{"id":"W3016661549","doi":"10.1101/2020.04.17.047225","title":"MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Medical Research Council; Gillings School of Public Health; National Institutes of Health; Canadian Institutes of Health Research; Centre hospitalier régional universitaire de Lille; Erasmus Medisch Centrum; Bundesministerium für Bildung und Forschung; Institut National de la Santé et de la Recherche Médicale; Hjartavernd; Université de Lille; European Commission; Wellcome Trust; New York Genome Center; Cancer Research UK; Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease; National Institute on Aging; Alzheimer's Association","keywords":"Genome-wide association study; Single-nucleotide polymorphism; Genetic association; Imputation (statistics); Predictive power; Gene; Transcriptome; Mediation; Additive model","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":[0.02259429,0.002274306,0.00269608,0.003527023,0.001249409,0.003948671,0.004067301,0.001861382,0.01233677],"category_scores_gemma":[0.03898956,0.001958615,0.005171135,0.003989973,0.001465126,0.002076856,0.005956843,0.004308616,0.003010172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008514675,"about_ca_system_score_gemma":0.002147315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002151059,"about_ca_topic_score_gemma":0.002840334,"domain_scores_codex":[0.9925787,0.005513389,0.0003012171,0.0008450783,0.0006118899,0.0001497079],"domain_scores_gemma":[0.9816703,0.01367237,0.0007567513,0.002781082,0.0007545466,0.000364913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001182517,0.0003101825,0.01927868,0.002059883,0.007219701,0.001902911,0.0008840326,0.3232126,0.01426745,0.1667593,0.03889596,0.4240268],"study_design_scores_gemma":[0.0002076887,0.0001071804,0.002491026,0.0001637223,0.0005602111,0.0002733339,0.0001090262,0.652328,0.002905821,0.3220313,0.01872467,0.00009809051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001134418,0.0002478206,0.9939764,0.0004015391,0.00006138116,0.00006892916,0.0009894767,0.002714731,0.0004052511],"genre_scores_gemma":[0.05221869,0.0004314067,0.9409692,0.0005354375,0.00017752,0.0009249864,0.002421767,0.001465702,0.0008552343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02259429,"threshold_uncertainty_score":0.1194915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03524793311492839,"score_gpt":0.2803421091345755,"score_spread":0.2450941760196471,"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."}}