Gene expression in the human brain

人脑中的基因表达

• 批准号: 8736661
• 负责人: Mark Cookson
• 金额: $ 25.35万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8736661
• 关键词: Affect; Brain; Code; DNA; Data; Data Set; Development; Disease; Exons; Gene Expression; Generations; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Goals; Human; Human Genome; Laboratories; Measures; Messenger RNA; MicroRNAs; Mus; Neurologic; Proteins; Quantitative Trait Loci; RNA Splicing; Reporting; Research; Series; Techniques; Work; base; genome wide association study; interest; mouse model; transcriptome sequencing

We have previously reported the generation of a large, integrated dataset whereby we, and other groups, can examine the relationships between genetic diversity and gene expression in the human brain. This has most immediate impact for understanding gene variants identified in genome-wide association studies where most nominated polymorphisms cannot immediately be assigned a function, as most do not change protein coding sequences. Rather, many are associated with differences in gene expression. We and others have used our data to derive such expression quantitative trait loci (eQTL) and have found them to be very helpful in understanding the genetic basis of a number of neurological and psychiatric conditions. However, our current dataset was generated using microarrays, which is a probe-based technique for estimating gene expression levels. Some of the known limitations of microarrays include that probes have only a single sequence whereas in the human genome, many genes are variable. Also, genes are alternatively spliced and edited which are poorly represented on most arrays. To overcome this, we are currently replacing our microarray based dataset with RNA-Seq, a newer technique that directly sequences expressed genes as well as providing measures of alternate exon using (ie splicing). We first applied this technique, and associated analytical approaches, to the mouse brain where we found substantial changes in gene expression, splicing and editing during development. Ongoing work in the laboratory includes applying the same approach to mouse models of disease and to a large series of human brains whose DNA has also been sequenced.

我们之前已经报道了一个大型综合数据集的生成，我们和其他小组可以借此研究人类大脑中遗传多样性和基因表达之间的关系。这对于理解全基因组关联研究中鉴定的基因变异具有最直接的影响，其中大多数提名的多态性不能立即被分配功能，因为大多数不改变蛋白质编码序列。相反，许多与基因表达的差异有关。我们和其他人已经使用我们的数据来获得这样的表达数量性状基因座（eQTL），并发现它们对理解许多神经和精神疾病的遗传基础非常有帮助。 然而，我们目前的数据集是使用微阵列生成的，这是一种基于探针的技术，用于估计基因表达水平。微阵列的一些已知限制包括探针只有单一序列，而在人类基因组中，许多基因是可变的。此外，基因是选择性剪接和编辑的，这在大多数阵列上表现不佳。为了克服这一点，我们目前正在用RNA-Seq取代我们的基于微阵列的数据集，RNA-Seq是一种新的技术，可以直接对表达的基因进行测序，并提供使用（即剪接）的交替外显子的测量。我们首先将这种技术和相关的分析方法应用于小鼠大脑，在那里我们发现了发育过程中基因表达、剪接和编辑的实质性变化。实验室正在进行的工作包括将相同的方法应用于小鼠疾病模型和大量的人类大脑，这些大脑的DNA也已被测序。