Investigating the mechanisms underlying disease using multiOmics data

使用多组学数据研究疾病的机制

• 批准号: 2259226
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2259226
• 关键词: Investigating; mechanisms; underlying; disease; using

Advances in genotyping technologies and computational capacities have brought new insights into the genetic mechanisms influencing disease. Using genetic markers (SNPs) and health and disease information from many thousands of individuals, Genome-Wide Association Studies (GWAS) have uncovered genetic differences between individuals that cause disease. We also know that epigenetic mechanisms - changes in a chromosome other than DNA sequence changes - play a role in determining disease through regulation of gene activity and expression. DNA methylation is a type of epigenetic modification, sometimes under nuclear genetic control.Generation Scotland (GS) is a cohort of 20,000 individuals with information on many health-related traits including body measures, indicators of cardiovascular and metabolic health and electronic health records. GS participants have information on over 500,000 SNPs, and 5,000 participants have DNA methylation information at around 700,000 locations ('sites') in the genome. Through this DNA methylation dataset that is large, in numbers of individuals and numbers of sites surveyed, compared to published studies, we have an unprecedented insight into the genetics underlying DNA methylation and have discovered that SNPs that control DNA methylation at sites linked to obesity also affect obesity itself. We will now have DNA methylation data on 5000 more individuals from the same cohort.We will use this large scale DNA methylation data, metabolite and protein measures and publicly available GWAS and expression data to get new insights on the genetic and epigenetic mechanisms that control disease. Our project will have 3 main stages:1. We will use the newly available DNA methylation information in GS and combine it with our previous study to further uncover SNPs that affect methylation levels across individuals (mQTLs). We are interested both in mQTLs that control methylation at nearby sites (in 'cis') and more distantly (in 'trans'), as they provide different information on how methylation is controlled.2. We will use the cis-mQTLs from 1 to elucidate the mechanism through which SNPs (mQTLs) affect methylation and disease. For instance: does the SNP affect DNA methylation and, through methylation, the phenotype? Or do SNPs affect the phenotype and that, in turn, changes DNA methylation? Or are both DNA methylation and phenotype affected by the same SNP (or nearby SNPs) but independently of each other? We will answer those questions using Mendelian Randomisation (MR) and reverse MR, our own data and publicly available GWAS results for disease-related phenotypes obtained from very large populations such as UK BioBank. These very large studies allow the detection of SNP-phenotype associations that smaller studies miss, and we will have information on SNP-phenotype associations for over 700 phenotypes of medical relevance.3. To help answer the questions in 2, we will also use publicly available data on gene expression in various tissues (GTEx data), sequence data and proteomics and metabolomics gathered in populations managed in-house at the IGMM.This project tackles active research areas and offers the opportunity to develop new computational and statistical approaches, in particular incorporating new Machine Learning methods.

基因分型技术和计算能力的进步为影响疾病的遗传机制带来了新的见解。全基因组关联研究（GWAS）利用遗传标记（SNPs）和来自数千人的健康和疾病信息，发现了导致疾病的个体之间的遗传差异。我们还知道，表观遗传机制--除了DNA序列变化之外的染色体变化--通过调节基因活性和表达在决定疾病方面发挥作用。DNA甲基化是一种表观遗传修饰，有时受核遗传控制。Generation Scotland（GS）是一个由20，000名个体组成的队列，其中包含许多健康相关特征的信息，包括身体测量，心血管和代谢健康指标以及电子健康记录。GS参与者拥有超过500，000个SNP的信息，5，000名参与者拥有基因组中约700，000个位置（“位点”）的DNA甲基化信息。通过这个庞大的DNA甲基化数据集，与已发表的研究相比，在调查的个体数量和站点数量方面，我们对DNA甲基化的遗传学有了前所未有的了解，并发现控制与肥胖相关的位点的DNA甲基化的SNP也会影响肥胖本身。我们现在将拥有来自同一队列的另外5000名个体的DNA甲基化数据。我们将使用这些大规模DNA甲基化数据、代谢物和蛋白质测量以及公开可用的GWAS和表达数据，以获得有关控制疾病的遗传和表观遗传机制的新见解。我们的项目将有3个主要阶段：1.我们将利用GS中新获得的DNA甲基化信息，并将其与我们先前的研究结合联合收割机，以进一步揭示影响个体间甲基化水平的SNP（mQTL）。我们对控制附近位点（顺式）和更远位点（反式）甲基化的mQTL都感兴趣，因为它们提供了关于甲基化如何控制的不同信息。我们将使用1中的顺式mQTL来阐明SNP（mQTL）影响甲基化和疾病的机制。例如：SNP是否影响DNA甲基化，并通过甲基化影响表型？或者SNP会影响表型，进而改变DNA甲基化吗？或者DNA甲基化和表型都受到相同SNP（或附近SNP）的影响，但彼此独立？我们将使用孟德尔随机化（MR）和反向MR，我们自己的数据和从非常大的人群（如英国生物银行）获得的疾病相关表型的公开GWAS结果来回答这些问题。这些非常大的研究允许检测SNP-表型关联，较小的研究错过了，我们将有超过700个医学相关表型的SNP-表型关联信息。为了帮助回答2中的问题，我们还将使用各种组织中基因表达的公开数据（GTEx数据），序列数据以及在IGMM内部管理的人群中收集的蛋白质组学和代谢组学。该项目涉及活跃的研究领域，并提供开发新的计算和统计方法的机会，特别是结合新的机器学习方法。