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

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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.