Suppr超能文献

采用多领域基于证据的优先级排序算法和神经发育假说选择的精神分裂症 167 个候选基因的关联研究。

Association study of 167 candidate genes for schizophrenia selected by a multi-domain evidence-based prioritization algorithm and neurodevelopmental hypothesis.

机构信息

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

出版信息

PLoS One. 2013 Jul 29;8(7):e67776. doi: 10.1371/journal.pone.0067776. Print 2013.

Abstract

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 this ranking 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.

摘要

整合来自多个领域的证据有助于优先考虑候选疾病基因,以便进行后续测试。我们使用三种不同的证据领域,对爱尔兰高密度精神分裂症家族研究中的连锁峰下的所有已知人类基因(n=3819)进行了排名:1)使用 Stanley Brain 集合进行微阵列基因表达结果的荟萃分析,2)精神分裂症蛋白质-蛋白质相互作用网络,以及 3)系统文献搜索。为每个基因分配了一个特定于域的 p 值,并在评估每个域内的证据后进行了排名。为了与该排名过程进行比较,还通过包括与神经发育相关的基因本体术语的基因,对大规模候选基因假设进行了测试。随后,使用定制的 Illumina iSelect 阵列中的 167 个基因的 3725 个 SNP 基因型来评估排名最高的基因与假设选择的基因。有 73 个基因排名较高且与神经发育有关(类别 1),而 42 个和 52 个基因分别仅与神经发育(类别 2)或排名较高(类别 3)有关。在 PRKG1、PRKCE 和 CNTN4 基因中观察到最显著的关联,但经过多次测试校正后,没有单个 SNP 具有统计学意义。两种方法的比较表明,使用假设驱动的神经发育类别进行的显著测试过多。从两项独立的精神分裂症全基因组关联研究(GWAS)中随机选择相似大小的基因表明,这种过量不太可能是偶然的。在对三个 GWAS 数据集的进一步荟萃分析中,有四个候选 SNP 达到了名义显著性。尽管使用整合的先验信息源进行基因排名并未增加当前实验中的显著结果,但使用先验假设(神经发育)进行基因选择优于随机选择。因此,使用更精心选择的信息源进一步开发基因排名策略是合理的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e21/3726675/b68b297e3227/pone.0067776.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验