{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007039497,0.0006813803,0.0005777497,0.0002147906,0.0001848963,0.0002258147,0.0008291268,0.001225974,0.0001880183],"category_scores_gemma":[0.0003932075,0.000731731,0.0002332596,0.0003074118,0.0001392557,0.00001399602,0.001118412,0.0007012095,0.00007461529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001296333,"about_ca_system_score_gemma":0.0005990114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001983558,"about_ca_topic_score_gemma":0.000001517113,"domain_scores_codex":[0.9971207,0.0002418405,0.0006858405,0.0009264825,0.0004299279,0.0005951552],"domain_scores_gemma":[0.996854,0.00004335916,0.0006563264,0.00169536,0.0005270978,0.0002238789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001105458,0.0001849577,0.02501302,0.001073457,0.0005245775,0.00001875368,0.00001600301,0.002041829,0.9413655,0.001411163,0.02823732,0.000002843283],"study_design_scores_gemma":[0.001054557,0.000243201,0.05230945,0.0008733061,0.0002575835,4.612294e-8,0.000004407924,0.0009713405,0.657355,0.000009425913,0.2849865,0.001935189],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9478915,0.00506333,0.03215056,0.001204514,0.005997702,0.003120739,0.0014247,0.0007870803,0.00235983],"genre_scores_gemma":[0.9911833,0.0003192239,0.005602527,0.001736497,0.0004237127,0.0001545786,0.00001190917,0.0001093255,0.000458904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2840105,"threshold_uncertainty_score":0.9995134,"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."}}