An MR method applied to data comprising related individuals (e.g., sibling pairs or parent-offspring trios) within individual-level data to overcome possible biases that may arise when applying MR within unrelated individuals such as assortative mating, dynastic effects and population stratification.
MR relies on the assumption that alleles are randomly allocated from parents to offspring. However, in traditional MR studies of an exposure and outcome using data comprising unrelated individuals, the random inheritance of genetic information from parents to offspring does not always guarantee that there will be no confounding of the associations between genetic variants and the exposure or outcome of interest. Using data from related individuals (e.g., sibling pairs or parent-offspring trios) can account for genotypic and phenotypic differences between families that may be due to variation in allele frequencies relating to assortative mating, population-level structure variation in ancestry and differences in phenotype due to family environment relating to dynastic effects. Therefore, family-based studies can be used to overcome some of the limitations of MR due to these biases. Within-family designs are therefore a useful tool for strengthening causal inference, especially when compared to other studies using different methodologies (such as conventional observational epidemiological designs and MR within unrelated individuals). However, given the large-scale individual-level data that are required for such analyses, the main limitation of these designs is that there are far fewer samples with the required data structure than exists for unrelated individuals.
References
Other terms in 'One-sample MR methods':
- Generalized Method of Moments (GMM) estimator
- MR with a time-to-event outcome
- Non-parametric methods with bounds of causal effect
- Polygenic risk score approach
- Structural Mean Models (SMMs)
- Two-stage least squares (TSLS)
- Two-stage least squares (TSLS) with binary outcomes
- Two-stage predictor substitution estimators
- Two-stage residual inclusion estimators