Behavioral and Genetic Biomarker Development for Autism and Related Disorders

描述(由申请人提供)：本申请涉及广泛的挑战领域(03)生物标记物的发现和验证以及特定的挑战主题03-MH-101*：精神疾病中的生物标记物。这项提案的总体目标是推进自闭症和相关疾病的行为和遗传生物标记物的开发。虽然很明显，自闭症有很强的遗传遗传成分，但仅依赖自闭症(谱系)障碍的一般诊断(或仅关于受影响个人的其他信息)的超大规模基因研究在识别风险等位基因方面取得的成功有限，这给该领域留下了一个关键问题。显然，需要替代的基因研究设计来补充现有的研究。行为生物标记物，特别是语言能力，已经被用于提高基因图谱的能力，取得了一些成功，但到目前为止，研究主要集中在自闭症患者的详细行为评估上，而不是他们的家庭成员，尽管有大量文献定义了家庭成员中相关表型的比例增加。我们的项目将填补这一关键空白。我们将使用我们现有的家族集合以及来自所有家庭成员的广泛的现有临床和遗传数据数据库，其中每个家庭至少包含一名自闭症先证者和至少一名语言缺陷的先证者，以定义风险的生物标记物。对于目标1，行为生物标记物发展，我们将开发一套自闭症和相关疾病的遗传风险的行为生物标记物。我们将分析我们广泛的行为测试数据库，以确定哪些措施具有最强的遗传影响，并进一步检查潜在的类结构，以进行数据约简和减少测量误差。我们将对研究参与者的子集进行后续评估，以确定选定生物标记物的纵向稳定性。对于目标2，行为生物标记物验证，我们将通过使用全基因组分析来验证行为生物标记物的遗传成分。我们将使用准贝叶斯后验概率方法对定量和二分行为生物标记物数据进行分析，以阐明风险的遗传结构，为遗传遗传成分的性质提供证据。对于目标3，遗传生物标记物识别，我们将识别与自闭症和相关疾病的遗传风险相关的特定DNA变异。我们将从目标2中确定的区域内的候选基因中测试常见和罕见的变异，SNPs和CNV，并评估文献中的其他变异作为调节风险决定基因网络的潜在因素。总体而言，我们计划结合行为和遗传生物标记物的分析，开发更准确的模型来预测自闭症谱系障碍的风险。我们现有的详细临床和行为信息，以及后续评估计划，也将为未来与特定生物标记物相关的临床病程和治疗反应要素的比较有效性研究提供重要的初步数据。人们希望这些研究将为自闭症的病因和有效治疗提供实质性的见解。自闭症是一种严重的、令人衰弱的疾病。虽然有强有力的证据支持这种疾病的重要遗传成分，但识别特定的易感基因一直很困难。通过所提出的方法识别易感基因可以为这种疾病的生物学基础提供重要的见解，这可能导致新的治疗方法的开发。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (03) Biomarker Discovery and Validation and specific Challenge Topic 03-MH-101*: Biomarkers in Mental Disorders. The overall goal of this proposal is to advance the development of behavioral and genetic biomarkers for autism and related disorders. While it is clear that autism has a strong inherited genetic component, very large scale genetic studies that have relied only on a general diagnosis of autism (spectrum) disorders (or other information only on affected individuals) have had limited success in identifying risk alleles, leaving a critical issue for the field. Clearly, alternative genetic study designs are needed to complement existing studies. Behavioral biomarkers, especially language ability, have been used with some success to increase power in gene mapping, but to date studies have focused on detailed behavioral assessments only of subjects with autism and not their family members, despite an extensive literature defining increased rates of related phenotypes in family members. This critical gap will be filled by our project. We will use our existing family set with an extensive existing database of clinical and genetic data from all family members, where each family contains at least one proband with autism and at least one proband with a language deficit, to define biomarkers for risk. For Aim 1, Behavioral Biomarker Development, we will develop a set of behavioral biomarkers of genetic risk for autism and related disorders. We will analyze our extensive behavioral testing database to determine which measures have the strongest genetic effects and further examine latent class structures for both data reduction and to reduce measurement error. We will conduct follow-up assessments on a subset of study participants to determine longitudinal stability of selected biomarkers. For Aim 2, Behavioral Biomarker Validation, we will validate inherited components of the behavioral biomarkers through the use of genome-wide analysis. We will use analysis of quantitative and dichotomous behavioral biomarker data using a quasi-Bayesian posterior probability method to elucidate the genetic architecture of risk, providing evidence of the nature of the inherited genetic component. For Aim 3, Genetic Biomarker Identification, we will identify specific DNA variations associated with genetic risk for autism and related disorders. We will test both common and rare variants, SNPs and CNVs, from candidate genes within the regions identified in Aim 2 and evaluate additional variants from the literature as potential factors modulating a network of risk-determining genes. Overall, we plan to combine analysis of behavioral and genetic biomarkers to develop more accurate models for the prediction of risk for autism spectrum disorders. Our existing detailed clinical and behavioral information, as well as plans for follow-up assessments, will also provide important preliminary data for future comparative effectiveness studies on elements of clinical course and treatment response related to specific biomarkers. It is hoped that these studies will provide substantive insights into the causes of, and effective treatments for, autism. Autism is a serious and debilitating disorder. While there is strong evidence supporting a significant genetic component to the disorder, the identification of specific susceptibility genes has been difficult. Identification of susceptibility genes through the approaches proposed could provide important insights into biological basis of this illness, which could result in the development of novel treatments.