{"id":"W2952718909","doi":"10.1002/gepi.22131","title":"Transcriptome‐wide association studies accounting for colocalization using Egger regression","year":2018,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; National Institute of Mental Health; Health Canada; Translation Centre for the Bodies of the European Union; National Cancer Institute; National Institutes of Health; FAS Division of Science, Harvard University; Foundation for the National Institutes of Health; Cancer Research UK; Government of Canada; Harvard University; Genome Canada","keywords":"Colocalization; Association (psychology); Regression; Biology; Statistics; Mathematics; Molecular biology; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002922209,0.0002715988,0.0006287453,0.00009399907,0.0004095938,0.000008517797,0.0002110897,0.000609567,0.00002396215],"category_scores_gemma":[0.02333725,0.0002446208,0.0002040919,0.0001616133,0.0002043828,0.000005892823,0.00009703443,0.00009924829,0.000017923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001594953,"about_ca_system_score_gemma":0.0001076701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003293691,"about_ca_topic_score_gemma":0.00008493313,"domain_scores_codex":[0.9967942,0.0007892139,0.0008830686,0.0006742879,0.0001012437,0.0007579825],"domain_scores_gemma":[0.9964841,0.001622158,0.0007043919,0.0003645679,0.0007327933,0.00009199249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001236277,0.00005077254,0.8251548,0.00004396856,0.0004355481,4.586256e-7,0.0002232846,0.001841223,0.08937965,0.0002635797,0.07872409,0.003759037],"study_design_scores_gemma":[0.004530466,0.003142721,0.4609647,0.0001826914,0.0007933225,0.00004618357,0.00125675,0.03674627,0.02906144,0.02906327,0.4322906,0.001921577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8013478,0.003295426,0.191122,0.002372696,0.001205105,0.0004722392,0.00002427912,0.0000298356,0.0001306093],"genre_scores_gemma":[0.8531225,0.001167616,0.1333761,0.008728578,0.002166128,0.0001560101,0.0001904512,0.00006040448,0.00103224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3641901,"threshold_uncertainty_score":0.9975347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07002123007188674,"score_gpt":0.380047086270924,"score_spread":0.3100258561990372,"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."}}