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. 2010 Jan 26;107 Suppl 1(Suppl 1):1757-64.
doi: 10.1073/pnas.0906183107. Epub 2009 Dec 22.

Evolution in health and medicine Sackler colloquium: Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease

Affiliations

Evolution in health and medicine Sackler colloquium: Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease

Andrew P Feinberg et al. Proc Natl Acad Sci U S A. .

Abstract

Neo-Darwinian evolutionary theory is based on exquisite selection of phenotypes caused by small genetic variations, which is the basis of quantitative trait contribution to phenotype and disease. Epigenetics is the study of nonsequence-based changes, such as DNA methylation, heritable during cell division. Previous attempts to incorporate epigenetics into evolutionary thinking have focused on Lamarckian inheritance, that is, environmentally directed epigenetic changes. Here, we propose a new non-Lamarckian theory for a role of epigenetics in evolution. We suggest that genetic variants that do not change the mean phenotype could change the variability of phenotype; and this could be mediated epigenetically. This inherited stochastic variation model would provide a mechanism to explain an epigenetic role of developmental biology in selectable phenotypic variation, as well as the largely unexplained heritable genetic variation underlying common complex disease. We provide two experimental results as proof of principle. The first result is direct evidence for stochastic epigenetic variation, identifying highly variably DNA-methylated regions in mouse and human liver and mouse brain, associated with development and morphogenesis. The second is a heritable genetic mechanism for variable methylation, namely the loss or gain of CpG dinucleotides over evolutionary time. Finally, we model genetically inherited stochastic variation in evolution, showing that it provides a powerful mechanism for evolutionary adaptation in changing environments that can be mediated epigenetically. These data suggest that genetically inherited propensity to phenotypic variability, even with no change in the mean phenotype, substantially increases fitness while increasing the disease susceptibility of a population with a changing environment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Examples of developmental genes with VMRs in livers from isogenic mice raised in the same environment. Shown are Bmp7 (A), Pou3f2 (B), and Ntrk3 (C), involved in early embryogenic programming and bone induction, neurogenesis and stem cell reprogramming, and body position sensing, respectively. In each paired plot, the top panel shows estimated methylation levels from various biological replicates from three different tissues: brain, liver, and spleen (dashed lines). The thicker solid lines represent the average curves for each tissue. The orange bar denotes the region in which our statistical method detected a VMR. The bottom panel highlights the liver. Only the four liver curves are shown. The different line types and colors represent the four individual mice.
Fig. 2.
Fig. 2.
VMRs are associated with variability in gene expression of nearby genes. The human liver VMRs detected with our statistical algorithm were divided into three types: low variation (lowest 70%), high variation (highest 5%), and medium variation (the remainder). The VMRs within 500 bases from a gene’s transcription start site were associated with that gene. The expression measurements were obtained for the same human livers, and the SD across subjects was used to quantify variability. These boxplots show the distribution of this variability stratified by VMR variability. The first boxplot represents genes not associated with a VMR.
Fig. 3.
Fig. 3.
Examples of developmental genes with VMRs in brains from isogenic mice raised in the same environment. Shown are Bmpr2, the receptor for the morphogenetic BMP protein (A), and Irs1, a key mediator of insulin-driven differentiation (B). Labeling is as in Fig. 1.
Fig. 4.
Fig. 4.
VMRs are often located near T-DMRs. Shown are mouse Ptp4a1, a protein tyrosine phosphatase involved in maintaining differentiated epithelial tissues (A), and human FOXD2, a forkhead transcription factor involved in embryogenesis (B). Labeling is as in Fig. 1. In (A), the VMR and T-DMR coincide, whereas in (B), they are adjacent.
Fig. 5.
Fig. 5.
An underlying genetic basis for species differences in DMRs. A 7,500-bp human region was mapped to the mouse genome. The x-axis shows an index so that mapped bases are on top of one another. (Top) Methylation profiles for each human sample. As in Fig. 1, the dashed lines represent the individuals, and the solid lines represent the tissue averages. (Middle) The same plot for mouse. (Bottom) Ticks representing CpG locations for human and mouse. The orange ticks represent CpGs that were conserved. The curves represent CpG counts in a moving window of size 200 bases. Note that the lack of CpGs in the mouse at the beginning of the regions is associated with a difference in methylation patterns between species. Shown is LHX1, a transcriptional regulator essential for vertebrate head organization and mesoderm organization. Note the DMR in human that is not in mouse on the left of the TSS. The human has gained CpGs at a CpG island shore (orange tick marks). In contrast, both species have a moderate CpG count to the right of the TSS, and both have DMRs in this region.
Fig. 6.
Fig. 6.
Results of simulations demonstrating that increased stochastic variation in the epigenome would increase fitness in a varying environment. (A) Simulations of natural selection. For each simulation, we computed the population average and SD of the phenotype as a function of generation. Two simulations are shown: simulation 1, natural selection in a fixed environment favoring positive Y but including a novel stochastic epigenetic element, such that eight mutations affect average Y and eight mutations affect variance of Y, and simulation 2, similar to simulation 1 but in this case allowing a changing environment across generations that favor at times positive Y and at times negative Y. The top panel shows the average (across all iterations) population average of Y as a function of generation for simulation 1 (green) and simulation 2 (orange). The dashed vertical lines indicate the generations at which the environment was changed in simulation 2. The bottom panel shows the average (across all iterations) population standard deviation of Y. Note that with a changing environment, the average Y fluctuates around a common point, but the SD of Y increases consistently. (B) Emulation of GWAS analysis based on simulation 2 (varying variance of Y). Observed odds ratios are for SNPs that change the mean phenotype.

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