{"id":"W4293149736","doi":"10.1093/hmg/ddac212","title":"Large registry-based analysis of genetic predisposition to tuberculosis identifies genetic risk factors at HLA","year":2022,"lang":"en","type":"article","venue":"Human Molecular Genetics","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Biotech; Business Finland; Bristol-Myers Squibb Canada; Boehringer Ingelheim; Signe ja Ane Gyllenbergin Säätiö; Medical Research Council; AbbVie; Genentech; Yrjö Jahnssonin Säätiö; Instrumentariumin Tiedesäätiö; Pfizer","keywords":"Mendelian randomization; Tuberculosis; Human leukocyte antigen; Biology; Allele; Genome-wide association study; Haplotype; Disease; Genetic association; Immunology; Genetic predisposition; Pleiotropy; Genetics; Genotype; Single-nucleotide polymorphism; Medicine; Internal medicine; Antigen; Gene; Genetic variants; Pathology; Phenotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001948514,0.0001808823,0.0003938785,0.001031887,0.0004023239,0.000541837,0.0004775713,0.0003858474,0.002290915],"category_scores_gemma":[0.004052211,0.0002336648,0.0004523952,0.00236695,0.0002887771,0.0004068325,0.0004911653,0.0005946507,0.00040652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002281088,"about_ca_system_score_gemma":0.0004030652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004291369,"about_ca_topic_score_gemma":0.005922198,"domain_scores_codex":[0.9987056,0.0005409521,0.0001208966,0.0003255672,0.0001939245,0.0001130857],"domain_scores_gemma":[0.996765,0.0008129037,0.001039874,0.0008533591,0.0002767413,0.000252103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001225713,0.00002642705,0.9976215,0.000009357964,0.00006507451,0.0000543367,0.00002619327,0.00004792878,0.000272505,0.00005277897,0.0002660242,0.001435408],"study_design_scores_gemma":[0.00001427261,0.00004474462,0.9990958,0.000005729512,0.00005582393,0.0001558149,0.00004566905,0.0001932699,0.00008809028,0.00004756272,0.0002504985,0.000002883272],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954834,0.0004264529,0.0007258145,0.00008631851,0.00001184826,0.00003061096,0.002208811,0.0000199988,0.001006776],"genre_scores_gemma":[0.9974885,0.0001628893,0.0003490999,0.00003407443,0.00001055246,0.00002181088,0.001745489,0.000004439557,0.0001831728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004291369,"threshold_uncertainty_score":0.01030487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008288566349710515,"score_gpt":0.2286189115088204,"score_spread":0.2203303451591099,"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."}}