Using multiple genetic variants as instrumental variables for modifiable risk factors

By T. Palmer, Deborah Lawlor, R. Harbord, Nuala Sheehan, Jonathan H. Tobias, Nicholas J. Timpson, George Davey Smith, J. Sterne. Statistical Methods in Medical Research, 2012

1,190 citations recorded by the source corpus.

Mendelian randomisation analyses use genetic variants as instrumental variables (IVs) to estimate causal effects of modifiable risk factors on disease outcomes. Genetic variants typically explain a small proportion of the variability in risk factors; hence Mendelian randomisation analyses can require large sample sizes. However, an increasing number of genetic variants have been found to be robustly associated with disease-related outcomes in genome-wide association studies. Use of multiple instruments can improve the precision of IV estimates, and also permit examination of underlying IV assumptions. We discuss the use of multiple genetic variants in Mendelian randomisation analyses with continuous outcome variables where all relationships are assumed to be linear. We describe possible violations of IV assumptions, and how multiple instrument analyses can be used to identify them. We present an example using four adiposity-associated genetic variants as IVs for the causal effect of fat mass on bone density, using data on 5509 children enrolled in the ALSPAC birth cohort study. We also use simulation studies to examine the effect of different sets of IVs on precision and bias. When each instrument independently explains variability in the risk factor, use of multiple instruments increases the precision of IV estimates. However, inclusion of weak instruments could increase finite sample bias. Missing data on multiple genetic variants can diminish the available sample size, compared with single instrument analyses. In simulations with additive genotype-risk factor effects, IV estimates using a weighted allele score had similar properties to estimates using multiple instruments. Under the correct conditions, multiple instrument analyses are a promising approach for Mendelian randomisation studies. Further research is required into multiple imputation methods to address missing data issues in IV estimation.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b7568142c5540a594b5c0e0df86f611c242a2827

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Stephen Burgess
2. J. Bowden
3. S. Thompson
4. George Davey Smith
5. Nuala Sheehan
6. Cosetta Minelli
7. J. Thompson
8. Adam S. Butterworth

In Statistics and Probability

1. Stephen Burgess
2. S. Thompson
3. Cosetta Minelli
4. Adam S. Butterworth
5. Nuala Sheehan
6. J. Thompson
7. F. Greco M
8. Peter Kraft

Citations in the corpus

• Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts (2013, background): “One of the methodological challenges of the MR approach relates to the large sample size requirement, arising from the availability of relatively weak instruments for most exposures [22,31].”
• Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators (2013, methodology): “A possible solution to this is the use of multiple IVs, when available (7, 16).”
• Mendelian randomization of blood lipids for coronary heart disease (2014, neutral): “This should increase power, because each additional SNP contributes incrementally to the explained variance in the lipid fraction of interest and reduces the lack of specificity often observed with single SNPs, because the effects on traits other than the lipid fraction of interest should be small, ”
• Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome (2015, methodology): “When using data on G − X and G − Y that was collected on the same subjects, formal testing of pleiotropy can be performed using the Sargan test [13], an over-identification test associated with the two-stage regression that tests the null hypothesis that all instruments provide the same MR estimate ”
• Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data (2013, background): “This problem can be partially redressed by the use of multiple genetic variants [Palmer et al., 2011].”