Related Experiment Video
Updated: Jul 28, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Falsification of the instrumental variable conditions in Mendelian randomization studies in the UK Biobank
Kelly Guo1, Elizabeth W Diemer2,3,4, Jeremy A Labrecque5
1Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. k.guo@erasmusmc.nl.
Abstract:
Mendelian randomization (MR) is an increasingly popular approach to estimating causal effects. Although the assumptions underlying MR cannot be verified, they imply certain constraints, the instrumental inequalities, which can be used to falsify the MR conditions. However, the instrumental inequalities are rarely applied in MR. We aimed to explore whether the instrumental inequalities could detect violations of the MR conditions in case studies analyzing the effect of commonly studied exposures on coronary artery disease risk.Using 1077 single nucleotide polymorphisms (SNPs), we applied the instrumental inequalities to MR models for the effects of vitamin D concentration, alcohol consumption, C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol on coronary artery disease in the UK Biobank. For their relevant exposure, we applied the instrumental inequalities to MR models proposing each SNP as an instrument individually, and to MR models proposing unweighted allele scores as an instrument. We did not identify any violations of the MR assumptions when proposing each SNP as an instrument individually. When proposing allele scores as instruments, we detected violations of the MR assumptions for 5 of 6 exposures.Within our setting, this suggests the instrumental inequalities can be useful for identifying violations of the MR conditions when proposing multiple SNPs as instruments, but may be less useful in determining which SNPs are not instruments. This work demonstrates how incorporating the instrumental inequalities into MR analyses can help researchers to identify and mitigate potential bias.
Insights
Instrumental inequalities can detect violations in Mendelian randomization (MR) causal effect estimates when using multiple genetic variants (SNPs). This method is less effective for individual SNPs but crucial for identifying bias in complex genetic analyses.
Area of Science:
- Epidemiology
- Statistical Genetics
Background:
- Mendelian randomization (MR) is a widely used method for inferring causal relationships.
- The core assumptions of MR cannot be directly tested but imply instrumental inequalities.
- Instrumental inequalities offer a falsification approach to MR assumptions but are underutilized.
Purpose of the Study:
- To evaluate the utility of instrumental inequalities in detecting violations of MR assumptions.
- To assess the performance of instrumental inequalities in case studies of common exposures and coronary artery disease risk.
Main Methods:
- Applied instrumental inequalities to MR models using 1077 SNPs in the UK Biobank.
- Analyzed exposures including vitamin D, alcohol, CRP, triglycerides, HDL, and LDL cholesterol.
- Tested MR models with individual SNPs and unweighted allele scores as instruments.
Main Results:
- No MR assumption violations were detected when using individual SNPs as instruments.
- Violations of MR assumptions were identified for 5 out of 6 exposures when using allele scores.
- Demonstrated the potential of instrumental inequalities to detect bias in multi-SNP MR analyses.
Conclusions:
- Instrumental inequalities are valuable for detecting MR assumption violations with multi-SNP instruments.
- The method may be less effective for pinpointing specific invalid SNPs.
- Incorporating instrumental inequalities can enhance the reliability of MR studies by identifying and mitigating bias.
More Related Videos
Related Concept Videos
Bias in Epidemiological Studies
Confounding in Epidemiological Studies
Randomized Experiments
Simple randomization
Simple...
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Regression Toward the Mean
Blinding

