Analysis of early vocalization features in children at high genetic risk for autism

项目总结/摘要 非典型的社会沟通是ASD的核心特征，其中延迟或异常的语言构成了最 父母对最终确诊的儿童的关注的常见最初原因。声乐发展 特别是在ASD儿童中通常是延迟的或非典型的，越来越多的文献表明，早在 婴儿期，发声特征，如能言善辩，声音成熟，持续时间和音高可能是有用的预测 ASD的诊断。然而，对发育过程和遗传机制知之甚少， 有助于这些协会，限制翻译的努力，以有效地检测和治疗异常发展 与自闭症和语言延迟有关。神经遗传性耳聋人群早期发声特征的研究 与ASD高度相关的NGS可以解决这一差距，因为NGS提供了一个“饱和”模型， 用于ASD出现的前瞻性监测。具体来说，因为NGS中的遗传异常是 已经知道，从广泛共享的特征中分离出NGS特异性的表型特征， 多个NGS（例如，由共同的智力或医学特征驱动的特征）可以告知生物贡献者 自闭症相关的非典型社交本提案使用这种交叉综合征方法， 在3个不同的、未充分研究的NGS中描述早期发声特征及其与ASD的关联： 综合征（AS）、唐氏综合征（DS）和脆性X综合征（FXS）。AIM 1将应用一种新颖的编码流水线 为了有效地识别早期发声发育的综合征特异性特征， 标准化评估工具的缺陷。事实上，这些标准化的措施往往是无效的， 捕捉NGS儿童的变异性，部分原因是限制分数变异性的高水平“地板效应”。 因此，这一目标的结果有望通过以下方面推动该领域的发展：（1）开发用于早期测量的程序 更适合NGS人群使用的发声发展，以及（2）建立在非常有限的 了解3个罕见NGS人群的早期发声发育。Aim 2将测试 在AS、DS和FXS中具有ASD相关特征的早期发声特征，所有这些都表现出独特的病因学 发展ASD的风险。这一目标的结果有望提高我们对发展和 非典型社会交流特征与ASD相关的遗传途径，如发声 可以认为与多个NGS组中的ASD要素关联的要素是可靠的 ASD的预测因素在病因学上不同的风险组，而症状特异性协会可能会告知 不同的发育或遗传机制，可以解释这些协会。总体而言，该项目直接 与NIDCD的使命一致，即告知发育表型和潜在的遗传基础， ASD中的语言障碍，最终推进翻译工作，以有效地检测和治疗这些 功能.

PROJECT SUMMARY/ABSTRACT Atypical social communication is a core feature of ASD, with delayed or abnormal language constituting the most common initial cause for parental concern for children who are ultimately diagnosed. Vocal development in particular is often delayed or atypical in children with ASD, and growing literature suggests that as early as infancy, vocalization features such as volubility, vocal maturity, duration, and pitch may be useful in predicting later ASD diagnosis. However, little is known about the developmental processes and genetic mechanisms that contribute to these associations, limiting translational efforts to efficiently detect and treat abnormal development related to ASD and language delays. Characterizing early vocalization features in populations with neurogenetic syndromes (NGS) that are highly associated with ASD can address this gap, as NGS offer a “saturated” model for prospective surveillance of ASD emergence. Specifically, because the genetic abnormalities in NGS are already known, isolating phenotypic features that are specific to an NGS from features broadly shared across multiple NGS (e.g. features driven by shared intellectual or medical features) may inform biological contributors to ASD-associated atypical social communication. The present proposal uses this cross-syndrome approach to characterize early vocalization features and associations with ASD in 3 distinct, understudied NGS: Angelman syndrome (AS), Down syndrome (DS), and fragile X syndrome (FXS). Aim 1 will apply a novel coding pipeline to efficiently identify syndrome-specific profiles of early vocal development that are not limited by traditional shortcomings of standardized assessment tools. Indeed, these standardized measures are often ineffective at capturing variability in children with NGS due, in part, to high levels of “floor effects” that limit score variability. Thus, the results of this aim are expected to advance the field by (1) developing procedures for measuring early vocal development that are more appropriate for use in NGS populations, and (2) building on very limited understanding of early vocal development in 3 rare NGS populations. Aim 2 will then test the associations of early vocal features with ASD-associated features in AS, DS and FXS, all of which demonstrate unique etiological risk for developing ASD. Results of this aim are expected to improve our understanding of developmental and genetic pathways by which atypical social communication features are associated with ASD, as vocalization features that demonstrate associations with ASD features across multiple NGS groups can be considered robust predictors of ASD across etiologically-distinct risk groups, while syndrome-specific associations may inform distinct developmental or genetic mechanisms that may explain these associations. Overall, this project directly aligns with the mission of NIDCD to inform the developmental phenotypes and potential genetic bases of language impairments in ASD, ultimately advancing translational efforts to effectively detect and treat these features.