{"id":"W4385320108","doi":"10.1101/2023.07.24.550431","title":"SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals","year":2023,"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":"University of Toronto; McGill University","funders":"","keywords":"Colocalization; Genome-wide association study; Linkage disequilibrium; Computer science; Computational biology; Inference; Locus (genetics); Genetic association; Statistical power; False positive paradox; Biology; Statistics; Machine learning; Artificial intelligence; Genetics; Neuroscience; Allele; Mathematics; Single-nucleotide polymorphism; Genotype; Gene; Haplotype","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004555666,0.001171218,0.001668474,0.001985148,0.000976063,0.001955242,0.002687221,0.001452697,0.009107064],"category_scores_gemma":[0.01261924,0.0009441256,0.002057685,0.001437618,0.001187818,0.0015363,0.002736652,0.001618396,0.00189292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006874815,"about_ca_system_score_gemma":0.002143387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005685578,"about_ca_topic_score_gemma":0.0071858,"domain_scores_codex":[0.9983236,0.0006846033,0.00007146326,0.0004596792,0.0003634259,0.00009728016],"domain_scores_gemma":[0.9955406,0.002736391,0.0003204065,0.0007660343,0.0004496779,0.0001869417],"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":[0.001237586,0.0002029735,0.0173387,0.0009305095,0.00152014,0.001568209,0.0005972252,0.3520558,0.03326977,0.0916087,0.03658394,0.4630864],"study_design_scores_gemma":[0.0001589843,0.00004956914,0.001481308,0.00002679883,0.00009434762,0.0004442699,0.00004857451,0.9335986,0.005287279,0.05114814,0.007604122,0.00005785533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005388601,0.0001183797,0.9899635,0.0001223692,0.00002546537,0.00004060473,0.0003294057,0.003591301,0.0004203335],"genre_scores_gemma":[0.1638195,0.0001642268,0.8276048,0.0002426779,0.00009919925,0.0002808839,0.001576963,0.003320316,0.002891392],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009107064,"threshold_uncertainty_score":0.03046614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03068822115407796,"score_gpt":0.2925457188812561,"score_spread":0.2618574977271781,"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."}}