Institutes of Brain Science, Fudan University, 138 Yixueyuan Road, Shanghai 200032, China.
BMC Genomics. 2011 Oct 20;12:518. doi: 10.1186/1471-2164-12-518.
Common genetic variants that regulate gene expression are widely suspected to contribute to the etiology and phenotypic variability of complex diseases. Although high-throughput, microarray-based assays have been developed to measure differences in mRNA expression among independent samples, these assays often lack the sensitivity to detect rare mRNAs and the reproducibility to quantify small changes in mRNA expression. By contrast, PCR-based allelic expression imbalance (AEI) assays, which use a "marker" single nucleotide polymorphism (mSNP) in the mRNA to distinguish expression from pairs of genetic alleles in individual samples, have high sensitivity and accuracy, allowing differences in mRNA expression greater than 1.2-fold to be quantified with high reproducibility. In this paper, we describe the use of an efficient PCR/next-generation DNA sequencing-based assay to analyze allele-specific differences in mRNA expression for candidate neuropsychiatric disorder genes in human brain.
Using our assay, we successfully analyzed AEI for 70 candidate neuropsychiatric disorder genes in 52 independent human brain samples. Among these genes, 62/70 (89%) showed AEI ratios greater than 1 ± 0.2 in at least one sample and 8/70 (11%) showed no AEI. Arranging log2AEI ratios in increasing order from negative-to-positive values revealed highly reproducible distributions of log2AEI ratios that are distinct for each gene/marker SNP combination. Mathematical modeling suggests that these log2AEI distributions can provide important clues concerning the number, location and contributions of cis-acting regulatory variants to mRNA expression.
We have developed a highly sensitive and reproducible method for quantifying AEI of mRNA expressed in human brain. Importantly, this assay allowed quantification of differential mRNA expression for many candidate disease genes entirely missed in previously published microarray-based studies of mRNA expression in human brain. Given the ability of next-generation sequencing technology to generate large numbers of independent sequencing reads, our method should be suitable for analyzing from 100- to 200-candidate genes in 100 samples in a single experiment. We believe that this is the appropriate scale for investigating variation in mRNA expression for defined sets candidate disorder genes, allowing, for example, comprehensive coverage of genes that function within biological pathways implicated in specific disorders. The combination of AEI measurements and mathematical modeling described in this study can assist in identifying SNPs that correlate with mRNA expression. Alleles of these SNPs (individually or as sets) that accurately predict high- or low-mRNA expression should be useful as markers in genetic association studies aimed at linking candidate genes to specific neuropsychiatric disorders.
普遍存在的调节基因表达的遗传变异，被广泛认为是复杂疾病病因和表型变异的原因。尽管已经开发出了用于测量独立样本间 mRNA 表达差异的高通量、基于微阵列的检测方法，但这些方法往往缺乏检测稀有 mRNA 的灵敏度，也缺乏定量检测 mRNA 表达微小变化的重现性。相比之下，基于聚合酶链反应的等位基因表达失衡（AEI）检测方法，使用 mRNA 中的“标记”单核苷酸多态性（mSNP）来区分个体样本中遗传等位基因的表达，具有很高的灵敏度和准确性，能够定量检测到大于 1.2 倍的 mRNA 表达差异，且具有很高的重现性。在本文中，我们描述了一种高效的基于聚合酶链反应/下一代 DNA 测序的检测方法，用于分析人类大脑中候选神经精神疾病基因的 mRNA 表达的等位基因特异性差异。
使用我们的检测方法，我们成功地分析了 52 个独立的人类大脑样本中 70 个候选神经精神疾病基因的 AEI。在这些基因中，62/70（89%）在至少一个样本中表现出大于 1±0.2 的 AEI 比，8/70（11%）则没有 AEI。按照从负到正的对数 AEI 比的增加顺序排列，揭示了每个基因/标记 SNP 组合的高度重现性的对数 AEI 比分布。数学模型表明，这些对数 AEI 分布可以为 cis 作用调节变异对 mRNA 表达的数量、位置和贡献提供重要线索。
我们已经开发出一种高度敏感和重现性的方法，用于定量分析人类大脑中表达的 AEI 的 mRNA。重要的是，与之前发表的人类大脑 mRNA 表达的基于微阵列的研究相比，该检测方法可以完全检测到许多候选疾病基因的差异 mRNA 表达。鉴于下一代测序技术能够产生大量独立的测序读段，我们的方法应该适用于在单个实验中分析 100-200 个候选基因的 100 个样本。我们认为，这是研究特定疾病相关候选基因的 mRNA 表达变异的适当规模，例如，全面涵盖在特定疾病中起作用的生物途径内的基因。本研究中描述的 AEI 测量和数学建模的组合可以帮助识别与 mRNA 表达相关的 SNP。这些 SNP 的等位基因（单独或作为集合）如果能够准确预测高或低的 mRNA 表达，则可以作为遗传关联研究的标记，旨在将候选基因与特定的神经精神疾病联系起来。
