Genomic analyses of autism spectrum disorders

描述（由申请人提供）：本研究的双重目标是使用大规模基因组方法来识别来自自闭症个体的细胞系中自闭症谱系障碍（ASD）的生物标志物，并更好地了解这些疾病的生物学。推动这种分析方法的核心假设是，自闭症谱系障碍患者的血细胞将反映与自闭症相关的分子缺陷或遗传控制元件。我们的初步研究结果支持了这一假设和方法：1）来自自闭症谱系障碍严重程度不同的三对同卵双胞胎血液的细胞系显示出不同的表达基因概况； 2）共享的高度差异表达基因在对神经系统发育和功能至关重要的通路中显着富集； 3)与来自相应未受影响兄弟姐妹的细胞系中相同基因的表达水平相比，某些基因的表达水平似乎与疾病的严重程度相关； 4)来自初步微阵列研究的候选基因具有包含已报道的自闭症易感性基因或基因座的相关数量性状基因座。因此，在容易获得的血液来源细胞中观察到的这种差异表达谱可能反映了自闭症患者大脑与正常大脑中的异常基因表达。本研究将利用 DNA 微阵列来：1) 鉴定自闭症谱系障碍患者与未受影响个体的淋巴母细胞系中差异表达的基因； 2) 确定使用现有诊断仪器（例如 ADI-R）根据 ASD 表型表达分离的 ASD 亚组是否可以通过基因表达谱进行区分； 3）基于各种类预测算法（包括k近邻、质心分类和神经网络）构建ASD分类器； 4) 分析实验亚组中受影响的信号或代谢途径； 5) 使用自闭症遗传学资源交换基因型数据库中的现有遗传数据绘制并识别导致差异表达基因的遗传决定因素。目前，自闭症谱系障碍的诊断主要依赖于临床医生或治疗师有时有偏见的行为观察以及家长/老师的问卷调查。可靠的生物标志物将极大地促进这些疾病的早期和明确检测，从而允许早期干预和治疗。

DESCRIPTION (provided by applicant): The twin goals of this study are to use large-scale genomic methods to identify biomarkers of autism spectrum disorders (ASD) in cell lines derived from autistic individuals, and to gain a better understanding of the biology of these disorders. The central hypothesis driving this analytical approach is that blood cells from individuals with ASD will reflect molecular defects or genetic control elements that are relevant to autism. This hypothesis and approach is supported by of our preliminary findings: 1) cell lines derived from the blood of three identical twin pairs that differ in severity of ASD show a different profile of expressed genes; 2) the shared highly differentially expressed genes are significantly enriched in pathways critical to the development and function of the nervous system; 3) the levels of expression of certain genes appear to be related to the severity of the disorder when compared to the levels of expression of the same genes in cell lines from respective non-affected siblings; 4) candidate genes from preliminary microarray studies have associated quantitative trait loci containing reported autism susceptibility genes or loci. Thus, this differential expression profile which is observed in easily accessible blood-derived cells may be reflective of aberrant gene expression in the autistic vs. normal brain. This study will utilize DNA microarrays to: 1) identify differentially expressed genes in lymphoblastoid cell lines from individuals with ASD in comparison to unaffected individuals; 2) determine whether subgroups of ASD segregated according to phenotypic expression of ASD using existing diagnostic instruments, such as the ADI-R, can be differentiated through gene expression profiling; 3) build a classifier for ASD based on various class prediction algorithms, including k-nearest neighbors, centroid classification, and neural networks; 4) analyze signaling or metabolic pathways affected in the experimental subgroups; 5) map and identify genetic determinants that are responsible for differentially expressed genes using existing genetic data in the Autism Genetics Resource Exchange genotype database. At present, diagnosis of ASD relies primarily upon sometimes biased behavioral observations by clinicians or therapists and parent/teacher questionnaires. Reliable biomarkers would greatly facilitate the early and definitive detection of these disorders, thereby permitting early intervention and therapy.