• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

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

一种基于共表达网络和 GWAS 数据的核心模块识别的计算方法。

A computational approach for identification of core modules from a co-expression network and GWAS data.

机构信息

Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908 USA.

Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908 USA.

出版信息

STAR Protoc. 2021 Aug 21;2(3):100768. doi: 10.1016/j.xpro.2021.100768. eCollection 2021 Sep 17.

DOI:10.1016/j.xpro.2021.100768
PMID:34467232
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8385446/
Abstract

This protocol describes the application of the "omnigenic" model of the genetic architecture of complex traits to identify novel "core" genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with "core-like" characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020).

摘要

本方案描述了将复杂特征遗传结构的“全能性”模型应用于鉴定影响疾病相关表型的新“核心”基因。核心基因被假设为直接调控疾病,可能作为治疗靶点。本方案利用 GWAS 数据、共表达网络以及包括 GTEx 数据库和国际小鼠表型联盟数据库在内的公开数据,鉴定富含具有“核心样”特征的基因的模块。如需详细了解本方案的使用和执行,请参考 Sabik 等人(2020 年)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/ff0e77903b65/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/8576d7d643a5/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/e394cc94efba/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/46a42f90149d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/ff0e77903b65/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/8576d7d643a5/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/e394cc94efba/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/46a42f90149d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d82/8385446/ff0e77903b65/gr3.jpg

相似文献

1
A computational approach for identification of core modules from a co-expression network and GWAS data.一种基于共表达网络和 GWAS 数据的核心模块识别的计算方法。
STAR Protoc. 2021 Aug 21;2(3):100768. doi: 10.1016/j.xpro.2021.100768. eCollection 2021 Sep 17.
2
FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.FunGraph:一种用于重建复杂性状的全基因多层互作网络的统计协议。
STAR Protoc. 2021 Dec 4;2(4):100985. doi: 10.1016/j.xpro.2021.100985. eCollection 2021 Dec 17.
3
A computational approach to generate highly conserved gene co-expression networks with RNA-seq data.一种基于 RNA-seq 数据生成高度保守的基因共表达网络的计算方法。
STAR Protoc. 2022 Jun 2;3(2):101432. doi: 10.1016/j.xpro.2022.101432. eCollection 2022 Jun 17.
4
Using biological networks to integrate, visualize and analyze genomics data.利用生物网络整合、可视化和分析基因组学数据。
Genet Sel Evol. 2016 Mar 31;48:27. doi: 10.1186/s12711-016-0205-1.
5
Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data.通过共表达网络和 GWAS 数据的整合鉴定骨密度的核心模块。
Cell Rep. 2020 Sep 15;32(11):108145. doi: 10.1016/j.celrep.2020.108145.
6
Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules.基于组织特异性网络的杏仁核影像学表型全基因组研究,以鉴定功能相互作用模块。
Bioinformatics. 2017 Oct 15;33(20):3250-3257. doi: 10.1093/bioinformatics/btx344.
7
Assessment of network module identification across complex diseases.评估复杂疾病中的网络模块识别。
Nat Methods. 2019 Sep;16(9):843-852. doi: 10.1038/s41592-019-0509-5. Epub 2019 Aug 30.
8
Co-expression pan-network reveals genes involved in complex traits within maize pan-genome.共表达泛网络揭示了玉米泛基因组中复杂性状相关的基因。
BMC Plant Biol. 2022 Dec 19;22(1):595. doi: 10.1186/s12870-022-03985-z.
9
Identification of novel susceptibility genes associated with seven autoimmune disorders using whole genome molecular interaction networks.利用全基因组分子相互作用网络鉴定与七种自身免疫性疾病相关的新的易感基因。
J Autoimmun. 2019 Feb;97:48-58. doi: 10.1016/j.jaut.2018.10.002. Epub 2018 Nov 1.
10
Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data.主调控因子活性 QTL 方案,利用 eQTL 数据来暗示可能介导 GWAS 信号的调控途径。
STAR Protoc. 2023 Sep 15;4(3):102362. doi: 10.1016/j.xpro.2023.102362. Epub 2023 Jun 17.

引用本文的文献

1
Co-expression of prepulse inhibition and Schizophrenia genes in the mouse and human brain.小鼠和人类大脑中前脉冲抑制与精神分裂症基因的共表达。
Neurosci Appl. 2024 Jun 7;3:104075. doi: 10.1016/j.nsa.2024.104075. eCollection 2024.
2
Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice.系统遗传学分析揭示了与 CFW 小鼠纹状体行为特征相关的关键基因。
J Neurosci. 2024 Jun 26;44(26):e0252242024. doi: 10.1523/JNEUROSCI.0252-24.2024.

本文引用的文献

1
A compendium of uniformly processed human gene expression and splicing quantitative trait loci.人类基因表达和剪接数量性状位点的综合分析。
Nat Genet. 2021 Sep;53(9):1290-1299. doi: 10.1038/s41588-021-00924-w. Epub 2021 Sep 6.
2
Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data.通过共表达网络和 GWAS 数据的整合鉴定骨密度的核心模块。
Cell Rep. 2020 Sep 15;32(11):108145. doi: 10.1016/j.celrep.2020.108145.
3
The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.
NHGRI-EBI GWAS Catalog 于 2019 年发布的已发表全基因组关联研究、靶向基因芯片和汇总统计数据
Nucleic Acids Res. 2019 Jan 8;47(D1):D1005-D1012. doi: 10.1093/nar/gky1120.
4
The UCSC Genome Browser database: 2019 update.UCSC 基因组浏览器数据库:2019 年更新。
Nucleic Acids Res. 2019 Jan 8;47(D1):D853-D858. doi: 10.1093/nar/gky1095.
5
From genome-wide associations to candidate causal variants by statistical fine-mapping.从全基因组关联研究到通过统计精细映射确定候选因果变异。
Nat Rev Genet. 2018 Aug;19(8):491-504. doi: 10.1038/s41576-018-0016-z.
6
Screening Gene Knockout Mice for Variation in Bone Mass: Analysis by μCT and Histomorphometry.筛选骨量变化的基因敲除小鼠:μCT 和组织形态计量学分析。
Curr Osteoporos Rep. 2018 Apr;16(2):77-94. doi: 10.1007/s11914-018-0421-4.
7
Genetic effects on gene expression across human tissues.基因对人体各组织基因表达的影响。
Nature. 2017 Oct 11;550(7675):204-213. doi: 10.1038/nature24277.
8
eDGAR: a database of Disease-Gene Associations with annotated Relationships among genes.eDGAR:一个具有基因间注释关系的疾病-基因关联数据库。
BMC Genomics. 2017 Aug 11;18(Suppl 5):554. doi: 10.1186/s12864-017-3911-3.
9
An Expanded View of Complex Traits: From Polygenic to Omnigenic.复杂性状的扩展观点:从多基因到泛基因
Cell. 2017 Jun 15;169(7):1177-1186. doi: 10.1016/j.cell.2017.05.038.
10
Integrating GWAS and Co-expression Network Data Identifies Bone Mineral Density Genes SPTBN1 and MARK3 and an Osteoblast Functional Module.整合 GWAS 和共表达网络数据鉴定出与骨密度相关的 SPTBN1 和 MARK3 基因以及成骨细胞功能模块。
Cell Syst. 2017 Jan 25;4(1):46-59.e4. doi: 10.1016/j.cels.2016.10.014. Epub 2016 Nov 17.