Combined effects of SNPs and CNVs on brain structure in patients with schizophre

尽管精神分裂症(SZ)和双相情感障碍(BP)是两种不同的诊断，但它们都有精神症状， 慢性病程和认知缺陷，以及来自临床、流行病学和 基因方面的发现。解释这种关系的潜在机制(S)还远不清楚。这个项目是 旨在提高对遗传易感性影响大脑结构的途径的理解 通过结合遗传和神经成像的进展，与临床症状相关的异常 技巧。我们提出了一项分层研究，它将利用大样本基因组分析，并具体 遗传神经成像关联分析。首先，我们将使用非常大的单核苷酸样本 精神科基因组学联盟(PGC)SZ和BP基因的多态性和拷贝数变异 患者，以便可靠地识别候选染色体区域。在候选区域内，我们将 应用多变量成像-遗传学方法提取与大脑直接相关的特定遗传因子 使用当地收集的样本，将患者与对照组区分开来的结构性网络。灰质 将分析大脑网络中的集中度和白质完整性，以确定它们与 基因图谱和病人诊断。通过比较SZ、BP和健康对照组的这种关联性 小组，我们将能够确定共同和独特的神经病理机制背后的这两个 精神错乱。这些遗传和神经特征可以作为完善患者分类的有效生物标记物。

Albeit being two separate diagnoses, schizophrenia (SZ) and bipolar disorder (BP) share psychotic symptoms, chronic courses and cognitive deficits, as well as genetic determinants from clinical, epidemiological and genetic findings. The underlying mechanism(s) accounting for such a relation is far from clear. This project is designed to improve the understanding of pathways by which genetic predisposition influences brain structure abnormalities associated with clinical symptoms, through combining the advances of genetic and neuroimaging techniques. We propose a hierarchical study that will leverage a large-sample genomic analysis, and specific genetic neuroimaging association analyses. First, we will use a very large sample of single nucleotide polymorphisms and copy number variations from the Psychiatric Genomics Consortium (PGC) SZ and BP patients in order to reliably identify the candidate chromosome regions. Within the candidate regions we will apply multivariate imaging-genetic methods to extract specific genetic factors directly associated with brain structural networks that differentiate patients from controls, using a locally collected sample. Gray matter concentration and white matter integrity within brain networks will be analyzed for their associations with both genetic profiles and patient diagnoses. Through comparison of such associations in SZ, BP and healthy control groups, we will be able to identify common and unique neuropathological mechanisms underlying the two disorders. Such genetic and neural features can act as effective biomarkers for refined patient categorization.