Methods and tools for structural models integrating multiple high-throughput omics data sets in genetic epidemiology

遗传流行病学中整合多个高通量组学数据集的结构模型的方法和工具

• 批准号: MR/M013138/1
• 负责人: Alexandra Lewin
• 金额: $ 48.6万
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM013138%2F1
• 关键词: Methods; tools; structural; models; integrating

In recent years, new methods for biological measurements using sophisticated technologies have enabled the simultaneous measurement of thousands of potential molecular biomarkers of disease. These biomarkers range from genetic variants which are fixed for each person throughout their life, through gene products such as proteins which are produced dynamically and vary with time and across different cells in the body, to large molecule metabolites which more closely reflect the processes involved in both normal functioning and disease development. Many biomarkers reflect environmental exposures, including lifestyle, occupational and dietary factors, and thus serve to study in a comprehensive way the interaction between genes and environment in relation to disease outcomes.The complexity and size of these data sets render their analysis difficult. Limitations of traditional multi-variate statistical methods have meant that the majority of existing analyses rely on univariate methods, which consider each type of biomarker, and in fact each particular molecule, separately. This means that important information on how different molecules co-vary is lost. Producing robust statistical tools capable of analysing these large-scale data sets coherently is important to ensure the best exploitation of these expensive data. In our project we propose to use structural equation models, which are able to model the relations between several different types of biomarkers and disease pathways in a single model. Traditionally these models have either been used on very small data sets, numbering tens of variables, or on sets of hundreds of variables but all of the same type. We propose to develop structural models which are capable of analysing multiple high-dimensional biomarker data sets together, thus enabling these models to be used on modern epidemiological data sets. The project will take advantage of our recent work in high-dimensional statistical modelling of pairs of molecular biomarker data sets, and extend our advances to the more complex structural models for analysing several biomarker sets together.The methods will be developed with reference to case studies from the North Finnish Birth Cohort, whose Principal Investigator is a co-Investigator on this project. We will also benefit from collaborations with the Airwave Health Monitoring Study and the European Prospective Investigation into Cancer and Nutrition cohort, both of which are hosted at Imperial. The project requires extensive interdisciplinary work, combining expertise in statistics, epidemiology, genetics and computation. In view of their complementary skills and access to data bases, the team of investigators is uniquely placed to successively achieve these objectives.

近年来，使用先进技术进行生物测量的新方法已经能够同时测量数千种潜在的疾病分子生物标志物。这些生物标志物的范围从每个人一生中固定的遗传变异，到动态产生的蛋白质等基因产物，这些蛋白质随时间和体内不同细胞而变化，再到更密切地反映正常功能和疾病发展过程的大分子代谢物。许多生物标志物反映了环境暴露，包括生活方式，职业和饮食因素，因此可以全面研究基因和环境之间的相互作用与疾病结局的关系。这些数据集的复杂性和规模使其分析变得困难。传统多变量统计方法的局限性意味着大多数现有分析依赖于单变量方法，其分别考虑每种类型的生物标志物，实际上是每种特定分子。这意味着关于不同分子如何共同变化的重要信息丢失了。开发能够连贯地分析这些大规模数据集的强大统计工具，对于确保最佳利用这些昂贵的数据至关重要。在我们的项目中，我们建议使用结构方程模型，它能够在单个模型中模拟几种不同类型的生物标志物和疾病途径之间的关系。传统上，这些模型要么用于非常小的数据集，编号为数十个变量，要么用于数百个变量但都是相同类型的集合。我们建议开发能够一起分析多个高维生物标志物数据集的结构模型，从而使这些模型能够用于现代流行病学数据集。该项目将利用我们最近在分子生物标志物数据集对的高维统计建模方面的工作，并将我们的进展扩展到更复杂的结构模型，用于分析多个生物标志物sets.The方法将参考来自北芬兰出生队列的案例研究，其首席研究员是该项目的共同研究员。我们还将受益于与空气波健康监测研究和欧洲癌症和营养队列前瞻性调查的合作，这两个研究项目都在帝国理工学院举办。该项目需要广泛的跨学科工作，结合统计学、流行病学、遗传学和计算方面的专门知识。鉴于调查员小组的技能互补，并能利用数据库，因此他们处于独特的地位，能够成功地实现这些目标。

项目成果

Common maternal and fetal genetic variants show expected polygenic effects on risk of small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies.

• DOI: 10.1371/journal.pgen.1009191
• 发表时间: 2020-12
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Beaumont RN;Kotecha SJ;Wood AR;Knight BA;Sebert S;McCarthy MI;Hattersley AT;Järvelin MR;Timpson NJ;Freathy RM;Kotecha S
• 通讯作者: Kotecha S

Bayesian Variable Selection for Gaussian Copula Regression Models

高斯 Copula 回归模型的贝叶斯变量选择

• DOI: 10.6084/m9.figshare.13143693
• 发表时间: 2020
• 作者: Alexopoulos A

Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?

• DOI: 10.1038/s41366-021-00764-y
• 发表时间: 2021-05
• 期刊: International journal of obesity (2005)
• 作者: Alkaf B;Blakemore AI;Järvelin MR;Lessan N
• 通讯作者: Lessan N

Bayesian Variable Selection for Gaussian copula regression models.

• DOI: 10.1080/10618600.2020.1840997
• 发表时间: 2020-12-10
• 期刊: Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
• 作者: Alexopoulos A;Bottolo L
• 通讯作者: Bottolo L

SERPINA1 methylation and lung function in tobacco-smoke exposed European children and adults: a meta-analysis of ALEC population-based cohorts.

• DOI: 10.1186/s12931-018-0850-8
• 发表时间: 2018-08-22
• 期刊: Respiratory research
• 影响因子: 5.8
• 作者: Beckmeyer-Borowko A;Imboden M;Rezwan FI;Wielscher M;Amaral AFS;Jeong A;Schaffner E;Auvinen J;Sebert S;Karhunen V;Bettschart R;Turk A;Pons M;Stolz D;Kronenberg F;Arathimos R;Sharp GC;Relton C;Henderson AJ;Jarvelin MR;Jarvis D;Holloway JW;Probst-Hensch NM
• 通讯作者: Probst-Hensch NM