Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome
Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome
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DOI：
10.1186/1755-8794-6-34
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发表时间：
2013-09-24
影响因子：
2.7
通讯作者：
Kong, Sek Won
中科院分区：
文献类型：
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作者：
Campbell, Malcolm G.;Kohane, Isaac S.;Kong, Sek Won
背景：数十年的研究强烈表明，自闭症谱系障碍（ASD）的遗传病因是异质性的。然而，大多数已发表的研究集中在病例和对照组之间的组差异。相比之下，我们假设这种疾病的异质性可以通过识别个体是离群值的通路来表征，而不是通过识别代表ASD诊断的共享组差异的通路来表征。两个先前发表的血液基因表达数据集-翻译遗传学研究所（TGen）数据集（70例和60例无关对照）和Simons Simplex Consortium（Simons）数据集（221名先证者和191名未受影响的家庭成员）进行了分析。每个数据集的所有个人都被投射到生物学途径，每个样本的Mahalanobis距离从一个汇集的质心计算比较的情况下，控制离群值的数量为每个pathway.Results：一组血液基因表达谱的分析从70 ASD和60无关的控制揭示了三种途径，其离群值显着overrepresented在ASD的情况下：神经元发育，包括轴突发生和神经突发育（ASD的29%，对照的3%）、一氧化氮信号传导（29%，3%）和骨骼发育（27%，3%）。总体而言，50%的病例和8%的对照是这三种途径之一的离群值，无法使用组比较或基因水平离群值方法进行识别。在由221名ASD和191名未受影响的家庭成员组成的独立收集的数据集中，神经发生途径中的离群值严重偏向病例（ASD的20.8%，对照的12.0%）。有趣的是，神经发生离群值在未受影响的家庭成员（Simons）中比无关对照（TGen）更常见，但这种影响的统计学显著性是边际的（卡方P < 0.09）。结论：与组差异方法不同，我们的分析确定了病例组和对照组中表现出每个表达信号的样本，并显示离群值组对于每个涉及的通路是不同的。此外，我们的研究结果表明，通过寻求异质性，基于路径的离群值分析可以揭示表达信号，当只考虑共享的组差异时，这些信号并不明显。
Background: Decades of research strongly suggest that the genetic etiology of autism spectrum disorders (ASDs) is heterogeneous. However, most published studies focus on group differences between cases and controls. In contrast, we hypothesized that the heterogeneity of the disorder could be characterized by identifying pathways for which individuals are outliers rather than pathways representative of shared group differences of the ASD diagnosis.Methods: Two previously published blood gene expression data sets - the Translational Genetics Research Institute (TGen) dataset (70 cases and 60 unrelated controls) and the Simons Simplex Consortium (Simons) dataset (221 probands and 191 unaffected family members) - were analyzed. All individuals of each dataset were projected to biological pathways, and each sample's Mahalanobis distance from a pooled centroid was calculated to compare the number of case and control outliers for each pathway.Results: Analysis of a set of blood gene expression profiles from 70 ASD and 60 unrelated controls revealed three pathways whose outliers were significantly overrepresented in the ASD cases: neuron development including axonogenesis and neurite development (29% of ASD, 3% of control), nitric oxide signaling (29%, 3%), and skeletal development (27%, 3%). Overall, 50% of cases and 8% of controls were outliers in one of these three pathways, which could not be identified using group comparison or gene-level outlier methods. In an independently collected data set consisting of 221 ASD and 191 unaffected family members, outliers in the neurogenesis pathway were heavily biased towards cases (20.8% of ASD, 12.0% of control). Interestingly, neurogenesis outliers were more common among unaffected family members (Simons) than unrelated controls (TGen), but the statistical significance of this effect was marginal (Chi squared P < 0.09).Conclusions: Unlike group difference approaches, our analysis identified the samples within the case and control groups that manifested each expression signal, and showed that outlier groups were distinct for each implicated pathway. Moreover, our results suggest that by seeking heterogeneity, pathway-based outlier analysis can reveal expression signals that are not apparent when considering only shared group differences.