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Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

机译:基于多域证据的优先排序算法和神经发育假说选择的167个精神分裂症候选基因的关联研究

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摘要

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to thisranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.
机译:整合来自多个域的证据有助于确定疾病候选基因的优先级,以便进行后续测试。我们使用三种不同的证据域对爱尔兰高密度精神分裂症家族研究中所有已知的人类基因(n = 3819)的连锁峰进行了排序:1)使用Stanley Brain收集的微阵列基因表达结果的荟萃分析,2)a精神分裂症蛋白质-蛋白质相互作用网络,以及3)系统文献检索。每个基因都被赋予一个域特定的p值,并在评估每个域中的证据后进行排名。为了与该排序过程进行比较,还通过包含具有与神经发育相关的基因本体学术语的基因来测试了大规模候选基因假设。随后,使用来自定制Illumina iSelect阵列的167个基因中的3725个SNP的基因型来评估排名最高的假设假设基因。共有73个基因被高度排名并参与了神经发育(类别1),而42和52个基因分别被神经发育(类别2)或高度排名(类别3)排斥。在基因PRKG1,PRKCE和CNTN4中观察到最显着的关联,但在多次测试校正后,没有单个SNP显着。方法的比较表明,使用假设驱动的神经发育类别进行了大量的重要检验。从两项独立的精神分裂症全基因组关联研究(GWAS)中随机选择大小相似的基因,结果表明偶然发生这种现象的可能性不大。在对三个GWAS数据集的进一步荟萃分析中,四个候选SNP达到了标称意义。尽管在当前实验中,使用先验信息的综合来源进行基因排名并不能获得明显的结果,但是使用先验假设(神经发育)进行的基因选择要优于随机选择。这样,使用更仔细选择的信息源就可以进一步发展基因分级策略。

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