Assortative mating

Occurs when people choose their partners based on particular characteristics, such as height or intelligence, rather than at random. Single-trait assortative mating is characterised by assortment on a single trait (e.g., taller women tend to partner with taller men or mid-height men tend to mate with shorter women). Cross-trait assortative mating occurs when, for example, men with high values of one trait tend to partner with women with either higher or lower values of another trait (e.g., more educated men tend to partner with taller women). 

Cross-trait assortment on the exposure and outcome leads to a spurious genetic correlation between parents, which renders the exposure and outcome phenotypes genetically correlated in the offspring. In some cases, single-trait assortative mating can lead to the same phenomenon. Such spurious genetic correlation can lead to bias in MR. If available, parent’s genetic data can be used to detect, and adjusted for, any bias from assortative mating through within-family MR.

References

• Hartwig FP, Davies NM, Davey Smith G. Bias in Mendelian randomization due to assortative mating. Genetic Epidemiology 2018; 42: 608-620
• Lawlor DA, Richmond R, Warrington N et al. Using Mendelian randomization to determine causal effects of pregnancy (intrauterine) exposures on offspring outcomes: Sources of bias and methods for assessing them. . Wellcome Open Research 2017; 2: 11.

Other terms in 'Sources of bias and limitations in MR':

• 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
• Population stratification
• 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