Related Experiment Video
Updated: Nov 17, 2025
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
The use of negative control outcomes in Mendelian randomization to detect potential population stratification
Eleanor Sanderson1,2, Tom G Richardson1,2, Gibran Hemani1,2
1MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Abstract:
A key assumption of Mendelian randomization (MR) analysis is that there is no association between the genetic variants used as instruments and the outcome other than through the exposure of interest. One way in which this assumption can be violated is through population stratification, which can introduce confounding of the relationship between the genetic variants and the outcome and so induce an association between them. Negative control outcomes are increasingly used to detect unobserved confounding in observational epidemiological studies. Here we consider the use of negative control outcomes in MR studies to detect confounding of the genetic variants and the exposure or outcome. As a negative control outcome in an MR study, we propose the use of phenotypes which are determined before the exposure and outcome but which are likely to be subject to the same confounding as the exposure or outcome of interest. We illustrate our method with a two-sample MR analysis of a preselected set of exposures on self-reported tanning ability and hair colour. Our results show that, of the 33 exposures considered, genome-wide association studies (GWAS) of adiposity and education-related traits are likely to be subject to population stratification that is not controlled for through adjustment, and so any MR study including these traits may be subject to bias that cannot be identified through standard pleiotropy robust methods. Negative control outcomes should therefore be used regularly in MR studies to detect potential population stratification in the data used.
Insights
Negative control outcomes can detect population stratification confounding in Mendelian randomization (MR) studies. This method helps identify biases in genetic association studies, particularly for adiposity and education traits, ensuring more reliable research findings.
Area of Science:
- Epidemiology
- Genetic Epidemiology
- Statistical Genetics
Background:
- Mendelian randomization (MR) relies on instrumental variables (genetic variants) not confounding the exposure-outcome relationship.
- Population stratification can violate this assumption by creating spurious associations between genetic variants and outcomes.
- Negative control outcomes are established tools for detecting unobserved confounding in observational studies.
Purpose of the Study:
- To propose and evaluate the use of negative control outcomes for detecting population stratification in Mendelian randomization (MR) studies.
- To assess confounding in MR analyses by examining phenotypes determined prior to the exposure and outcome of interest.
- To identify specific traits (e.g., adiposity, education) prone to population stratification in genome-wide association studies (GWAS).
Main Methods:
- Utilized a two-sample MR analysis framework.
- Employed negative control outcomes, defined as phenotypes predating the exposure and outcome.
- Investigated 33 preselected exposures using GWAS data for self-reported tanning ability and hair color as negative controls.
Main Results:
- Identified population stratification confounding in GWAS for adiposity and education-related traits.
- Demonstrated that standard pleiotropy-robust methods may not detect this specific type of bias.
- Highlighted that MR studies using these traits risk unidentifiable bias due to uncontrolled population stratification.
Conclusions:
- Negative control outcomes are crucial for detecting population stratification in MR studies.
- Regular use of negative control outcomes can improve the validity of MR findings.
- Adiposity and education-related traits require careful handling in MR due to potential confounding by population stratification.
More Related Videos
07:15Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
09:37Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
Published on: August 15, 2019
Related Concept Videos
01:13Randomized Experiments
Simple randomization
Simple...
01:25Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:13Controls in Experiments
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:52Regression Toward the Mean
01:27Confounding in Epidemiological Studies