{"id":"W3038990418","doi":"10.3389/fgene.2020.00578","title":"Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute on Drug Abuse; National Institute on Aging; Lundbeckfonden; Department of Health and Social Care; National Institute of Mental Health; Medical Research Council; National Institute for Health and Care Research","keywords":"Polygenic risk score; Open source; Computer science; Profit (economics); Econometrics; Computational biology; Biology; Statistics; Genetics; Single-nucleotide polymorphism; Economics; Gene; Mathematics; Software; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004017072,0.0002891182,0.0004571029,0.00008008494,0.0001556538,0.000105966,0.001077626,0.000241433,0.00000410236],"category_scores_gemma":[0.001123642,0.0003127245,0.00004268633,0.0002415047,0.00006296313,0.00001053913,0.00172237,0.0001321867,0.000003985584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003376345,"about_ca_system_score_gemma":0.000135035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004272426,"about_ca_topic_score_gemma":0.0001844774,"domain_scores_codex":[0.9975385,0.0001843385,0.0005248195,0.001137188,0.0001022158,0.0005129384],"domain_scores_gemma":[0.9984596,0.00006936971,0.0001435587,0.0009009613,0.00009740972,0.0003290434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001598043,0.00002489203,0.9125285,0.00001444611,0.0001447458,0.000001792306,0.0003122633,0.01126815,0.04286119,3.66582e-7,0.009608129,0.02307575],"study_design_scores_gemma":[0.001497955,0.001062469,0.4331709,0.00005062243,0.0002382469,0.000004874976,0.0003000797,0.548489,0.005041488,0.000174878,0.009097744,0.0008717504],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.671977,0.001262427,0.3250126,0.0001372276,0.000255894,0.0007439281,0.0005861476,0.00001008843,0.00001465598],"genre_scores_gemma":[0.5368604,0.0001362916,0.461423,0.000990351,0.0002349126,0.00003814859,0.000243042,0.00005028648,0.0000235547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5372208,"threshold_uncertainty_score":0.9999325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04404591021138038,"score_gpt":0.3120847336595815,"score_spread":0.2680388234482011,"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."}}