Abstract
BackgroundTo date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individual’s own genotype on their birthweight, their mother’s genotype, or both.MethodsWe demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and fetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study.ResultsUnlike simple regression models, our approach is unbiased when there is both a maternal and a fetal effect. The method can be used when either the individual’s own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, and that there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype and reasonable power to detect whether it is a fetal and/or a maternal effect. We also identify a subset of birthweight-associated single nucleotide polymorphisms (SNPs) that have opposing maternal and fetal effects in the UK Biobank.ConclusionsOur results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.
Highlights
Birthweight is a complex trait, and low birthweight is robustly associated with increased risk of a range of cardiometabolic diseases in later life.[1]
In the scenarios where there is no maternal effect, the fetal effect estimated from the linear model is unbiased
These results show that for single nucleotide polymorphisms (SNPs) where the maternal and fetal effects go in opposite directions, the fetal effect estimated in the GWAS6 would have been reported to be smaller than its true effect
Summary
Birthweight is a complex trait, and low birthweight is robustly associated with increased risk of a range of cardiometabolic diseases in later life.[1]. Org/) and the UK Biobank,[7] we identified 60 single nucleotide polymorphisms (SNPs) associated with birthweight at genome-wide levels of significance.[6] One difficulty we faced in interpreting our results was that it was often not clear whether genetic associations reflected the effect of an individual’s own genotype on their birthweight, an effect of their mother’s genotype on their birthweight (i.e. maternal genotype mediated through the intrauterine effect) or some combination of both. In the case that the mother has hyperglycaemia due to a GCK mutation, but the fetus does not inherit the mutation, the birthweight is higher due to normal glucose sensing and above-average insulin secretion. This example reflects contrasting effects mediated through the intrauterine environment (i.e. maternal effects) and direct effects of the offspring’s genotype.[8]. Conclusions: Our results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone
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