X-chromosome inactivation is an epigenetic process that regulates gene dosage in females. Occurring as a random coin-flip early in development, the status of inactivation is then stably inherited down cell lineages via DNA methylation. The degree of “skewing” toward one chromosome over the other has been researched intensively, and importantly it has been linked to disease, where female carriers of X-linked disorders can have differential disease penetrance as a function of skewing.
But what about the autosomes? Is the allelic expression of autosomal genes epigenetically regulated? And if so, could it have an impact on disease risk?
In this work, we uncover one major axis of random variation with a large and permanent regulatory influence on the allelic expression on autosomes: developmental stochasticity. By assaying the transcriptome of wild monozygotic quadruplets of the nine-banded armadillo, we find that persistent changes occur early in development, and these give rise to clear transcriptional signatures which uniquely characterize individuals relative to siblings.
Our central experimental strategy is to measure gene expression over time and look for signatures permanently distinguishing siblings from one another. As an aggregate readout of gene regulation between the genetically identical individuals, gene expression serves as a likely intermediate to capture purely non-genetic regulatory variability with an influence on phenotype.
We find that purely stochastic variation in development has a large and permanent impact on gene expression. Using allelic imbalances, we timed the contribution of developmental stochasticity within our data to the assignment of tissue-specific epigenetic marks. Using expression profiles, including co-expression and human twin data, we determined conserved functions affected by developmental stochasticity. Comparing these results to human twins, we find the transcriptional signatures which define individuals exhibit conserved co-expression, suggesting a substantial fraction of phenotypic and disease discordance within mammals arises from regulatory stochasticity occurring early in development. We examine regulatory basis of the largest effect size changes in single cell data.
Genetic variation, epigenetic regulation and major environmental stimuli are key contributors to phenotypic variation, but the influence of minor perturbations or “noise” has previously been difficult to assess in mammals. By using armadillos as a model, we control for genetic and environmental factors and reveal early developmental stochasticity as a major source of variability between individuals.
Associate Professor of Computational Genomics, Cold Spring Harbor LaboratoryView Slides
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