{"id":"W2166075109","doi":"10.3389/fgene.2014.00357","title":"A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of Toronto; Public Health Ontario; Lunenfeld-Tanenbaum Research Institute; University of Guelph","funders":"National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Pleiotropy; Genome-wide association study; Trait; Confounding; Quantitative trait locus; Framingham Heart Study; Biology; Genetic correlation; Genetic association; Random effects model; Genetic architecture; Genetics; Statistics; Genetic variation; Computer science; Phenotype; Gene; Single-nucleotide polymorphism; Mathematics; Disease; Genotype; Meta-analysis","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":[0.01110815,0.001402874,0.001936331,0.00202771,0.0009981784,0.001025017,0.002775617,0.001929482,0.008495712],"category_scores_gemma":[0.01930324,0.000874789,0.002970161,0.001586834,0.0007730541,0.00110953,0.002418661,0.002441505,0.001704039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004834503,"about_ca_system_score_gemma":0.003038189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006778764,"about_ca_topic_score_gemma":0.01118906,"domain_scores_codex":[0.9961128,0.001986787,0.0002228121,0.0008536461,0.0006233103,0.0002006841],"domain_scores_gemma":[0.9866912,0.01027594,0.0005301438,0.0008668324,0.001226093,0.0004097486],"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":[0.00248015,0.001075801,0.07063892,0.0007461859,0.002388394,0.002189775,0.0009629494,0.104399,0.03636349,0.0298679,0.01021956,0.738668],"study_design_scores_gemma":[0.00039638,0.0008199712,0.01202877,0.00007956419,0.000450657,0.0008669749,0.0001783783,0.9442647,0.008441929,0.02518614,0.007118658,0.0001679307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01708405,0.0002487044,0.9805061,0.0002441504,0.00006803967,0.0003946724,0.0003389877,0.000782994,0.0003322961],"genre_scores_gemma":[0.07440216,0.0001403875,0.919989,0.0003573102,0.00006989435,0.001142561,0.00120246,0.0001497292,0.002546553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01110815,"threshold_uncertainty_score":0.05874616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531194472989539,"score_gpt":0.2593157684849393,"score_spread":0.2440038237550439,"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."}}