Next-generation DNA sequencing-based assay for measuring allelic expression imbalance (AEI) of candidate neuropsychiatric disorder genes in human brain.
Next-generation DNA sequencing-based assay for measuring allelic expression imbalance (AEI) of candidate neuropsychiatric disorder genes in human brain.
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基于下一代 DNA 测序的测定法，用于测量人脑中候选神经精神疾病基因的等位基因表达失衡 (AEI)
DOI：
10.1186/1471-2164-12-518
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发表时间：
2011-10-20
期刊：
影响因子：
4.4
通讯作者：
Saffen D
中科院分区：
文献类型：
--
作者：
Xu X;Wang H;Zhu M;Sun Y;Tao Y;He Q;Wang J;Chen L;Saffen D
背景调控基因表达的常见遗传变异被广泛怀疑与复杂疾病的病因学和表型变异有关。虽然已经开发了高通量的基于微阵列的分析来测量独立样本之间的mRNA表达的差异，但这些分析往往缺乏检测稀有mRNAs的灵敏度和量化mRNAs表达的微小变化的重复性。相比之下，基于聚合酶链式反应的等位基因表达不平衡分析(AEI)，它使用信使核糖核酸单核苷酸多态(MSNP)来区分个体样本中的表达和一对遗传等位基因，具有高灵敏度和准确性，允许以高重复性量化超过1.2倍的信使核糖核酸表达差异。在这篇文章中，我们描述了一种高效的基于PCR/下一代DNA测序的方法来分析人脑中候选神经精神障碍基因的等位基因特异性差异表达。结果使用我们的方法，我们成功地分析了52个独立的人脑样本中70个候选神经精神障碍基因的AEI。在这些基因中，62/70(89%)至少有一个样本的AEI比值大于1±0.2，8/70(11%)没有AEI。按照从负值到正值的递增顺序排列log2AEI比率，可以发现log2AEI比率的高度重复性分布，这些分布对于每个基因/标记SNP组合是不同的。数学模拟表明，这些log2AEI分布可以提供关于顺式作用调控变异体的数量、位置和对mRNA表达的贡献的重要线索。结论我们建立了一种高度敏感和重复性好的定量人脑表达mRNA的AEI的方法。重要的是，这种方法可以定量检测许多候选疾病基因的差异mRNA表达，这些基因完全没有被先前发表的基于微阵列的人脑mRNA表达研究所遗漏。鉴于下一代测序技术能够产生大量独立的测序读数，我们的方法应该适用于在一次实验中分析100个样本中100到200个候选基因。我们认为，这是研究已定义集合候选障碍基因mRNA表达差异的合适尺度，例如，允许全面覆盖在特定障碍所涉及的生物途径中发挥功能的基因。这项研究中描述的AEI测量和数学模型的结合可以帮助识别与mRNA表达相关的SNPs。这些SNPs的等位基因(单独或成组)准确预测高或低mRNA表达，在旨在将候选基因与特定神经精神疾病联系起来的遗传关联研究中应该是有用的标记。
BackgroundCommon 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.ResultsUsing 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 ofcis-acting regulatory variants to mRNA expression.ConclusionsWe 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.
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影响因子：
48
作者：
Craig, David W.;Pearson, John V.;Szelinger, Szabolcs;Sekar, Aswin;Redman, Margot;Corneveaux, Jason J.;Pawlowski, Traci L.;Laub, Trisha;Nunn, Gary;Stephan, Dietrich A.;Homer, Nils;Huentelman, Matthew J.
通讯作者：
Huentelman, Matthew J.
影响因子：
168.9
作者：
Ballard, Clive;Gauthier, Serge;Jones, Emma
通讯作者：
Jones, Emma
DOI：
10.1093/bfgp/elp021
发表时间：
2009-07-01
期刊：
Briefings in Functional Genomics & Proteomics
影响因子：
--
作者：
Epstein, Douglas J.
通讯作者：
Epstein, Douglas J.
影响因子：
5.3
作者：
Bray, NJ;Buckland, PR;O'Donovan, MC
通讯作者：
O'Donovan, MC
影响因子：
3.5
作者：
Heap GA;Yang JH;Downes K;Healy BC;Hunt KA;Bockett N;Franke L;Dubois PC;Mein CA;Dobson RJ;Albert TJ;Rodesch MJ;Clayton DG;Todd JA;van Heel DA;Plagnol V
通讯作者：
Plagnol V