{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006096257,0.0001267753,0.0001402544,0.0000521808,0.0001214976,0.00009188354,0.00008912473,0.0001660205,0.000007869718],"category_scores_gemma":[0.0004166374,0.0001132193,0.00004997124,0.00008470081,0.0000304226,0.000008149839,0.0000692595,0.00004488982,0.000006970638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001567094,"about_ca_system_score_gemma":0.00007257207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001032881,"about_ca_topic_score_gemma":0.00001513723,"domain_scores_codex":[0.9990711,0.00004866308,0.0003619677,0.0002137689,0.00007124383,0.000233232],"domain_scores_gemma":[0.9994455,0.0001100985,0.00009982514,0.0001733169,0.0001045347,0.00006671806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002643632,0.0003501288,0.04487984,0.002638762,0.0009113782,0.000006813352,0.005703284,0.3706778,0.2337214,0.002206822,0.03249315,0.3061462],"study_design_scores_gemma":[0.000247325,0.0001985925,0.004825808,0.00001728894,0.00003827969,0.00001338627,0.0002459507,0.9785979,0.003113855,0.0001135551,0.01242257,0.0001655105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.365356,0.0004737248,0.6333974,0.00007694204,0.0001546562,0.0003413946,0.00009166998,0.00002747969,0.00008067895],"genre_scores_gemma":[0.812426,0.00008181041,0.1863792,0.000303597,0.0001852516,0.00007560672,0.0004250959,0.00002147909,0.0001020197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6079201,"threshold_uncertainty_score":0.461695,"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."}}