{"id":"W4319777513","doi":"10.1002/gepi.22516","title":"A fast linkage method for population GWAS cohorts with related individuals","year":2023,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"National Human Genome Research Institute; National Institute on Drug Abuse; National Institute on Aging; Norwegian Institute of Public Health; Medical School, University of Michigan; School of Public Health, University of Michigan; Helse Midt-Norge; Fakultet for medisin og helsevitenskap, Norges Teknisk-Naturvitenskapelige Universitet; Consiglio Nazionale delle Ricerche; Faculty of Medicine and Health, University of Sydney; Norges Teknisk-Naturvitenskapelige Universitet; Stiftelsen Kristian Gerhard Jebsen","keywords":"Genetics; Genotyping; Linkage (software); Population; Biology; Genetic linkage; Genome-wide association study; Genetic association; Computational biology; Single-nucleotide polymorphism; Gene; Medicine; 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":[0.003417484,0.0002970202,0.0007156838,0.000160899,0.0002142115,0.000007103875,0.0002575211,0.0006612547,0.00003137437],"category_scores_gemma":[0.002789567,0.0002570147,0.0001932204,0.0003322112,0.0001054376,0.000002887396,0.0001362433,0.0001621886,0.0000609987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002407603,"about_ca_system_score_gemma":0.00008386232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001147208,"about_ca_topic_score_gemma":0.00008629458,"domain_scores_codex":[0.9962568,0.001070064,0.0008756463,0.0008638185,0.00009501092,0.0008387185],"domain_scores_gemma":[0.9976999,0.0009540743,0.0004604689,0.0005853966,0.0001388603,0.0001612611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008132362,0.00004058277,0.9090822,0.00003728365,0.0003770609,0.000003366539,0.0000781852,0.03875323,0.006227706,0.000776866,0.01809928,0.02644295],"study_design_scores_gemma":[0.001075019,0.001147978,0.9599378,0.00001514876,0.0001141493,0.00006565698,0.00006066281,0.009325955,0.0002406336,0.01227026,0.01535081,0.0003959373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7978359,0.0006477825,0.1976172,0.002126434,0.0003614544,0.0008878349,0.0001074434,0.0001020309,0.0003139174],"genre_scores_gemma":[0.5305324,0.0005605359,0.4581174,0.002095315,0.0004944612,0.0006563382,0.003627178,0.00009991362,0.003816465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2673035,"threshold_uncertainty_score":0.9999882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0265753600389996,"score_gpt":0.3377635907329521,"score_spread":0.3111882306939526,"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."}}