{"id":"W3006817833","doi":"10.48550/arxiv.2002.08505","title":"A Bayes Factor Approach with Informative Prior for Rare Genetic Variant Analysis from Next Generation Sequencing Data","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto","funders":"","keywords":"False discovery rate; Bayes factor; Bayes' theorem; Prior probability; Test statistic; Sample size determination; Bayesian probability; Null distribution; Genome-wide association study; Statistics; Beta-binomial distribution; Context (archaeology); Computational biology; Computer science; Statistical hypothesis testing; Negative binomial distribution; Biology; Mathematics; Genetics; Single-nucleotide polymorphism; Gene; Genotype","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":[0.02686385,0.001485531,0.002221867,0.004255407,0.001115129,0.002358118,0.002970415,0.002545847,0.002901536],"category_scores_gemma":[0.08472365,0.001159195,0.00234022,0.003219224,0.003138172,0.002848532,0.001955044,0.004105219,0.0005653279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001394924,"about_ca_system_score_gemma":0.00264854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006401179,"about_ca_topic_score_gemma":0.004770083,"domain_scores_codex":[0.9873168,0.008913247,0.0005784344,0.001554037,0.001366678,0.0002707492],"domain_scores_gemma":[0.9530166,0.0418903,0.00151041,0.001855716,0.00132956,0.0003973762],"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":[0.0004728969,0.0001504548,0.01028972,0.0006905122,0.001022358,0.0010288,0.0006517214,0.3866476,0.00306977,0.3654269,0.004457676,0.2260917],"study_design_scores_gemma":[0.00009697704,0.00008994693,0.001197248,0.0001233134,0.0001861236,0.0004006337,0.00005133391,0.6330134,0.001086769,0.3595285,0.004141544,0.00008424532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0013564,0.0002941963,0.9978077,0.0001211186,0.00003115438,0.00003844459,0.00006287076,0.0001176513,0.0001703773],"genre_scores_gemma":[0.1222509,0.001224839,0.8732784,0.0003899146,0.0003639083,0.0006003891,0.0005822141,0.0001703481,0.001138918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02686385,"threshold_uncertainty_score":0.1420714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1966434321179264,"score_gpt":0.2249872791276774,"score_spread":0.02834384700975098,"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."}}