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We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown.
We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes.
Figure 1 illustrates the mathematical features of our structural equation model (SEM) in the form of a path diagram.
One-headed arrows represent causal paths and two-headed arrows correlational relationships.
The residual error terms for the birthweight of the individual and their offspring are represented by Structural equation model (SEM) used to estimate maternal and offspring genetic effects on birthweight.
There is considerable interest in elucidating the causal effect of maternal environmental exposures on offspring outcomes.
However, traditional observational epidemiological studies are prone to confounding, bias and reverse causality.
This includes studies that aim to assess the causal effect of exposures related to fetal growth restriction on future risk of disease in offspring.
We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes.
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We discuss how this partitioning could be used to facilitate large-scale two-sample MR studies of maternal exposures and offspring outcomes in different samples of individuals, maximizing sample size and obviating the requirement of individual-level genotyped mother–offspring pairs.