Using genomic tools to study regulatory evolution.
Using genomic tools to study regulatory evolution.
复制标题
使用基因组工具研究调控进化。
DOI：
10.1007/978-1-61779-585-5_14
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Gilad,Yoav
中科院分区：
文献类型：
--
作者：
Gilad,Yoav
基因调控的差异被认为在物种形成和适应方面发挥着重要作用。对基因表达水平的比较基因组学研究已经确定了大量在物种之间差异表达的基因，在一些情况下，还指出了物种间基因调控差异与最终生理或形态表型差异之间的联系。基因调控变化的潜在机制也正在积极地利用比较基因组学方法进行研究。然而，不同的调控机制对物种间基因表达水平差异的相对重要性尚未被很好地理解。特别是，通常很难推断调控机制的明显差异和基因表达水平变化之间的因果关系，这一挑战因序列变异和基因调控之间的联系不明确而变得更加困难。事实上，在某些情况下，即使相关调控元件的序列发生了变化，基因调控也是可以保守的。在这一章中，我考察了不同的基因组方法来研究调节进化以及潜在的遗传和表观遗传调节机制。我试图区分假设驱动的研究和探索性研究，并认为后一类研究本身提供了有价值的信息，也为前者提供了必要的背景。我讨论了与基因组研究的研究设计和统计分析相关的问题，并回顾了关于基因表达水平和相关调控机制的自然选择的证据。所讨论的大多数问题与多变量基因组数据的一般性质有关，因此无论用于收集高通量基因组数据的技术(例如，微阵列或大规模并行测序)如何，往往都是相关的。
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).
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