{"id":"W2982213743","doi":"10.1101/813170","title":"Positioning Personal Polygenic Risk score against the population background","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; Dementias Platform UK; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Medical Research Council; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; UK Dementia Research Institute; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Disease; Population; Context (archaeology); Polygenic risk score; Genotyping; Biology; Genetics; Medicine; Environmental health; Single-nucleotide polymorphism; Genotype; Pathology","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":[0.005323573,0.0005596029,0.0008441657,0.002011918,0.0003884144,0.002019374,0.0009272878,0.001070224,0.005442655],"category_scores_gemma":[0.02538966,0.0002906393,0.0006048367,0.002395571,0.001116612,0.001217008,0.001171725,0.001242157,0.0009653193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005676534,"about_ca_system_score_gemma":0.0004787265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007494099,"about_ca_topic_score_gemma":0.003240213,"domain_scores_codex":[0.9963481,0.001816839,0.0001313823,0.001271199,0.0003089235,0.0001235789],"domain_scores_gemma":[0.9905778,0.005802835,0.0008605085,0.001813589,0.0007597098,0.0001855796],"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":[0.0005384879,0.000119065,0.4390079,0.0003483088,0.001295271,0.001127908,0.000924559,0.2799119,0.007299669,0.08038842,0.01087435,0.1781643],"study_design_scores_gemma":[0.0000841384,0.0001829726,0.1495222,0.0001827032,0.0003302222,0.00107347,0.000283933,0.6762343,0.004597204,0.1530876,0.01427585,0.0001454176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3180275,0.0006954301,0.6675794,0.001069777,0.0001563291,0.00009318993,0.00653904,0.001406019,0.004433304],"genre_scores_gemma":[0.9241557,0.0002775491,0.07048637,0.0001719289,0.0001058891,0.00008857749,0.003126026,0.0001364852,0.001451525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007494099,"threshold_uncertainty_score":0.02815408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473365141076311,"score_gpt":0.2336448278701211,"score_spread":0.218911176459358,"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."}}