Systematic characterisation of genetically influenced 'omics' phenotypes and disease modules within biological networks

生物网络中受遗传影响的“组学”表型和疾病模块的系统表征

• 批准号: MR/S003746/1
• 负责人: Praveen Surendran
• 金额: $ 41.89万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS003746%2F1
• 关键词: Systematic; characterisation; genetically; influenced; omics

Introduction: Identification of biological pathways associated with diseases and functional characterisation of changes that perturb biological processes is the key to understanding disease aetiology, prognosis and prevention. Recent studies have been successful in identifying the association of genetic variants and potentially causal genes with various proteins, metabolites and lipids and their influence on key biological pathways that are associated with diseases [1-6]. In addition, there is increasing evidence of genetic overlap between unrelated diseases and traits that point to a shared aetiology of diseases [7-8]. The aim of the proposed project is to understand the shared cause of diseases by combining multi-omics (proteins, metabolites and lipids) phenotype data, genetic association with multi-omics phenotypes and diseases and electronic health records (EHR) within the INTERVAL Bioresource. Background and Aim: Analyses of the vast amount of data from these high dimensional analyses is often difficult without constructing an interactive biological network. Gaussian Graphical Modelling (GGM) allows the construction of a biological (omics) network, and an automated feature detection algorithm will enable the extraction of disease modules from this network. Here disease module refers to a set of proteins/lipids/metabolites and associated pathways that are picked by the algorithm as associated with diseases (eg. CHD, Type II Diabetes etc). Further functional characterisation and computational follow-up of these disease modules using clinical measures in EHR will lead to the identification of novel genes and pathways associated with diseases. This will also improve our understanding of the shared cause of diseases. ie. if there's a change in the gene that's associated with reduced risk of CHD and T2D but an increase in Asthma. The project will primarily focus on 1. Developing an interactive web resource that will allow the investigation of phenotype data and genetic association with the phenotypes measured within the INTERVAL bioresource. 2. Developing a supervised learning method to identify disease modules within the biological network 3. Investigate disease modules and underlying biological pathways using electronic health records (EHR) to understand shared aetiology of diseases Methods: The proposed work will focus on constructing GGMs for the multi-omics data (proteins, metabolites and lipids) with edges representing the partial correlation between two phenotype measures conditioned for other variables within the model. Meta-data which include genetic associations with multi-omics phenotypes from Genome-Wide Association Studies (GWAS), genetic association with diseases from public databases (PhenoScanner, SNiPA etc.) and biological pathways (KEGG) will be added to the network to allow the identification of molecular pathways and disease modules using supervised learning methods. This will be particularly useful as an automated tool to detect disease modules within a biological network and inform mendelian randomisation studies (MR) to understand the shared aetiology of diseases. These genetically influenced disease modules can be tested for association across Electronic Health Record (HER) phenotypes (eg. Diagnosis, presence or absence of multi-morbidity and drug responses). This agnostic approach will provide insights into the influence of perturbations within the omics network on medical phenome and identify key omics phenotypes and pathways that are shared by diseases thereby providing candidate targets for therapeutic intervention.References: 1. Shin, S.-Y. et al. Nat. Genet. (2014) 2. Long, T. et al. Nat. Genet. (2017) 3. Kettunen, J. et al. Nat. Commun. (2016) 4. Suhre, K. et al. Nat. Commun. (2017) 5. Suhre, K. et al. Nature (2011) 6. Draisma, H. H. M. et al. Nat. Commun. (2015) 7. Sarwar et.al. Lancet. (2012) 8. Ferreira et.al PLoS Genetics (2013)

简介：识别与疾病相关的生物学途径和扰乱生物学过程的变化的功能特征是理解疾病病因、预后和预防的关键。最近的研究已经成功地确定了遗传变异和潜在致病基因与各种蛋白质，代谢物和脂质的关联及其对与疾病相关的关键生物学途径的影响[1-6]。此外，越来越多的证据表明，不相关的疾病和性状之间存在遗传重叠，这些遗传重叠表明疾病的共同病因[7-8]。拟议项目的目的是通过将多组学（蛋白质，代谢物和脂质）表型数据，与多组学表型和疾病的遗传关联以及INTERVAL Bioresource中的电子健康记录（EHR）相结合来了解疾病的共同原因。背景和目的：如果不构建交互式生物网络，对来自这些高维分析的大量数据的分析通常是困难的。高斯图形建模（GGM）允许构建生物（组学）网络，自动特征检测算法将能够从该网络中提取疾病模块。在这里，疾病模块是指一组蛋白质/脂质/代谢物和相关的途径，这些途径被算法选为与疾病相关的（例如，CHD、II型糖尿病等）。使用EHR中的临床措施对这些疾病模块进行进一步的功能表征和计算随访将导致识别与疾病相关的新基因和途径。这也将增进我们对共同的疾病原因的了解。ie.如果有一个基因的变化，与冠心病和T2 D的风险降低，但增加哮喘。该项目将主要集中在1。开发一个交互式网络资源，允许调查表型数据和与INTERVAL生物资源中测量的表型的遗传关联。2.开发监督学习方法以识别生物网络内的疾病模块3.使用电子健康记录（EHR）调查疾病模块和潜在的生物学途径，以了解疾病的共同病因方法：所提出的工作将集中在构建GGM的多组学数据（蛋白质，代谢物和脂质）与边缘代表的两个表型之间的部分相关性的措施条件模型内的其他变量。元数据包括来自全基因组关联研究（GWAS）的多组学表型的遗传关联，来自公共数据库（PhenoScanner，SNiPA等）的疾病遗传关联。和生物途径（KEGG）将被添加到网络中，以允许使用监督学习方法识别分子途径和疾病模块。作为一种自动化工具，这将特别有用，可以检测生物网络内的疾病模块，并为孟德尔随机化研究（MR）提供信息，以了解疾病的共同病因。这些受遗传影响的疾病模块可以测试电子健康记录（HER）表型之间的关联（例如，诊断、是否存在多重发病和药物反应）。这种不可知的方法将提供对组学网络内的扰动对医学表型的影响的见解，并识别疾病共享的关键组学表型和途径，从而为治疗干预提供候选靶点。Shin，S.- Y.等，Nat.Genet.（2014年）2. Long，T.等，Nat.Genet.（2017年）3. Kettunen，J.等，Nat. Commun.（2016）4. Suhre，K.等，Nat.Commun.（2017年）5. Suhre，K.等人Nature（2011）6.德赖斯马，H. H. M.等，Nat.Commun.（2015）7.萨尔瓦尔等人，《柳叶刀》。（2012年）8.费雷拉et.al PLoS Genetics（2013）

项目成果

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use.

外显子组芯片荟萃分析精细绘制因果变异图并阐明吸烟和饮酒中罕见编码变异的遗传结构。

• DOI: 10.17863/cam.35946
• 发表时间: 2019
• 作者: Brazel D

Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis.

LPA变体与冠状动脉疾病的风险及其对脂蛋白（A）较低疗法的影响：Mendelian随机分析的影响。

• DOI: 10.1001/jamacardio.2018.1470
• 发表时间: 2018-07-01
• 期刊: JAMA cardiology
• 影响因子: 24
• 作者: Burgess S;Ference BA;Staley JR;Freitag DF;Mason AM;Nielsen SF;Willeit P;Young R;Surendran P;Karthikeyan S;Bolton TR;Peters JE;Kamstrup PR;Tybjærg-Hansen A;Benn M;Langsted A;Schnohr P;Vedel-Krogh S;Kobylecki CJ;Ford I;Packard C;Trompet S;Jukema JW;Sattar N;Di Angelantonio E;Saleheen D;Howson JMM;Nordestgaard BG;Butterworth AS;Danesh J;European Prospective Investigation Into Cancer and Nutrition–Cardiovascular Disease (EPIC-CVD) Consortium
• 通讯作者: European Prospective Investigation Into Cancer and Nutrition–Cardiovascular Disease (EPIC-CVD) Consortium

Genetically predicted levels of the human plasma proteome and risk of stroke: a Mendelian Randomization study

人类血浆蛋白质组的基因预测水平和中风风险：孟德尔随机研究

• DOI: 10.1101/2021.10.22.21265375
• 发表时间: 2021
• 作者: Chen L

Systematic Mendelian randomization using the human plasma proteome to discover potential therapeutic targets for stroke.

• DOI: 10.1038/s41467-022-33675-1
• 发表时间: 2022-10-17
• 期刊: Nature communications
• 影响因子: 16.6