{"id":"W4396561183","doi":"10.1093/bioinformatics/btae295","title":"SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université de Montréal; McGill University; Montreal Heart Institute","funders":"Canada First Research Excellence Fund","keywords":"Colocalization; Genome-wide association study; Linkage disequilibrium; Computational biology; Computer science; Locus (genetics); Genetic association; Statistical power; False positive paradox; Biology; Machine learning; Statistics; Genetics; Neuroscience; Allele; Mathematics; Genotype; Single-nucleotide polymorphism; Gene; Haplotype","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00370092,0.001239737,0.001608544,0.001706976,0.001087377,0.001786872,0.002822383,0.001430385,0.01335738],"category_scores_gemma":[0.01266967,0.001030395,0.002076278,0.001323034,0.00113658,0.001554131,0.002845548,0.001742951,0.002800211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007407527,"about_ca_system_score_gemma":0.002762151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006911287,"about_ca_topic_score_gemma":0.01048979,"domain_scores_codex":[0.9986333,0.0005040686,0.00006814296,0.0004238795,0.0002790786,0.00009152313],"domain_scores_gemma":[0.996473,0.002170398,0.0002564232,0.0005973554,0.000346795,0.0001560439],"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":[0.00141911,0.0002759256,0.02899517,0.001596032,0.002008999,0.001828787,0.000835884,0.260168,0.03204617,0.08666222,0.08469976,0.4994639],"study_design_scores_gemma":[0.0003081559,0.00007744863,0.002860845,0.00005776421,0.0001774363,0.0007440384,0.00007996702,0.8989102,0.007537022,0.07059124,0.01855087,0.0001050576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00747467,0.0002097087,0.9779305,0.0002645047,0.00005314071,0.00007197972,0.001129154,0.01199994,0.0008663194],"genre_scores_gemma":[0.1704095,0.0002486061,0.8130646,0.000485559,0.0001239755,0.000441893,0.004260409,0.006890107,0.004075333],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01335738,"threshold_uncertainty_score":0.04468489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02546075931547463,"score_gpt":0.3226342372464311,"score_spread":0.2971734779309565,"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."}}