Age-related susceptibility to disease is the most common cause of morbidity, mortality, and diminished quality of life. Although likely related to both genetics and epigenetics, the epigenetic influences on age- related disease have not been defined previously or related to genetic variation. We have recently proposed a novel paradigm for understanding the relationship between variation in DNA sequence, epigenetic marks, and phenotype that considers the role of genes and epigenetics in affecting not only the mean of a phenotype but also its plasticity, or variance. We have begun to apply our approach by identifying highly variably methylated regions (VMRs) in the human genome. We have discovered that VMRs define an epigenetic signature in aged individuals, and that they are associated with important health indicators such as BMI. The purpose of this proposal is to fully explore this new paradigm by identifying VMRs associated with common age-related phenotypes across 3 domains (body composition; energy availability/demand; maintenance of body homeostasis) and determine the relationship between VMRs and underlying genetic variation. In addition to the traditional view that genotypes directly control expected (or mean) phenotype values, we argue that another major effect of genotype is to control stochastic epigenetic variation leading to increased variability of methylation at a particular genomic site and to a consequent increased phenotypic variation. This is a dramatically new concept. We postulate a new kind of SNP, which we call a vSNP, or variation-SNP, that is associated with the degree of methylation variability at VMRs, rather than mean methylation level, and thus also associates with the spread, or variability, of a phenotype in a population rather than the mean value. Such a vSNP would increase the proportion of individuals at both high and low risk of disease, and thus would not be detectible in traditional association analyses. To support our approach, we show preliminary data identifying vSNPs related to VMRs, vSNPs related to increased phenotypic variance, and VMRs related to phenotype.
Our aims are: 1 - To investigate the relationship between DNA sequence, DNA methylation, and quantitative aging phenotypes under both our mean and plasticity models, using 2000 participants in the Baltimore Longitudinal Study of Aging (BLSA), and genome-wide SNP and methylation approaches. 2 - To perform focused DNA sequencing and capture bisulfite sequencing to identify the specific sequence and epigenetic variants responsible for findings in Aim 1. This work will help elucidate how genetic variation relates to methylation variation and how both impact age-associated phenotypes that increase disease susceptibility. DNA methylation could be a new target for risk assessment and intervention that can reduce the burden of disease and disability and slow down the deleterious effects of aging.

Public Health Relevance

We plan to study the relationship between genes, epigenetic modifications of DNA, and age- related phenotypes that increase susceptibility to disease. We will focus on two hypotheses: (1) that genes control the mean values of DNA methylation and the mean values of phenotypes and (2) that genes control the variability, or spread, of DNA methylation and phenotypes in a population. If our hypothesis is correct that specific changes in DNA and DNA methylation are important for the age-associated increase in disease susceptibility, DNA methlyation could be a new target for risk assessment and intervention that can reduce the burden of disease and disability associated with aging.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Guo, Max
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Johns Hopkins University
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Diep, Dinh; Plongthongkum, Nongluk; Zhang, Kun (2018) Large-Scale Targeted DNA Methylation Analysis Using Bisulfite Padlock Probes. Methods Mol Biol 1708:365-382
Mendelson, Michael M; Marioni, Riccardo E; Joehanes, Roby et al. (2017) Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach. PLoS Med 14:e1002215
Patel, Chirag J; Kerr, Jacqueline; Thomas, Duncan C et al. (2017) Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 26:1370-1380
Chen, Brian H; Marioni, Riccardo E; Colicino, Elena et al. (2016) DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging (Albany NY) 8:1844-1865
Ligthart, Symen; Marzi, Carola; Aslibekyan, Stella et al. (2016) DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol 17:255
Marioni, Riccardo E; Shah, Sonia; McRae, Allan F et al. (2015) DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 16:25
Vandiver, Amy R; Idrizi, Adrian; Rizzardi, Lindsay et al. (2015) DNA methylation is stable during replication and cell cycle arrest. Sci Rep 5:17911
Vandiver, Amy R; Irizarry, Rafael A; Hansen, Kasper D et al. (2015) Age and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skin. Genome Biol 16:80
Ladd-Acosta, Christine (2015) Epigenetic Signatures as Biomarkers of Exposure. Curr Environ Health Rep 2:117-25
Liu, Yun; Li, Xin; Aryee, Martin J et al. (2014) GeMes, clusters of DNA methylation under genetic control, can inform genetic and epigenetic analysis of disease. Am J Hum Genet 94:485-95

Showing the most recent 10 out of 12 publications