NIRG: Taming the skew - empowering Mendelian randomization trials by controlling stratification bias

NIRG：克服偏差 - 通过控制分层偏差增强孟德尔随机化试验的能力

• 批准号: MR/R025126/1
• 负责人: Eran Elhaik
• 金额: $ 54.46万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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%2FR025126%2F1
• 关键词: NIRG; Taming; skew; empowering; Mendelian

BackgroundMendelian randomization (MR) is a tool which uses common genetic variants as surrogates for an exposure of interest to statistically estimate the causal effect of an exposure on a disease. MR is a powerful, rapid, and cheap alternative to randomised control trials, which may be unfeasible to execute. However, MR assumes that there is no confounding between the genetic polymorphism (which is a proxy for the exposure) and the disease outcome. Population Stratification bias occurs due to ancestral differences in the studied individuals, and it can bias Genome-wide Association Studies (GWAS) and MR trials as it violates the assumption of genetic homogeneity. A similar problem exists in two sample MR, which uses summary statistics from two separate sets of studies (one set of studies for the SNP-exposure association and another for the SNP-outcome association) to determine the overall effect estimate. In this scenario it is unlikely that population stratification will be adequately controlled for and thus bias will arise. To continue utilizing Big Data resources, like the UK BioBank (whole genome data for 500,000 people), for MR we must identify and correct the population stratification bias. ObjectivesThis project will develop tools and statistical methods to test, identify, measure, address, and make recommendations about how to handle population stratification bias particularly in MR analyses. However, for that, we first require to develop a unified ancestry model for all human populations. We will then develop tools to estimate ancestry and compare our tools with existing ones. Next, we will develop tools to optimise case-control matches based on ancestry, thus overcoming problems of mismatching case and controls in terms of ancestry, which may bias the results. We will also compare our tools with existing ones using a series of simulated and real databases. Based on the results, we will make recommendation to guide analyses when to use the appropriate methods. We will tailor specific solutions to MR analyses in developing a statistic that measures the bias and measuring the bias in using GWAS summary statistics in MR. Finally, we will apply our methods to study two open questions in science where population stratification may bias efforts, namely: do vitamin D levels affect height and is there a relationships between milk intake (using the LCT gene) and diabetes?Computer code, accompanying guidance notes, and training materials will be made freely available to ensure analysts can easily implement our methods.

孟德尔随机化（MR）是一种工具，它使用常见的遗传变异作为感兴趣的暴露的替代品，以统计估计暴露对疾病的因果效应。核磁共振是一种强大、快速、廉价的替代随机对照试验的方法，而随机对照试验可能无法实施。然而，MR假设遗传多态性（这是暴露的代理）和疾病结果之间没有混淆。群体分层偏倚的发生是由于被研究个体的祖先差异，它可能会使全基因组关联研究（GWAS）和MR试验产生偏倚，因为它违反了遗传同质性假设。在双样本MR中也存在类似的问题，它使用来自两组独立研究的汇总统计数据（一组研究用于snp暴露关联，另一组研究用于snp结局关联）来确定总体效应估计。在这种情况下，人口分层不太可能得到充分控制，因此会产生偏见。为了继续利用大数据资源，如英国生物银行（50万人的全基因组数据），对于MR，我们必须识别和纠正人口分层偏差。本项目将开发工具和统计方法来测试，识别，测量，解决，并就如何处理人口分层偏差提出建议，特别是在MR分析中。然而，为此，我们首先需要为所有人类群体建立一个统一的祖先模型。然后，我们将开发工具来估计祖先，并将我们的工具与现有的工具进行比较。接下来，我们将开发工具来优化基于祖先的病例-对照匹配，从而克服在祖先方面病例和对照不匹配的问题，这可能会使结果产生偏差。我们还将使用一系列模拟和真实的数据库将我们的工具与现有的工具进行比较。根据结果，我们将提出建议，以指导分析何时使用适当的方法。我们将为MR分析量身定制具体的解决方案，以开发一种测量偏差的统计数据，并在MR中使用GWAS汇总统计数据来测量偏差。最后，我们将应用我们的方法来研究人口分层可能导致偏差的两个科学开放问题，即：维生素D水平是否影响身高，牛奶摄入量（使用LCT基因）与糖尿病之间是否存在关系？计算机代码、附带的指导说明和培训材料将免费提供，以确保分析人员能够轻松地实施我们的方法。

项目成果

Additional file 1 of Population genetic considerations for using biobanks as international resources in the pandemic era and beyond

附加文件 1：在大流行时代及以后使用生物样本库作为国际资源的群体遗传考虑因素

• DOI: 10.6084/m9.figshare.14609320
• 发表时间: 2021
• 作者: Carress H

aYChr-DB: a database of ancient human Y haplogroups.

• DOI: 10.1093/nargab/lqaa081
• 发表时间: 2020-12
• 期刊: NAR genomics and bioinformatics
• 影响因子: 4.6
• 作者: Freeman L;Brimacombe CS;Elhaik E
• 通讯作者: Elhaik E

Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years.

• DOI: 10.1016/j.crmeth.2022.100270
• 发表时间: 2022-08-22
• 期刊: Cell reports methods

Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond

在大流行时代及以后使用生物样本库作为国际资源的群体遗传考虑因素

• DOI: 10.20944/preprints202004.0394.v1
• 发表时间: 2020
• 作者: Elhaik E

Population genetic considerations for using biobanks as international resources in the pandemic era and beyond.

• DOI: 10.1186/s12864-021-07618-x
• 发表时间: 2021-05-17
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Carress H;Lawson DJ;Elhaik E
• 通讯作者: Elhaik E