Developing a Predictive Gene Classifier for Autism Spectrum Disorders Based upon Differential Gene Expression Profiles of Phenotypic Subgroups.
Developing a Predictive Gene Classifier for Autism Spectrum Disorders Based upon Differential Gene Expression Profiles of Phenotypic Subgroups.
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DOI：
10.7156/najms.2013.0603107
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发表时间：
2013-01-01
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影响因子：
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通讯作者：
Lai, Yinglei
中科院分区：
文献类型：
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作者：
Hu, Valerie W;Lai, Yinglei
自闭症谱系障碍（ASD）是神经发育障碍，目前仅根据异常刻板行为以及可观察到的沟通和社会功能缺陷来诊断。虽然在遗传分析的基础上已经确定了各种候选基因，并且高达20%的ASD病例可以与遗传异常共同相关，但没有单一基因或遗传变异适用于超过1- 2%的一般ASD人群。在这份报告中，我们应用类预测算法的基因表达谱的淋巴母细胞系（LCL）从几个特发性自闭症的表型亚组定义的自闭症诊断访谈修订的ASD诊断工具的行为严重程度评分的聚类分析。我们进一步证明，这些ASD亚组的个体可以从非自闭症控制的基础上，有限的差异表达基因的预测分类准确性高达94%，灵敏度和特异性的~90%或更好，基于支持向量机分析留一法验证。通过高通量定量核酸酶保护测定法对“分类器”基因的子集进行验证，使用一组新的LCL样本，这些样本来自一个表型亚组中的个体和一组新的对照，结果总体分类预测准确率为~ 82%，灵敏度为~90%，特异性为75%。虽然需要更大的队列进行额外的验证，并且有效的临床翻译必须包括确认早期发育病例的原代细胞中差异表达的基因，但我们认为，基于对ASD个体表型更同质亚组的表达分析，这些基因组可能是诊断特发性自闭症亚型的有用生物标志物。
Autism spectrum disorders (ASD) are neurodevelopmental disorders which are currently diagnosed solely on the basis of abnormal stereotyped behavior as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been identified on the basis of genetic analyses and up to 20% of ASD cases can be collectively associated with a genetic abnormality, no single gene or genetic variant is applicable to more than 1-2 percent of the general ASD population. In this report, we apply class prediction algorithms to gene expression profiles of lymphoblastoid cell lines (LCL) from several phenotypic subgroups of idiopathic autism defined by cluster analyses of behavioral severity scores on the Autism Diagnostic Interview-Revised diagnostic instrument for ASD. We further demonstrate that individuals from these ASD subgroups can be distinguished from nonautistic controls on the basis of limited sets of differentially expressed genes with a predicted classification accuracy of up to 94% and sensitivities and specificities of ~90% or better, based on support vector machine analyses with leave-one-out validation. Validation of a subset of the "classifier" genes by high-throughput quantitative nuclease protection assays with a new set of LCL samples derived from individuals in one of the phenotypic subgroups and from a new set of controls resulted in an overall class prediction accuracy of ~82%, with ~90% sensitivity and 75% specificity. Although additional validation with a larger cohort is needed, and effective clinical translation must include confirmation of the differentially expressed genes in primary cells from cases earlier in development, we suggest that such panels of genes, based on expression analyses of phenotypically more homogeneous subgroups of individuals with ASD, may be useful biomarkers for diagnosis of subtypes of idiopathic autism.