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: 9243127
• 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 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9243127
• 关键词: Adopted; Affect; Alcohol dependence; Area; Atlases; Bipolar Disorder; Brain; Brain Diseases; Brain imaging; Brain region; Classification; Clinical Medicine; Data; Data Set; Databases; Dimensions; Disease; Future; Gene Expression; Genes; Genetic; Genetic Determinism; Genetic study; Genotype; Heritability; Human; Individual; Knowledge; Label; Lead; MRI Scans; Magnetic Resonance Imaging; Maps; Measures; Mental disorders; Methods; Modeling; Nicotine Dependence; Pathogenesis; Pathway interactions; Patient risk; Patients; Pattern; Performance; Phenotype; Procedures; Risk; Sampling; Schizophrenia; Single Nucleotide Polymorphism; Statistical Methods; Structure; Substance Addiction; Substance Use Disorder; Surface; Symptoms; System; Testing; Thick; Twin Multiple Birth; Variant; Yang; base; brain behavior; clinical Diagnosis; cortex mapping; disorder risk; gene conservation; gene discovery; genetic association; genetic variant; genome wide association study; human disease; imaging biomarker; insight; neurotransmission; new therapeutic target; novel; novel strategies; patient stratification; pleiotropism; predictive modeling; public health relevance; 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，我们基于双胞胎的磁共振成像（MRI）数据，使用模糊聚类产生了人类大脑皮层的第一个遗传图谱。这一发现不仅证实了人类大脑表型是可遗传的特征，而且还证明了一个非常清晰的区域特异性遗传模式，其中皮质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，并将其应用到一个独特的大型内部样本与MRI和单核苷酸多态性（SNP）数据，以产生基于SNP的图谱。我们将采用Visscher及其同事开发的统计框架（Yang et al. 2010，Deary et al. 2012）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建模，其检查对性状的总遗传影响，并且比传统的全基因关联研究（GWAS）方法捕获更大部分的遗传力。然而，无论是双胞胎还是Visscher方法都不能提供关于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SNP，以建立一个数据库工具，该工具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，精神和物质使用障碍会影响大脑和行为。我们将研究参与疾病或症状发病机制的遗传变异和机制，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：基于MRI和SNP数据生成人类大脑皮层的遗传图谱。我们将采取一种新的方法来实现由Yang等人开发的方法，其优势在于估计所有SNP的总遗传效应，通过模糊聚类生成皮质表面积和厚度的遗传图谱。目的2：发现与皮质聚类图和个体皮质下结构的个体遗传划分相关的SNPs。我们将把大脑皮层分成由双胞胎基因图谱定义的基因大脑区域，并找到与每个大脑区域相关的SNP或基因。目标3：确定精神和物质使用障碍的遗传基础，重点是与神经系统相关的SNP。与脑表型相关的SNPs的信息将进一步应用于提高我们在精神病患者中识别疾病相关SNPs的能力。 疾病（包括精神分裂症、双相和单相疾病）和物质依赖（包括酒精和尼古丁依赖）。

项目成果

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia.

• DOI: 10.1176/appi.ajp.2017.17040410
• 发表时间: 2018-07-01
• 期刊: The American journal of psychiatry
• 作者: Kauppi K;Rosenthal SB;Lo MT;Sanyal N;Jiang M;Abagyan R;McEvoy LK;Andreassen OA;Chen CH
• 通讯作者: Chen CH