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

描述（由申请人提供）：精神分裂症是一种高度遗传的神经精神疾病，具有显著的公共卫生成本。了解遗传变异在疾病中的作用将有助于确定更好的预后和治疗反应的预测因子。目前的研究正在使用全基因组扫描（GWS）方法来确定可能在精神分裂症中发挥作用的许多基因-无论是增加整体疾病的风险，还是通过调节各种临床症状。结构性神经影像学测量表明精神分裂症患者存在灰质损失;精神分裂症患者的脑室往往比健康对照组更大，灰质体积更小，我们和其他人已经确定了内侧额叶、颞叶和岛叶脑回的区域性损失。识别 这个项目的目标是研究这些灰质损失模式背后的遗传影响。我们提出了一个多元的方法来分析已经存在的GWS数据和体素措施的灰质密度。我们将应用并行伊卡，参考;该技术识别大脑结构中的空间变化模式和基因型模式， 有联系我们开始与3200结构成像和GWS样本健康对照组和精神分裂症患者，从聚集的遗留数据。我们约束的成像和遗传分析与参考向量纳入先验信息。在目标1中，我们将使用基于源的形态测量方法开发初始的先验空间模式和结构网络;在目标2中，我们将在独立的家系样本中确定这些网络的遗传力和数量性状基因座;在目标3中，我们计划使用数量性状基因座作为遗传的先验约束。 数据，和遗传结构网络作为我们的并行伊卡分析的成像数据的约束。使用这些方法，我们将确定的空间模式和遗传档案，我们的样本内的协变，并显示在精神分裂症和控制数据的诊断效果。我们包括一个复制的分半分析和后续高密度基因分型计划。这种方法确定了大脑内的结构网络，允许年龄，药物暴露和其他措施的变化，并将它们与遗传组合联系起来。最终结果将是影响疾病中结构性脑损失模式的基因型谱的组合。开发的方法将允许复杂的成像和GWS数据进行组合分析，可能适用于许多疾病。

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.