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）生物标志物的发现和验证以及特定的挑战主题03- mh -101*：精神障碍中的生物标志物。本提案的总体目标是促进自闭症及相关疾病的行为和遗传生物标志物的发展。虽然很明显，自闭症具有很强的遗传成分，但大规模的基因研究仅依赖于自闭症（谱系）障碍的一般诊断（或仅依赖于受影响个体的其他信息），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，尤其是语言能力，已经成功地用于增加基因定位的能力，但迄今为止的研究只关注自闭症患者的详细行为评估，而不是他们的家庭成员，尽管有大量的文献定义了家庭成员中相关表型的增加率。这个关键的缺口将由我们的项目来填补。我们将使用现有的家庭集和来自所有家庭成员的临床和遗传数据的广泛现有数据库，其中每个家庭至少包含一个患有自闭症的先证者和至少一个患有语言缺陷的先证者，来定义风险的生物标志物。针对目标1，行为生物标志物开发，我们将开发一套自闭症及相关疾病遗传风险的行为生物标志物。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效应，并进一步检查潜在的类别结构，以减少数据和减少测量误差。我们将对一部分研究参与者进行随访评估，以确定所选生物标志物的纵向稳定性。对于目标2，行为生物标志物验证，我们将通过使用全基因组分析来验证行为生物标志物的遗传成分。我们将使用准贝叶斯后验概率方法对定量和二分类行为生物标志物数据进行分析，以阐明风险的遗传结构，为遗传遗传成分的本质提供证据。对于目标3，遗传生物标志物鉴定，我们将确定与自闭症和相关疾病遗传风险相关的特定DNA变异。我们将从Aim 2中确定的区域内的候选基因中测试常见和罕见变异、snp和CNVs，并评估文献中作为调节风险决定基因网络的潜在因素的其他变异。总的来说，我们计划结合行为和遗传生物标志物的分析来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志物相关的临床过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使人衰弱的疾病。虽然有强有力的证据支持这种疾病的重要遗传成分，但确定特定的易感基因一直很困难。通过提出的方法鉴定易感基因可以为了解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发展。

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.