Non-linear patterns in age-related DNA methylation may reflect CD4⁺ T cell differentiation

Figures & data

Table 1. Reference information regarding each of the 11 data sets analyzed.

Gene series	Study	Abbrev.	Tissue type	Publication
GSE72680	Grady Trauma Project	GTP	Peripheral blood	Zannas, Arloth, Carrillo-Roa, Iurato, Röh, Ressler, Nemeroff, Smith, Bradley, Heim, Menke, Lange, Brückl, Ising, Wray, Erhardt, Binder and Mehta^53
GSE60132	TOPS Family Study of Epigenetics	TOPS	Peripheral blood	Ali, Cerjak, Kent, James, Blangero, Carless and Zhang^54
GSE56581	MESA Epigenomics and Transcriptomics Study (human T cells)	MESA-T	Purified CD4^+ T cells	Reynolds, Taylor, Ding, Lohman, Johnson, Siscovick, Burke, Post, Shea, Jacobs, Stunnenberg, Kritchevsky, Hoeschele, McCall, Herrington, Tracy and Liu^56
GSE56046	MESA Epigenomics and Transcriptomics Study (human monocytes)	MESA-M	Purified monocytes	Reynolds, Taylor, Ding, Lohman, Johnson, Siscovick, Burke, Post, Shea, Jacobs, Stunnenberg, Kritchevsky, Hoeschele, McCall, Herrington, Tracy and Liu^56
GSE59065	Estonian Genome Center Investigation of Age-related epigenetics and immune system function in PBL, CD4+ and CD8+ T cells	EGC-PBL	Peripheral blood leukocytes (PBL), CD4+ and CD8+ T cells	Tserel, Kolde, Limbach, Tretyakov, Kasela, Kisand, Saare, Vilo, Metspalu, Milani and Peterson^40
		EGC-CD4
		EGC-CD8
phs000741.v1.p1	Genetics Of Lipid Lowering Drugs and Diet Network	GOLDN	Purified CD4^+ T cells	Irvin, Zhi, Joehanes, Mendelson, Aslibekyan, Claas, Thibeault, Patel, Day, Jones, Liang, Chen, Yao, Tiwari, Ordovas, Levy, Absher and Arnett^63
GSE74193	Schizophrenia-related DNAm and gene expression	CTX	Dorsolateral prefrontal cortex (dlPFC)	Jaffe, Gao, Deep-Soboslay, Tao, Hyde, Weinberger and Kleinman^57
GSE41826	National Institute of Child Health and Human Development (NICHD) Brain Bank of Developmental Disorders – Cell Heterogeneity	NICHD-G	Prefrontal cortex (PFC) Glial cells	Guintivano, Aryee and Kaminsky^58
		NICHD-N	Prefrontal cortex neurons

Table 2. Statistics corresponding to the age² term of the regression model fitted separately to each of the 11 data sets for the CpG site, cg07955995 (Equation 1(Equation 1) ${\beta }_i={\mathit{\alpha }}_0+{\mathit{\gamma }}_{\mathit{a}\mathit{g}\mathit{e}}\left(AG{E}_i\right)+{\mathbf{\gamma }}_{\mathit{a}\mathit{g}{\mathit{e}}^2}\left(AG{E}_i^2\right)+{\displaystyle \mathbf{\sum }}_{j = 1}^k{\mathit{\gamma }}_{\mathit{j}}co{v}_{ij}+{\mu }_i+{ϵ}_i$(Equation 1) ). The regression model includes covariates. Columns 1–3 include abbreviations of the data set, cell/tissue type, and the sample size. Columns 4–6 include the estimated rate of change in DNAm proportion per year at 50, 60, and 70 years of age. Columns 7–10 include the coefficient, standard error, T-statistic, and P-value on the age² term.

Dataset	Cell/tissue type	n	${\gamma }_{age = 50}$	${\gamma }_{age = 60}$	${\gamma }_{age = 70}$	${\gamma }_{ag{e}^2}$	$S{E}_{ag{e}^2}$	$T_{ag{e}^2}$	$P_{ag{e}^2}$
GTP	Whole blood	336	1.6E-03	2.4E-03	3.1E-03	3.7E-05	4.1E-06	9.1	1.6E-17
TOPS	Whole blood	192	2.0E-03	2.7E-03	3.4E-03	3.4E-05	3.9E-06	8.6	4.3E-15
EGC-PBL	Whole blood	97	1.3E-03	1.1E-03	8.1E-04	−1.3E-05	2.3E-05	−0.57	0.57
MESA-M	Monocyte	1202	1.1E-03	1.6E-03	2.0E-03	2.2E-05	1.0E-05	2.2	0.030
EGC-CD8	CD8^+ T cell	100	1.2E-03	1.8E-03	2.4E-03	2.8E-05	2.6E-05	1.1	0.29
MESA-T	CD4^+ T cell	214	7.7E-04	1.2E-03	1.6E-03	2.0E-05	1.6E-05	1.2	0.22
EGC-CD4	CD4^+ T cell	99	9.5E-04	1.0E-03	1.1E-03	3.6E-06	1.7E-05	0.21	0.83
GOLDN	CD4^+ T cell	991	4.3E-03	4.9E-03	5.5E-03	4.0E-06	7.7E-07	5.2	2.0E-07
CTX	dlPFC	346	1.7E-03	1.8E-03	1.9E-03	5.3E-06	2.6E-06	2.1	0.038
NICHD-G	Glial cell (FC)	50	1.3E-03	1.3E-03	1.2E-03	−2.9E-06	8.7E-06	−0.33	0.74
NICHD-N	Neuron (FC)	50	2.0E-03	2.3E-03	2.5E-03	1.3E-05	1.0E-05	1.2	0.23

The regression model includes covariates. Columns 1–3 include abbreviations of the data set, cell/tissue type, and the sample size. Columns 4–6 include the estimated rate of change in DNAm proportion per year at 50, 60, and 70 years of age. Columns 7–10 include the coefficient, standard error, T-statistic, and P-value on the age² term.

Table 3. Statistics corresponding to the age² term of the regression model fitted separately to each of the 11 data sets for the CpG site, cg22285878 (Equation 1(Equation 1) ${\beta }_i={\mathit{\alpha }}_0+{\mathit{\gamma }}_{\mathit{a}\mathit{g}\mathit{e}}\left(AG{E}_i\right)+{\mathbf{\gamma }}_{\mathit{a}\mathit{g}{\mathit{e}}^2}\left(AG{E}_i^2\right)+{\displaystyle \mathbf{\sum }}_{j = 1}^k{\mathit{\gamma }}_{\mathit{j}}co{v}_{ij}+{\mu }_i+{ϵ}_i$(Equation 1) ).

Dataset	Cell/tissue type	n	${\gamma }_{age = 50}$	${\gamma }_{age = 60}$	${\gamma }_{age = 70}$	${\gamma }_{ag{e}^2}$	$S{E}_{ag{e}^2}$	$T_{ag{e}^2}$	$P_{ag{e}^2}$
GTP	Whole blood	336	8.8E-04	1.3E-03	1.7E-03	2.1E-05	3.3E-06	6.4	5.3E-10
TOPS	Whole blood	192	9.8E-04	1.3E-03	1.6E-03	1.5E-05	2.7E-06	5.7	5.0E-08
EGC-PBL	Whole blood	97	6.0E-04	7.7E-04	9.5E-04	8.6E-06	1.4E-05	0.62	0.54
MESA-M	Monocytes	1202	5.4E-04	8.7E-04	1.2E-03	1.6E-05	6.3E-06	2.6	0.010
EGC-CD8	CD8^+ T cells	100	6.3E-04	1.4E-03	2.1E-03	3.7E-05	1.7E-05	2.2	0.033
MESA-T	CD4^+ T cells	214	6.2E-04	7.7E-04	9.2E-04	7.5E-06	1.2E-05	0.64	0.52
EGC-CD4	CD4^+ T cells	99	7.8E-04	8.2E-04	8.5E-04	1.7E-06	2.1E-05	0.082	0.93
GOLDN	CD4^+ T cells	991	1.2E-03	1.3E-03	1.3E-03	3.9E-06	8.5E-07	4.6	4.2E-06
CTX	dlPFC	346	1.3E-03	1.5E-03	1.6E-03	6.9E-06	2.2E-06	3.1	0.0018
NICHD-G	Glial cell (FC)	50	1.5E-03	1.5E-03	1.4E-03	−1.9E-06	8.3E-06	−0.22	0.82
NICHD-N	Neuron (FC)	50	1.1E-03	1.2E-03	1.4E-03	7.9E-06	8.3E-06	0.95	0.35

The regression model includes covariates. Columns 1–3 include abbreviations of the data set, cell/tissue type, and the sample size. Columns 4–6 include the estimated rate of change in DNAm proportion per year at 50, 60, and 70 years of age. Columns 7–10 include the coefficient, standard error, T-statistic, and P-value on the age² term.

Figure 1. Scatterplots of DNAm (y-axis) vs. age (x-axis) with fitted quadratic regression lines for each of the 11 data sets, holding covariates constant at their mean values.

Figure 2. Plot of the variance of methylation across subjects at cg07955995 (left) and cg22285878 (right) for the older age group (73 years or older) against the younger age group for each data set. MESA-M, MESA-T, NICHD-G, and NICHD-N appear with different symbols because the age groups were calculated differently than the other data sets. For these 4 data sets, the median ages (in years) were used (58 for MESA-M, 60 for MESA-T, and 23 for both NICHD-G and NICHD-N) to create young groups (≤median) and old groups (>median) because MESA-M and MESA-T had no individuals less than 34 years of age and NICHD-G and NICHD-N only had 2 individuals older than 73 years.

Table 4. Variances of the young age group, variances of the old age group, and P-values corresponding to the F-statistic calculated as the ratio of variances of the young and old age groups for each of the 11 data sets.

	$cg07955995$			$cg22285878$
Dataset	$Va{r}_{young}$	$Va{r}_{old}$	$P_{Var}$	$Va{r}_{young}$	$Va{r}_{old}$	$P_{Var}$
GTP	8.3E-05	2.7E-03	1.4E-11	6.6E-05	2.0E-03	5.6E-11
TOPS	6.5E-05	3.0E-03	2.8E-26	6.6E-05	6.0E-04	2.0E-08
EGC-PBL	1.8E-04	1.5E-03	3.8E-05	1.5E-04	4.3E-04	9.7E-03
MESA-M	4.2E-04	1.6E-03	6.4E-56	1.8E-04	7.3E-04	1.9E-62
EGC-CD8	3.9E-04	1.9E-03	1.0E-11	3.6E-04	8.0E-04	1.4E-04
MESA-T	1.9E-04	4.2E-04	2.1E-03	1.4E-04	2.1E-04	1.0E-09
EGC-CD4	2.9E-04	6.6E-04	4.4E-08	1.9E-04	1.2E-03	3.0E-03
GOLDN	7.5E-05	3.8E-04	2.1E-03	7.8E-05	2.7E-04	1.1E-03
CTX	4.3E-04	1.2E-03	6.7E-19	4.5E-04	1.4E-03	9.8E-12
NICHD-G	3.4E-04	9.2E-05	0.76	3.0E-04	1.9E-04	0.54
NICHD-N	4.4E-04	1.2E-07	0.9997	4.3E-04	1.9E-04	0.65

Columns 2–4 correspond to cg07955995 and columns 5–7 correspond to cg22285878.

Zannas AS, Arloth J, Carrillo-Roa T, Iurato S, Röh S, Ressler KJ, Nemeroff CB, Smith AK, Bradley B, Heim C, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: Relevance of glucocorticoid signaling. Genome Biol 2015; 16:1-12; PMID:25583448; https://doi.org/10.1186/s13059-015-0828-5

 PubMed Web of Science ®Google Scholar

Ali O, Cerjak D, Kent JW, Jr., James R, Blangero J, Carless MA, Zhang Y. An epigenetic map of age-associated autosomal loci in northern European families at high risk for the metabolic syndrome. Clin Epigenetics 2015; 7:12; PMID:25806089; https://doi.org/10.1186/s13148-015-0048-6

 PubMed Web of Science ®Google Scholar

Reynolds LM, Taylor JR, Ding J, Lohman K, Johnson C, Siscovick D, Burke G, Post W, Shea S, Jacobs DR, Jr., et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Nat Commun 2014; 5:5366; PMID:25404168; https://doi.org/10.1038/ncomms6366

 PubMed Web of Science ®Google Scholar

Tserel L, Kolde R, Limbach M, Tretyakov K, Kasela S, Kisand K, Saare M, Vilo J, Metspalu A, Milani L, et al. Age-related profiling of DNA methylation in CD8+ T cells reveals changes in immune response and transcriptional regulator genes. Sci Rep 2015; 5:13107; PMID:26286994; https://doi.org/10.1038/srep13107

 PubMed Web of Science ®Google Scholar

Irvin MR, Zhi D, Joehanes R, Mendelson M, Aslibekyan S, Claas SA, Thibeault KS, Patel N, Day K, Jones LW, et al. Epigenome-wide association study of fasting blood lipids in the genetics of lipid-lowering drugs and diet network study. Circulation 2014; 130:565-72; PMID:24920721; https://doi.org/10.1161/CIRCULATIONAHA.114.009158

 PubMed Web of Science ®Google Scholar

Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, Kleinman JE. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci 2016; 19:40-7; PMID:26619358; https://doi.org/10.1038/nn.4181

 PubMed Web of Science ®Google Scholar

Guintivano J, Aryee MJ, Kaminsky ZA. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics 2013; 8:290-302; PMID:23426267; https://doi.org/10.4161/epi.23924

 PubMed Web of Science ®Google Scholar