Predicting DNA methylation level across human tissues.
Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue. The method was evaluated in publicly available data and in two studies using the latest IlluminaBeadChips: a childhood asthma study with methylation measured in both peripheral blood leukocytes (PBL) and lymphoblastoid cell lines; and a study of postoperative atrial fibrillation with methylation in PBL, atrium and artery. We found that our method can greatly improve accuracy of cross-tissue prediction at CpG sites that are variable in the target tissue [R(2) increases from 0.38 (original R(2) between tissues) to 0.89 for PBL-to-artery prediction; from 0.39 to 0.95 for PBL-to-atrium; and from 0.81 to 0.98 for lymphoblastoid cell line-to-PBL based on cross-validation, and confirmed using cross-study prediction]. An extended model with multiple CpGs further improved performance. Our results suggest that large-scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles.
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA, College of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning Province 116026, China, Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA, National Heart and Lung Institute, Imperial College, London SW3 6LY, UK, Department of Clinical Sciences and Community, University of Milan, Milan 20122, Italy, Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Baoshan
B
Ma
Elissa H
EH
Wilker
Saffron A G
SA
Willis-Owen
Hyang-Min
HM
Byun
Kenny C C
KC
Wong
Valeria
V
Motta
Andrea A
AA
Baccarelli
Joel
J
Schwartz
William O C M
WO
Cookson
Kamal
K
Khabbaz
Murray A
MA
Mittleman
Miriam F
MF
Moffatt
Liming
L
Liang
D001158:Arteries / Q000378:metabolism; D020517:Atrial Appendage / Q000378:metabolism; D002461:Cell Line, Transformed; D002648:Child; D018899:CpG Islands; D019175:DNA Methylation; D005260:Female; D006801:Humans; D007962:Leukocytes / Q000378:metabolism; D008297:Male; D015233:Models, Statistical