Nature CommunicationsGeneticsPeer-reviewed journal article
Tissue-specific impacts of aging and genetics on gene expression patterns in humans
Ryo YamamotoiD, Ryan ChungiD, Juan Manuel VázqueziD, Huanjie ShengiD, Philippa L. SteinbergiD, Nilah M. IoannidisiD, Peter H. SudmantiD
Abstract
Age is the primary risk factor for many common human diseases including heart disease, Alzheimers dementias, cancers, and diabetes. Determining how and why tissues age differently is key to understanding the onset and progression of such pathologies. Here, we set out to quantify the relative contributions of genetics and aging to gene expression patterns from data collected across 27 tissues from 948 humans. We show that age impacts the predictive power of expression quantitative trait loci across several tissues. Jointly modelling the contributions of age and genetics to transcript level variation we find that the heritability (h2) of gene expression is largely consistent among tissues. In contrast, the average contribution of aging to gene expression variance varied by more than 20-fold among tissues with [Formula] in 5 tissues. We find that the coordinated decline of mitochondrial and translation factors is a widespread signature of aging across tissues. Finally, we show that while in general the force of purifying selection is stronger on genes expressed early in life compared to late in life as predicted by Medawars hypothesis, a handful of highly proliferative tissues exhibit the opposite pattern. These non-Medawarian tissues exhibit high rates of cancer and age-of-expression associated somatic mutations in cancer. In contrast, gene expression variation that is under genetic control is strongly enriched for genes under relaxed constraint. Together we present a novel framework for predicting gene expression phenotypes from genetics and age and provide insights into the tissue-specific relative contributions of genes and the environment to phenotypes of aging.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 79
- FWCI
- 9.95
- Field percentile
- top 1%
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