Jonathan A Heiss1, Lutz P Breitling1,2, Benjamin Lehne3, Jaspal S Kooner4,5,6, John C Chambers3,4,5, Hermann Brenner1,7,8. 1. Division of Clinical Epidemiology & Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. 2. Pneumology & Respiratory Critical Care Medicine, Thorax Clinic, University of Heidelberg, Heidelberg, Germany. 3. Department of Epidemiology & Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK. 4. Ealing Hospital NHS Trust, Middlesex, UK. 5. Imperial College Healthcare NHS Trust, London, UK. 6. National Heart & Lung Institute, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London, UK. 7. Division of Preventive Oncology, National Center for Tumor Diseases (NCT) & German Cancer Research Center (DKFZ), Heidelberg, Germany. 8. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Abstract
AIM: Whole-blood DNA methylation depends on the underlying leukocyte composition and confounding hereby is a major concern in epigenome-wide association studies. Cell counts are often missing or may not be feasible. Computational approaches estimate leukocyte composition from DNA methylation based on reference datasets of purified leukocytes. We explored the possibility to train such a model on whole-blood DNA methylation and cell counts without the need for purification. MATERIALS & METHODS: Using whole-blood DNA methylation and corresponding five-part cell counts from 2445 participants from the London Life Sciences Prospective Population Study, a model was trained on a subset of 175 subjects and evaluated on the remaining. RESULTS: Correlations between cell counts and estimated cell proportions were high (neutrophils 0.85, eosinophils 0.88, basophils 0.02, lymphocytes 0.84, monocytes 0.55) and estimated proportions explained more variance in whole-blood DNA methylation levels than counts. CONCLUSION: Our model provided precise estimates for the common cell types.
AIM: Whole-blood DNA methylation depends on the underlying leukocyte composition and confounding hereby is a major concern in epigenome-wide association studies. Cell counts are often missing or may not be feasible. Computational approaches estimate leukocyte composition from DNA methylation based on reference datasets of purified leukocytes. We explored the possibility to train such a model on whole-blood DNA methylation and cell counts without the need for purification. MATERIALS & METHODS: Using whole-blood DNA methylation and corresponding five-part cell counts from 2445 participants from the London Life Sciences Prospective Population Study, a model was trained on a subset of 175 subjects and evaluated on the remaining. RESULTS: Correlations between cell counts and estimated cell proportions were high (neutrophils 0.85, eosinophils 0.88, basophils 0.02, lymphocytes 0.84, monocytes 0.55) and estimated proportions explained more variance in whole-blood DNA methylation levels than counts. CONCLUSION: Our model provided precise estimates for the common cell types.
Entities:
Keywords:
DNA methylation; Infinium 450K; KAROLA; LOLIPOP; estimation of cell proportions; leukocyte composition; white-blood cell distribution
Authors: Lucas A Salas; Ze Zhang; Devin C Koestler; Rondi A Butler; Helen M Hansen; Annette M Molinaro; John K Wiencke; Karl T Kelsey; Brock C Christensen Journal: Nat Commun Date: 2022-02-09 Impact factor: 14.919