Hu Valerie W, Steinberg Mara E
Department of Biochemistry and Molecular Biology, The George Washington University Medical Center, 2300 Eye St., N.W., Washington, DC 20037, USA.
Autism Res. 2009 Apr;2(2):67-77. doi: 10.1002/aur.72.
Heterogeneity in phenotypic presentation of Autism spectrum disorders has been cited as one explanation for the difficulty in pinpointing specific genes involved in autism. Recent studies have attempted to reduce the "noise" in genetic and other biological data by reducing the phenotypic heterogeneity of the sample population. The current study employs multiple clustering algorithms on 123 item scores from the Autism Diagnostic Interview-Revised (ADI-R) diagnostic instrument of nearly 2,000 autistic individuals to identify subgroups of autistic probands with clinically relevant behavioral phenotypes in order to isolate more homogeneous groups of subjects for gene expression analyses. Our combined cluster analyses suggest optimal division of the autistic probands into four phenotypic clusters based on similarity of symptom severity across the 123 selected item scores. One cluster is characterized by severe language deficits, while another exhibits milder symptoms across the domains. A third group possesses a higher frequency of savant skills while the fourth group exhibited intermediate severity across all domains. Grouping autistic individuals by multivariate cluster analysis of ADI-R scores reveals meaningful phenotypes of subgroups within the autistic spectrum, which we show, in a related (accompanying) study, to be associated with distinct gene expression profiles.
自闭症谱系障碍（ASD）表型表现的异质性被认为是难以确定自闭症相关特定基因的一个原因。最近的研究试图通过减少样本群体的表型异质性来降低遗传和其他生物学数据中的“噪音”。本研究对近2000名自闭症个体的自闭症诊断访谈修订版（ADI-R）诊断工具中的123项得分应用了多种聚类算法，以识别具有临床相关行为表型的自闭症先证者亚组，从而分离出更同质的受试者群体用于基因表达分析。我们的联合聚类分析表明，根据123个选定项目得分的症状严重程度相似性，自闭症先证者可最佳地分为四个表型聚类。一个聚类的特征是严重的语言缺陷，而另一个聚类在各个领域表现出较轻的症状。第三组具有较高频率的学者技能，而第四组在所有领域表现出中等严重程度。通过对ADI-R得分进行多变量聚类分析对自闭症个体进行分组，揭示了自闭症谱系内亚组的有意义表型，我们在一项相关（伴随）研究中表明，这些表型与不同的基因表达谱相关。
