{"id":"W2571173152","doi":"10.1371/journal.pgen.1006493","title":"Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data","year":2016,"lang":"en","type":"article","venue":"PLoS Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institute on Aging; Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health; U.S. Department of Health and Human Services; National Center for Advancing Translational Sciences; Wellcome Trust; Ontario Institute for Cancer Research; Ministerio de Economía y Competitividad","keywords":"Genome-wide association study; Single-nucleotide polymorphism; Genetic association; Winner's curse; Heritability; Linkage disequilibrium; Biology; Computational biology; Missing heritability problem; Computer science; Genetics; Statistics; Mathematics; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01703385,0.001107198,0.002301439,0.001142058,0.00093742,0.001824773,0.002784124,0.001808872,0.001109062],"category_scores_gemma":[0.05342271,0.000639066,0.001779519,0.001409358,0.001394424,0.001676465,0.001966135,0.002357624,0.0003449415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008402643,"about_ca_system_score_gemma":0.003322085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01746752,"about_ca_topic_score_gemma":0.013278,"domain_scores_codex":[0.9941209,0.002947098,0.0004584295,0.001168935,0.0008699695,0.0004346802],"domain_scores_gemma":[0.9646381,0.02664794,0.001984869,0.003990993,0.002170369,0.0005676844],"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":[0.001189162,0.0001693681,0.03750238,0.0004196236,0.001358405,0.0008290809,0.0005090546,0.6855717,0.008939979,0.0212726,0.00743397,0.2348047],"study_design_scores_gemma":[0.00008654193,0.00006047763,0.003029777,0.00002155309,0.00007277747,0.00007065954,0.00002319411,0.9841865,0.002103824,0.009481071,0.0008263367,0.00003720571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09548165,0.0009151702,0.8984517,0.000929735,0.0002348032,0.0001107355,0.000213546,0.002413176,0.001249502],"genre_scores_gemma":[0.6695913,0.0003457285,0.3256517,0.0006857123,0.0001891063,0.0002002833,0.0005952154,0.0006285313,0.00211241],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01746752,"threshold_uncertainty_score":0.09008467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03234417847464572,"score_gpt":0.2529286959855799,"score_spread":0.2205845175109342,"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."}}