{"id":"W4387868635","doi":"10.1101/2023.10.23.23297399","title":"Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Genome-wide association study; Single-nucleotide polymorphism; Context (archaeology); Biology; SNP; Genetics; Genetic association; Computational biology; Allele; Genome; Gene; 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":[0.001116511,0.0004758643,0.0007259121,0.0007358449,0.0003093604,0.0007900764,0.0005492038,0.0005572166,0.001840572],"category_scores_gemma":[0.002174087,0.0002980724,0.0006761608,0.0007759834,0.0003388373,0.0005403782,0.0009289091,0.0009094684,0.0003955441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003248893,"about_ca_system_score_gemma":0.0004817535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003253841,"about_ca_topic_score_gemma":0.004535901,"domain_scores_codex":[0.9996305,0.000103786,0.0000161578,0.0001189277,0.00006309906,0.00006742172],"domain_scores_gemma":[0.9993968,0.0003448403,0.00005873614,0.0001071423,0.0000405035,0.00005194878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00167658,0.0003077971,0.2047857,0.001678847,0.001771665,0.001294647,0.0008451425,0.1210815,0.4261357,0.04002617,0.0137849,0.1866114],"study_design_scores_gemma":[0.0002057895,0.0002986349,0.1880002,0.0001920782,0.001070097,0.0009006786,0.0004692482,0.5913009,0.07665695,0.105018,0.03572478,0.0001626412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7374267,0.004524677,0.2376868,0.001166342,0.0001539499,0.00003686607,0.01301824,0.002048397,0.003938005],"genre_scores_gemma":[0.9342346,0.001246394,0.05331236,0.0003836499,0.00003938834,0.00005769137,0.009019738,0.0003026481,0.001403488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003253841,"threshold_uncertainty_score":0.006469786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02710106490823685,"score_gpt":0.2688604305652175,"score_spread":0.2417593656569806,"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."}}