{"id":"W4413028238","doi":"10.1101/2025.08.05.668745","title":"Higher eQTL power reveals signals that boost GWAS colocalization","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Common Fund; National Institute on Drug Abuse; National Institute of Diabetes and Digestive and Kidney Diseases; NIH Office of the Director; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Human Genome Research Institute; University of Toronto","keywords":"Expression quantitative trait loci; Genome-wide association study; Colocalization; Biology; Computational biology; Quantitative trait locus; Genetics; Gene; Single-nucleotide polymorphism; Neuroscience; Genotype","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":[0.04445693,0.001087663,0.001878964,0.002781978,0.001411608,0.0032454,0.001375019,0.002849712,0.008774019],"category_scores_gemma":[0.09726305,0.0009571416,0.003110914,0.002068734,0.003514176,0.002990273,0.003443066,0.002903622,0.0007695126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007948027,"about_ca_system_score_gemma":0.0008130551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001290892,"about_ca_topic_score_gemma":0.001201156,"domain_scores_codex":[0.9761681,0.01255025,0.001974153,0.005924757,0.00232688,0.001055751],"domain_scores_gemma":[0.8436535,0.1331622,0.006125152,0.01269676,0.002949,0.001413373],"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":[0.003825594,0.0006323455,0.6208842,0.002713965,0.008735644,0.003014517,0.002738479,0.01763048,0.2016556,0.02422289,0.004071759,0.1098745],"study_design_scores_gemma":[0.001087239,0.003293426,0.766723,0.0003997696,0.008631977,0.003923791,0.0008101857,0.05373126,0.0776384,0.06554513,0.01792902,0.0002867857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7557209,0.003289808,0.2239249,0.003872247,0.0003814114,0.0003319172,0.002484888,0.002116341,0.007877469],"genre_scores_gemma":[0.9766055,0.0002157841,0.0203002,0.001374708,0.00009506546,0.0001904951,0.0005507994,0.0002295352,0.0004378098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04445693,"threshold_uncertainty_score":0.2351135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069129629804568,"score_gpt":0.2396744193289,"score_spread":0.2289831230308543,"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."}}