Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia

挖掘全基因组扫描：精神分裂症结构损失的遗传图谱

• 批准号: 8816132
• 负责人: VINCE D CALHOUN
• 金额: $ 48.45万
• 依托单位: THE MIND RESEARCH NETWORK
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8816132
• 关键词: Affect; Age; Alleles; Applied Genetics; Biological Markers; Brain; Brain region; Clinical; Complex; Computer Simulation; Copy Number Polymorphism; Data; Data Set; Development; Diagnosis; Disease; Environmental Risk Factor; Extended Family; Family; Family Study; Genes; Genetic; Genome; Genome Scan; Genotype; Goals; Health Care Costs; Heritability; Hippocampus (Brain); Image; Image Analysis; Link; Magnetic Resonance Imaging; Measures; Medial; Mental disorders; Methods; Mining; Mutation; Pathway interactions; Pattern; Pharmaceutical Preparations; Phenotype; Play; Positioning Attribute; Public Health; Quantitative Trait Loci; Research; Role; Sampling; Schizophrenia; Single Nucleotide Polymorphism; Source; Structure; Symptoms; Techniques; Technology; Testing; Variant; base; brain volume; density; disorder risk; follow-up; genetic analysis; genetic linkage analysis; genetic profiling; genome wide association study; gray matter; healthy volunteer; independent component analysis; morphometry; neural circuit; neuroimaging; neuropsychiatry; outcome forecast; treatment response; vector

DESCRIPTION (provided by applicant): Schizophrenia is a highly heritable neuropsychiatric disorder with significant public health costs. Understanding the contributions of genetic variability in the disease will help identify better predictors of prognosis and treatment response Current studies are using genome wide scan (GWS) approaches to identify the numerous genes which might play a role in schizophrenia-either in increased risk for the disorder overall, or through modulating the various clinical symptoms. Structural neuroimaging measures implicate gray matter loss in schizophrenia; subjects with schizophrenia tend to have larger ventricles and smaller grey matter volumes than do their healthy counterparts, and regional loss in the medial frontal, temporal and insular gyri have been identified by us and others. Identifying the genetic influences underlying these patterns of gray matter loss is the goal of this project. We propose a multivariate method for analyzing already existing GWS data and voxelwise measures of gray matter density. We will apply parallel ICA, with reference; this technique identifies patterns of spatial variation in the brain structure and patterns of genotypes which are linked. We begin with 3200 structural imaging and GWS samples from healthy controls and schizophrenics, from aggregated legacy data. We constrain the imaging and genetic analyses with reference vectors to incorporate a priori information. In Aim 1 we will develop initial a prioi spatial patterns, structural networks using source- based morphometry methods; in Aim 2 we will determine the heritability and quantitative trait loci for these networks in independent famil samples; in Aim 3 we plan to use the quantitative trait loci as a priori constraints on the genetic data, and the heritable structural networks as constraints on the imaging data on our parallel ICA analysis. Using these methods, we will determine the spatial patterns and genetic profiles that covary within our sample, and which show effects of diagnosis in schizophrenic and control data. We include a split-half analysis for replication and a follow-up high-density genotyping plan. This approach identifies structural networks within the brain allowing for variation in age, medication exposure, and other measures, and links them to genetic combinations. The final results will be the combinations of genotypic profiles which influence the patterns of structural brain loss in the disease. The methods developed will allow complex imaging and GWS data to be analyzed in combination, potentially applicable to many disorders.

描述（由申请人提供）：精神分裂症是一种高度可遗传的神经精神疾病，具有巨大的公共卫生成本。了解遗传变异性在疾病中的贡献将有助于确定预后的更好预测指标，治疗反应当前的研究使用基因组范围扫描（GWS）方法来识别可能在精神分裂症中起作用的众多基因 - 在整体上增加了对疾病的风险，或通过调节各种临床症状。结构性神经影像措施暗示精神分裂症中的灰质丧失；与健康的受试者相比，精神分裂症的受试者往往具有更大的心室和较小的灰质体积，并且已经确定了我们和其他人的内侧额叶，时间和岛状回合的区域损失。识别 这些灰质损失模式的遗传影响是该项目的目标。我们提出了一种多元方法，用于分析已经现有的GWS数据和灰质密度的紫ogs。我们将使用并行的ICA进行参考；该技术确定了大脑结构的空间变化模式和基因型的模式 链接。我们从来自健康对照组和精神分裂症患者的3200个结构成像和GWS样品开始，来自汇总的遗产数据。我们用参考向量约束成像和遗传分析以纳入先验信息。在AIM 1中，我们将使用基于源的形态计量学方法开发初始的PRIOI空间模式，结构网络；在AIM 2中，我们将确定独立家庭样本中这些网络的遗传力和定量性状基因座；在AIM 3中，我们计划将定量性状基因座用作对遗传的先验约束 数据以及可遗传的结构网络是我们平行ICA分析的成像数据的约束。使用这些方法，我们将确定样本中共变的空间模式和遗传谱，并显示出精神分裂症和对照数据中诊断的影响。我们包括用于复制的半分析和随访高密度基因分型计划。这种方法确定了大脑内的结构网络，从而使年龄，药物暴露和其他措施的变化，并将其与遗传组合联系起来。最终结果将是基因型特征的组合，这些曲线会影响疾病中结构性脑丧失的模式。开发的方法将允许组合分析复杂的成像和GWS数据，可能适用于许多疾病。