The genetic heterogeneity of autism has stymied the search for causes and cures. Even whole-genomic studies on large numbers of families have yielded results of relatively little impact. In the present work, we analyze two genomic databases using a novel strategy that takes prior knowledge of genetic relationships into account and that was designed to boost signal important to our understanding of the molecular basis of autism. Our strategy was designed to identify significant genomic variation within a priori defined biological concepts and improves signal detection while lessening the severity of multiple test correction seen in standard analysis of genome-wide association data. Upon application of our approach using 3,244 biological concepts, we detected genomic variation in 68 biological concepts with significant association to autism in comparison to family-based controls. These concepts clustered naturally into a total of 19 classes, principally including cell adhesion, cancer, and immune response. The top-ranking concepts contained high percentages of genes already suspected to play roles in autism or in a related neurological disorder. In addition, many of the sets associated with autism at the DNA level also proved to be predictive of changes in gene expression within a separate population of autistic cases, suggesting that the signature of genomic variation may also be detectable in blood-based transcriptional profiles. This robust cross-validation with gene expression data from individuals with autism coupled with the enrichment within autism-related neurological disorders supported the possibility that the mutations play important roles in the onset of autism and should be given priority for further study. In sum, our work provides new leads into the genetic underpinnings of autism and highlights the importance of reanalysis of genomic studies of complex disease using prior knowledge of genetic organization.

中文翻译：

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与自闭症相关的途径中基因组变异的丰富

自闭症的遗传异质性阻碍了寻找原因和治疗方法的努力。即使是对大量家庭的全基因组研究，其影响也相对较小。在当前的工作中，我们使用一种新颖的策略分析了两个基因组数据库，该策略考虑了遗传关系的先验知识，旨在增强对我们对自闭症分子基础的理解很重要的信号。我们的策略旨在在先验定义的生物学概念内识别明显的基因组变异，并改善信号检测，同时降低在全基因组关联数据的标准分析中看到的多次测试校正的严重性。在运用3,244个生物学概念应用我们的方法后，与基于家庭的对照相比，我们检测到68个生物学概念的基因组变异与自闭症存在显着关联。这些概念自然地分为19类，主要包括细胞粘附，癌症和免疫反应。排名靠前的概念包含高百分比的基因，这些基因已经被怀疑在自闭症或相关的神经系统疾病中起作用。此外，在DNA水平上与自闭症相关的许多研究还证明可以预测独立的自闭症患者群体中基因表达的变化，这表明在基于血液的转录谱中也可以检测到基因组变异的特征。对来自自闭症个体的基因表达数据以及与自闭症相关的神经系统疾病的丰富性进行的强大的交叉验证，支持了这种突变在自闭症发作中起重要作用的可能性，因此应优先进行进一步研究。总而言之，我们的工作为自闭症的遗传基础提供了新线索，并强调了利用遗传组织的先验知识对复杂疾病的基因组研究进行重新分析的重要性。