Identifying the Polygenic Basis of the Human Brain and Psychiatric Disorders

识别人脑和精神疾病的多基因基础

• 批准号: 8690981
• 负责人: Chi-Hua Chen
• 金额: $ 41.37万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8690981
• 关键词: Adopted; Affect; Alcohol dependence; Area; Atlases; Base of the Brain; Bipolar Disorder; Brain; Brain Diseases; Brain Part; Brain imaging; Brain region; Chromosome Mapping; Classification; Clinical Medicine; Data; Data Set; Databases; Dimensions; Disease; Future; Gene Expression; Genes; Genetic; Genetic Determinism; Genotype; Health; Heritability; Housing; Human; Individual; Knowledge; Label; Lead; Magnetic Resonance Imaging; Maps; Measures; Mental disorders; Methods; Modeling; Nicotine Dependence; Pathogenesis; Pathway interactions; Patients; Pattern; Performance; Phenotype; Procedures; Risk; Sampling; Schizophrenia; Single Nucleotide Polymorphism; Statistical Methods; Stratification; Structure; Substance Addiction; Substance Use Disorder; Surface; Symptoms; System; Testing; Thick; Twin Multiple Birth; Variant; base; brain behavior; clinical Diagnosis; disorder risk; gene conservation; gene discovery; genetic association; genetic variant; genome wide association study; human disease; insight; neurotransmission; new therapeutic target; novel; novel strategies; pleiotropism; predictive modeling; relating to nervous system; tool; trait

DESCRIPTION (provided by applicant): Recently we have produced the first genetic atlas of the human cortex based on magnetic resonance imaging (MRI) data of twins using fuzzy clustering. This finding not only confirms that human brain phenotypes are heritable traits but also demonstrates a very clear region-specific genetic pattern in which the cortex can be subdivided into various pleiotropic regions. In this project, we will extend this approach further and apply it to a uniquely large in-house sample with both MRI and single nucleotide polymorphism (SNP) data, in order to generate SNP-based atlases. We will adopt a statistical framework developed by Visscher and colleagues (Yang et al. 2010, Deary et al. 2012) to examine the contribution of all SNPs to phenotypic variation in aggregate. This approach is analogous to twin modeling which examines aggregate genetic influences on a trait, and captures a much larger portion of heritability than the conventional gene-wide association study (GWAS) approach. However, neither the twin nor the Visscher methods is informative about specific genetic variants underlying each genetic division. Next, we will take the genetic maps and find SNPs associated with each genetic division using new statistical methods to establish a database tool that has information about contribution of specific genetic variants to phenotypic variation in different parts of the brain. Finally, it is well-recognized that psychiatric and substance use disorders affect both brain and behavior. We will study the genetic variants and mechanisms that are involved in disease or symptom pathogenesis with benefit from incorporating the knowledge of polygenic basis of the brain. The Specific Aims of the proposal are: Aim 1: Generate genetic atlases of the human cortex based on MRI and SNP data. We will take a novel approach of implementing a method developed by Yang et al, with its strength for estimating aggregate genetic effects of all SNPs, to generate genetic maps of cortical surface area and thickness by fuzzy clustering. Aim 2: Discover SNPs associated with individual genetic divisions of the cortical cluster maps and individual subcortical structures. We will parcel the cortex into genetic brain regions defined by the twin-based genetic atlases and find SNPs or genes associated with each brain region. Aim 3: Identify genetic basis of psychiatric and substance use disorders with a focus on SNPs associated with neural systems implicated in the disorders. The information regarding SNPs associated with brain phenotypes will be further applied to enhance our ability in identifying disease- related SNPs in individuals with psychiatric disorders (including schizophrenia, and bipolar and unipolar disorders) and substance dependence (including alcohol and nicotine dependence).

描述（由申请人提供）：最近，我们使用模糊聚类基于双胞胎的磁共振成像（MRI）数据制作了第一个人类皮质遗传图谱。这一发现不仅证实了人类大脑表型是可遗传的特征，而且还证明了一种非常清晰的区域特异性遗传模式，其中皮层可以细分为各种多效性区域。在这个项目中，我们将进一步扩展这种方法，并将其应用于具有 MRI 和单核苷酸多态性 (SNP) 数据的独特大型内部样本，以生成基于 SNP 的图谱。我们将采用 Visscher 及其同事开发的统计框架（Yang 等人，2010 年；Deary 等人，2012 年）来检查所有 SNP 对表型变异的总体贡献。这种方法类似于双胞胎模型，它检查对性状的总体遗传影响，并比传统的全基因关联研究（GWAS）方法捕获更大的遗传力部分。然而，双胞胎方法和维歇尔方法都无法提供有关每个遗传分裂背后的特定遗传变异的信息。接下来，我们将使用新的统计方法获取遗传图谱并找到与每个遗传分区相关的单核苷酸多态性（SNP），以建立一个数据库工具，其中包含有关特定遗传变异对大脑不同部位表型变异的贡献的信息。最后，众所周知，精神疾病和物质使用障碍会影响大脑和行为。我们将研究与疾病或症状发病机制有关的遗传变异和机制，并结合大脑多基因基础的知识。该提案的具体目标是： 目标 1：根据 MRI 和 SNP 数据生成人类皮质的遗传图谱。我们将采用一种新颖的方法来实施由 Yang 等人开发的方法，该方法具有估计所有 SNP 的总体遗传效应的优势，通过模糊聚类生成皮质表面积和厚度的遗传图。目标 2：发现与皮质簇图和个体皮质下结构的个体遗传划分相关的 SNP。我们将把皮质划分为由基于双胞胎的遗传图谱定义的遗传大脑区域，并找到与每个大脑区域相关的 SNP 或基因。目标 3：确定精神疾病和药物滥用疾病的遗传基础，重点关注与疾病相关的神经系统相关的 SNP。有关与大脑表型相关的 SNP 的信息将进一步应用于增强我们识别精神疾病个体中与疾病相关的 SNP 的能力 疾病（包括精神分裂症、双向情感障碍和单相情感障碍）和物质依赖（包括酒精和尼古丁依赖）。