Using genomic tools to study regulatory evolution

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Abstract

Differences in gene regulation are thought to play an important role in speciation and adaptation. Comparative genomic studies of gene expression levels have identified a large number of differentially expressed genes among species, and, in a number of cases, also pointed to connections between interspecies differences in gene regulation and differences in ultimate physiological or morphological phenotypes. The mechanisms underlying changes in gene regulation are also being actively studied using comparative genomic approaches. However, the relative importance of different regulatory mechanisms to interspecies differences in gene expression levels is not yet well understood. In particular, it is often difficult to infer causality between apparent differences in regulatory mechanisms and changes in gene expression levels, a challenge that is compounded by the fact that the link between sequence variation and gene regulation is not clear. Indeed, in certain cases, gene regulation can be conserved even when sequences at associated regulatory elements have changed. In this chapter, I examine different genomic approaches to the study of regulatory evolution and the underlying genetic and epigenetic regulatory mechanisms. I try to distinguish between hypothesis-driven and exploratory studies, and argue that the latter class of studies provides valuable information in its own right as well as necessary context for the former. I discuss issues related to study designs and statistical analyses of genomic studies, and review the evidence for natural selection on gene expression levels and associated regulatory mechanisms. Most of the issues that are discussed pertain to the general nature of multivariate genomic data, and thus are often relevant regardless of the technology that is used to collect high-throughput genomic data (for example, microarrays or massively parallel sequencing). © 2012 Springer Science+Business Media, LLC.

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APA

Gilad, Y. (2012). Using genomic tools to study regulatory evolution. Methods in Molecular Biology, 856, 335–361. https://doi.org/10.1007/978-1-61779-585-5_14

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