Population stratification occurs when there are population subgroups that experience both different phenotypic distributions and have different allele frequencies for genetic variants that might be used in MR. This can result in spurious associations between genotype and phenotype (i.e., a trait that might be used as either the exposure or outcome in MR).
If genetic instrumental variables (IVs) used in an MR study were derived from a genome-wide association study (GWAS) in a population in which there were sub-groups (e.g., people from a variety of ancestries) that differed with respect to the distributions of both the genetic IVs and the phenotype (exposure of interest in the MR study), then the association between the genetic IV and exposure could be biased by confounding. If the association between the genetic IV and outcome was identified from studies in which there were sub-groups with differing genetic and outcome distributions, the association between the genetic IV and outcome could be biased by confounding. Most GWASs and MR studies try to minimise this by including homogeneous groups of participants (e.g., of just one ancestry) and/or by adjusting for genetic principal components that reflect different sub-groups within that population sample that explain variation in the data. MR using data within families (such as siblings or family trios) is an increasingly used method to account for possible confounding through population structure.
References
Other terms in 'Sources of bias and limitations in MR':
- Assortative mating
- Canalization
- Collider
- Collider bias
- Conditional F-statistic for multiple exposures
- Confounding
- Exclusion restriction assumption
- F-statistic
- Harmonization (in two-sample MR)
- Homogeneity Assumption
- Horizontal Pleiotropy
- Independence assumption
- INstrument Strength Independent of Direct Effect (InSIDE) assumption
- Intergenerational (or dynastic) effects
- Monotonicity assumption
- MR for testing critical or sensitive periods
- MR for testing developmental origins
- No effect modification assumption
- NO Measurement Error (NOME) assumption
- Non-linear MR
- Non-overlapping samples (in two-sample MR)
- Overfitting
- Pleiotropy
- R-squared
- Regression dilution bias (attenuation by errors)
- Relevance assumption
- Reverse causality
- Same underlying population (in two-sample MR)
- Statistical power and efficiency
- Vertical pleiotropy
- Weak instrument bias
- Winner's curse