Determinants of expression variability

Abstract

The amount of tissue-specific expression variability (EV) across individuals is an essential characteristic of a gene and believed to have evolved, in part, under functional constraints. However, the determinants and functional implications of EV are only beginning to be investigated. Our analyses based on multiple expression profiles in 41 primary human tissues show that a gene’s EV is significantly correlated with a number of features pertaining to the genomic, epigenomic, regulatory, polymorphic, functional, structural and network characteristics of the gene. We found that (i) EV of a gene is encoded, in part, by its genomic context and is further influenced by the epigenome; (ii) strong promoters induce less variable expression; (iii) less variable gene loci evolve under purifying selection against copy number polymorphisms; (iv) genes that encode inherently disordered or highly interacting proteins exhibit lower variability; and (v) genes with less variable expression are enriched for house-keeping functions, while genes with highly variable expression tend to function in development and extra-cellular response and are associated with human diseases. Thus, our analysis reveals a number of potential mediators as well as functional and evolutionary correlates of EV, and provides new insights into the inherent variability in eukaryotic gene expression.

Universal attenuators and their interactions with feedback loops in gene regulatory networks

“…Theoretical and experimental efforts have provided strong evidence that variability in gene expression plays a significant role in controlling cellular phenotypes in development (49), health (50) and disease (51,52). While the transcriptional mechanisms responsible for controlling this variability continue to be an active area of research (53), the potential system-level interactions are less well explored, and network analysis of GRNs is a powerful approach to fill this gap.…”

Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors

“…We calculated the log ratio of observed to expected variability as described in Alemu et al [19]. This method, which fits a local-likelihood regression method to estimate expected variability as a function of each gene's mean expression level was shown to better control for variability of lowlyexpressed genes than the commonly used coefficient of variation.…”

“…We obtained publicly available gene expression microarray data for tumors in each of the five tumors from the Gene Expression Barcode project [20,21]. Since expression is not available for normal samples in all tissues in this platform, we defined hypervariability by calculating the log-ratio of observed variability to expected variability (conditioned on mean expression level) across tumor samples for each gene [19], and then tested association between hyper-variability (observed is twice the expected variability) and the gene's TSS being inside a hypo-methylation block in each cancer type. We found that hyper-variability is enriched in the hypomethylation blocks in each cancer type (P <0.05) except breast cancer (P = 0.5) where the small number of hypomethylation domains results in lack of power.…”

Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling

“…There is evidence that suggests biological variance plays an important part in determining phenotypes (Ozbudak et al, 2002; Colman-Lerner et al, 2005; Raser and O’Shea, 2005; Cai et al, 2006; Manolio et al, 2009). Such variability, particularly in the context of gene expression, may be indicative of genomic or epigenomic influences on the function of a given gene or protein (Alemu et al, 2014). Genes with more constrained expression have been reported to be more likely to encode products with “housekeeping” functions, whereas genes with more variable expression tended to be those involved in developmental and environmental responses and more often associated with disease (Alemu et al, 2014).…”