Computational Discovery of Synergistic Mechanisms Responsible for Psychiatric Dis

描述（由申请人提供）：精神疾病的生物学机制在很大程度上是未知的。鉴于鉴定赋予遗传变异的显著个体风险的成功有限，认为发现反映复杂途径中的分子元件的多种遗传变异之间的负责任上位相互作用将阐明新的疾病机制。我们将通过我们的计算和遗传实验室之间的合作进行旨在发现这种机制的研究。我们将开发基于系统的计算和视觉工具来发现这种相互作用，并将其应用于两种疾病的临床可用和内部全基因组关联数据：精神分裂症和双相情感障碍。我们将验证我们的结果的统计意义，并在独立数据上进行计算机模拟。我们的计算方法将被设计为分析单核苷酸多态性（SNP）以及拷贝数变异（CNVs），使用遗传变异对中固有的协同作用的定量测量，表明可能的联合参与途径。我们将从生物学上解释由此产生的计算输出，并试图从遗传学上验证所确定的相互作用。如果由此产生的涉及两个基因的生物学假设被认为是有希望的，我们将使用体外神经生物学实验来测试这些假设。如果有足够的证据支持生物上位性的可能性，我们将最终产生一个或两个基因在一个相互作用对的转基因动物模型，在可行的情况下，使用遗传学或药理学方法，旨在确认生物相互作用和解剖潜在的机制基础。我们提出的研究与公共卫生的相关性证明了它的潜力，以提高我们对精神分裂症和双相情感障碍的病因机制的理解。反过来，这些发现将有助于为这些疾病开发急需的诊断和治疗方法。

DESCRIPTION (provided by applicant): The biological mechanisms responsible for psychiatric disorders are largely unknown. Given the limited success of identifying significant individual risk conferring genetic variants, it is believed that discovery of responsible epistatic interactions among multiple genetic variants reflecting molecular elements in complex pathways will elucidate novel disease mechanisms. We will perform our research aimed at discovering such mechanisms through collaboration between our computational and genetic laboratories. We will develop systems-based computational and visual tools to discover such interactions and apply them to publically available as well as in-house genome-wide association data for two diseases: schizophrenia and bipolar disorder. We will validate the statistical significance of our results and replicate them in silico on independent data. Our computational methodology will be designed to analyze both single nucleotide polymorphisms (SNPs) as well as copy number variations (CNVs) using quantitative measures of the synergy inherent in pairs of genetic variants indicating possible joint involvement in pathways. We will biologically interpret the resulting computational outputs and attempt to genetically validate the identified interactions. If the resulting biological hypotheses involving two genes are deemed promising, we will test those using in vitro neurobiological experiments. If enough evidence is accrued to support the possibility of biological epistasis, we will eventually generate transgenic animal models for either one or both genes in an interacting pair using genetic or pharmacological approaches when feasible, designed to confirm a biological interaction and dissect the underlying mechanistic basis. The relevance of our proposed research to public health is evidenced by its potential to enhance our understanding of etiological mechanisms responsible for schizophrenia and bipolar disorder. In turn, these discoveries will be helpful for the development of highly needed diagnostic and therapeutic methods for these diseases.