Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis
Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis
复制标题
利用自闭症异常 mRNA 表达进行基因发现和诊断
DOI：
10.1007/s00439-016-1673-7
复制
发表时间：
2016-07-01
期刊：
影响因子：
5.3
通讯作者：
Cai, James J.
中科院分区：
文献类型：
--
作者：
Guan, Jinting;Yang, Ence;Cai, James J.
自闭症谱系障碍(ASD)的特点是大量的表型和遗传异质性，这使得识别导致疾病的遗传因素变得非常复杂。研究设计主要集中在病例和对照之间的群体差异。问题是，从本质上讲，基于群体差异的方法(例如，差异表达分析)模糊或瓦解了群体内的异质性。由于忽略了组内表达可变的基因，导致受影响个体表达差异的一个重要的遗传异质性轴被忽视了。为此，我们发展了一种新的基因表达分析方法--异常基因表达分析，该方法基于通常用于孤立点检测的多变量距离。我们的方法检测不同组之间的基因表达差异，并识别表达差异显著的基因。使用这种新方法，我们重新访问了从47个ASD和57个对照样本的死后脑组织中产生的RNA测序数据。我们鉴定了54个功能基因组，它们在ASD样本中的表达差异比对照组更明显，以及76个共表达模块，它们在对照组中存在，但在ASD样本中由于ASD特有的异常基因表达而缺失。我们还利用异常表达的基因作为ASD诊断的生物标志物。使用全血表达数据集，我们识别了三个异常表达的基因集，它们的表达水平作为区分变量获得了70%的分类准确率。总之，我们的方法代表了ASD的一种新的发现和诊断策略。我们的发现可能有助于为其他遗传异质性疾病开辟一条以表达可变性为中心的研究途径。
Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.