Chorley Brian N, Wang Xuting, Campbell Michelle R, Pittman Gary S, Noureddine Maher A, Bell Douglas A
Environmental Genomics Section, Laboratory of Molecular Genetics, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, United States.
Mutat Res. 2008 Jul-Aug;659(1-2):147-57. doi: 10.1016/j.mrrev.2008.05.001. Epub 2008 May 4.
The most common form of genetic variation, single nucleotide polymorphisms or SNPs, can affect the way an individual responds to the environment and modify disease risk. Although most of the millions of SNPs have little or no effect on gene regulation and protein activity, there are many circumstances where base changes can have deleterious effects. Non-synonymous SNPs that result in amino acid changes in proteins have been studied because of their obvious impact on protein activity. It is well known that SNPs within regulatory regions of the genome can result in disregulation of gene transcription. However, the impact of SNPs located in putative regulatory regions, or rSNPs, is harder to predict for two primary reasons. First, the mechanistic roles of non-coding genomic sequence remain poorly defined. Second, experimental validation of the functional consequences of rSNPs is often slow and laborious. In this review, we summarize traditional and novel methodologies for candidate rSNPs selection, in particular in silico techniques that aid in candidate rSNP selection. Additionally we will discuss molecular biological techniques that assess the impact of rSNPs on binding of regulatory machinery, as well as functional consequences on transcription. Standard techniques such as EMSA and luciferase reporter constructs are still widely used to assess effects of rSNPs on binding and gene transcription; however, these protocols are often bottlenecks in the discovery process. Therefore, we highlight novel and developing high-throughput protocols that promise to aid in shortening the process of rSNP validation. Given the large amount of genomic information generated from a multitude of re-sequencing and genome-wide SNP array efforts, future focus should be to develop validation techniques that will allow greater understanding of the impact these polymorphisms have on human health and disease.
最常见的基因变异形式，即单核苷酸多态性（SNPs），会影响个体对环境的反应方式并改变疾病风险。尽管数百万个SNP中的大多数对基因调控和蛋白质活性影响很小或没有影响，但在许多情况下，碱基变化可能会产生有害影响。由于其对蛋白质活性有明显影响，导致蛋白质氨基酸变化的非同义SNP已得到研究。众所周知，基因组调控区域内的SNP可导致基因转录失调。然而，位于假定调控区域的SNP（即rSNP）的影响较难预测，主要有两个原因。第一，非编码基因组序列的机制作用仍不清楚。第二，对rSNP功能后果的实验验证通常缓慢且费力。在本综述中，我们总结了用于选择候选rSNP的传统方法和新方法，特别是有助于选择候选rSNP的计算机技术。此外，我们将讨论评估rSNP对调控机制结合影响的分子生物学技术，以及对转录的功能后果。诸如电泳迁移率变动分析（EMSA）和荧光素酶报告基因构建体等标准技术仍被广泛用于评估rSNP对结合和基因转录的影响；然而，这些方案往往是发现过程中的瓶颈。因此，我们重点介绍了有望有助于缩短rSNP验证过程的新型和正在发展的高通量方案。鉴于从大量重测序和全基因组SNP阵列研究中产生了大量基因组信息，未来的重点应该是开发验证技术，以便更好地了解这些多态性对人类健康和疾病的影响。
