Research Paper Volume 11, Issue 8 pp 2253—2280
The widespread increase in inter-individual variability of gene expression in the human brain with age
- 1 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
- 2 Current Address - Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
received: January 23, 2019 ; accepted: April 5, 2019 ; published: April 19, 2019 ;https://doi.org/10.18632/aging.101912
How to Cite
Copyright: Kedlian et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Aging is broadly defined as a time-dependent progressive decline in the functional and physiological integrity of organisms. Previous studies and evolutionary theories of aging suggest that aging is not a programmed process but reflects dynamic stochastic events. In this study, we test whether transcriptional noise shows an increase with age, which would be expected from stochastic theories. Using human brain transcriptome dataset, we analyzed the heterogeneity in the transcriptome for individual genes and functional pathways, employing different analysis methods and pre-processing steps. We show that unlike expression level changes, changes in heterogeneity are highly dependent on the methodology and the underlying assumptions. Although the particular set of genes that can be characterized as differentially variable is highly dependent on the methods, we observe a consistent increase in heterogeneity at every level, independent of the method. In particular, we demonstrate a weak but reproducible transcriptome‐wide shift towards an increase in heterogeneity, with twice as many genes significantly increasing as opposed to decreasing their heterogeneity. Furthermore, this pattern of increasing heterogeneity is not specific but is associated with a wide range of pathways.
BP GO: Biological Process Gene Ontology; DE: differentially expressed genes; DV: differentially variable genes; Δvar: the measure of change in the expression variability with age; GO: Gene Ontology; GSEA: Gene Set Enrichment Analysis; IQR: interquartile range; KEGG: Kyoto Encyclopedia of Genes and Genomes; PMI: post-mortem interval; QN: quantile normalization; RIN: RNA integrity number; Rho: Spearman correlation estimate; SVA: Surrogate Variable Analysis.