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. Author manuscript; available in PMC: 2019 May 31.
Published in final edited form as: Cell. 2018 Apr 26;173(6):1385–1397.e14. doi: 10.1016/j.cell.2018.03.079

Figure 6.

Figure 6

Non-Heritable Influences Explain Most Variations in Chromatin Modification Profiles.

(A) Overview of heritability analyses. 9 pairs of monozygotic (MZ) twins and 10 pairs of dizygotic (DZ) twins are subject to EpiTOF analysis utilizing four EpiTOF panels covering major immune cells and T cell subsets.

(B) Variance in chromatin modification profiles is largely driven by unique environmental factors. Heatmap representation of the proportions of variance explained by additive genetics (left), common environment (middle) or unique environment (right) for the indicated chromatin mark and immune cell subset pairs. Chromatin marks are ranked from top to bottom based on the average influences from additive genetics. Immune cells are ranked from left to right by the averages of additive genetics influences across all chromatin marks. The average influences of each component on all 800 data points are shown.

(C and D) Aging is associated with divergent chromatin modification profiles between twins. PCA of younger (cyan) and older (salmon) twin subjects. Each dot represents a single twin subject and the twins are connected. Principal component, variance of 800 data points. The percentage of variance explained by each principal component is shown (C). Euclidean distances of 800 chromatin mark and cell type pairs are computed for each pair of twins (left) or randomly selected genetically unrelated subjects (right) from separate age groups (cyan, younger subjects; salmon, older subjects). p values for the statistical significance of increased Euclidean distance in older pairs are shown (D).

(E) Concordance of chromatin modification profiles in younger MZ twins diminishes with age. Euclidean distances calculated from the 800 data points between MZ (left), DZ (middle) twins and randomly paired genetically unrelated individuals (right). Top, younger subjects; bottom, older subjects. p values for the statistical significance of distinct Euclidean distance are shown.

(F) Non-heritable influences drive the increased variability in chromatin modifications with age. Spearman’s rank correlations of 40 chromatin marks across 20 immune cells types are computed for younger and older MZ twins. Each dot represents a chromatin mark. x-axis, correlation between older twin pairs; y-axis, correlation between young twin pairs. Dashed line, equal correlation in younger and older MZ twins. Arrows indicate the directions of higher concordance in younger pairs (upper left) or in older pairs (lower right).