![]() ![]() Received: JanuAccepted: JPublished: July 6, 2023Ĭopyright: © 2023 Wolf et al. Our results suggest that the mechanisms responsible for maintaining optimal levels of variation in high- versus low-variance differ, and that this variability is the result of different patterns of selection.Ĭitation: Wolf S, Melo D, Garske KM, Pallares LF, Lea AJ, Ayroles JF (2023) Characterizing the landscape of gene expression variance in humans. Specifically, we uncovered gene expression variance was significantly associated with gene length, nucleotide diversity, the degree of connectivity and the presence of non-coding RNA. Using large publicly available RNA-seq data sets, we were able to identify the general properties associated with high- and low-variance genes, as well as factors driving variation in variance across genes. Here, we investigate the landscape of transcriptional variance across tissues, populations, and studies. While the average level of gene expression is typically the focus of research, the variation around this average level (i.e., gene expression variance) can also be important for understanding complex traits and disease. Gene expression variance, or the variation in the level of gene expression within a population, can have significant impacts on physiology, disease, and evolutionary adaptations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. These results show that the pattern of transcriptional variance is not noise. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. ![]() Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research.
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