001029628,0.002535839,0.00535127,0.0007987265,0.001816067,0.002282233,0.001684575,0.005011083],"category_scores_gemma":[0.0250236,0.0007276169,0.003614871,0.007522337,0.0006707077,0.0006506615,0.001738546,0.002374712,0.0008770812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006645847,"about_ca_system_score_gemma":0.001399262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009581524,"about_ca_topic_score_gemma":0.01027504,"domain_scores_codex":[0.993955,0.001830161,0.0007100864,0.00230417,0.0006823919,0.0005182368],"domain_scores_gemma":[0.9814695,0.0137746,0.001165865,0.002528646,0.0008541482,0.0002072544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001747079,0.0002950618,0.7621285,0.0009476446,0.007607088,0.004556388,0.000538559,0.0564974,0.02114223,0.008188977,0.01365415,0.1226968],"study_design_scores_gemma":[0.001491067,0.0005434346,0.4547234,0.0002470773,0.006525254,0.003884236,0.0005735754,0.4541531,0.009684497,0.05086496,0.01707149,0.0002378684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5446126,0.003516866,0.3732895,0.001763999,0.0001668612,0.0004063815,0.07108613,0.003397091,0.001760659],"genre_scores_gemma":[0.8121183,0.0008358624,0.1051074,0.0008787789,0.0001203678,0.0005985809,0.07917572,0.0003228096,0.0008422848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01078986,"threshold_uncertainty_score":0.05706298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1047738342321897,"score_gpt":0.335285391396434,"score_spread":0.2305115571642443,"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."}}