{"id":"W3175473008","doi":"10.1101/2021.06.24.21259449","title":"Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"Economic and Social Research Council; Canadian Institutes of Health Research; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Biotechnology and Biological Sciences Research Council; Joint Programming Initiative A healthy diet for a healthy life; Erasmus Universiteit Rotterdam; ZonMw; European Commission","keywords":"Epigenome; DNA methylation; Epigenetics; CpG site; Methylation; Birth weight; Context (archaeology); Biology; Cord blood; Gestational age; Epigenomics; Genetics; Population; Medicine; Pregnancy; Gene","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":[0.02614862,0.001464161,0.001485572,0.00293833,0.0008765879,0.001977687,0.003446161,0.002216373,0.003998669],"category_scores_gemma":[0.06004704,0.0008538471,0.004067813,0.002150379,0.001442298,0.0008693347,0.002398904,0.003178668,0.0008395683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009127776,"about_ca_system_score_gemma":0.0008157725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008537119,"about_ca_topic_score_gemma":0.005532281,"domain_scores_codex":[0.985216,0.01180972,0.0003373571,0.002032834,0.0003986089,0.0002055627],"domain_scores_gemma":[0.9453906,0.04686937,0.00139739,0.005029365,0.001061214,0.0002521113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001805518,0.0003144861,0.3019679,0.0006918611,0.009876518,0.00163777,0.001544912,0.4299403,0.009197349,0.02737065,0.007545403,0.2081074],"study_design_scores_gemma":[0.000131296,0.0002423479,0.04041237,0.0001224506,0.0007692338,0.0005067716,0.0001706658,0.9165416,0.001999973,0.03311584,0.005910973,0.00007643369],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1226868,0.0008426537,0.8687888,0.0009221491,0.00007258488,0.000182869,0.00344367,0.002238543,0.0008218716],"genre_scores_gemma":[0.6238208,0.0003205996,0.3637337,0.0006873799,0.0001259833,0.0009676398,0.007489512,0.0006604195,0.002193991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02614862,"threshold_uncertainty_score":0.1382887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954553029988993,"score_gpt":0.2686118608621079,"score_spread":0.2490663305622179,"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."}}