Using Structural Equation Modeling to Jointly Estimate Maternal and Foetal Effects on Birthweight in the UK Biobank

BackgroundTo date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individuals own genotype on their birthweight, their mothers genotype, or both.\n\nMethodsWe demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and foetal 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.\n\nResultsUnlike simple regression models, our approach is unbiased when there is both a maternal and foetal effect. The method can be used when either the individuals 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, 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 foetal and/or maternal effect. We also identify a subset of birth weight associated SNPs that have opposing maternal and foetal effects in the UK Biobank.\n\nConclusionsOur results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.\n\nKey MessagesO_LIWe describe a structural equation model to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual.\nC_LIO_LIUsing simulation, we show that our approach is unbiased when there is both a maternal and foetal effect, unlike simple linear regression models. Additionally, we illustrate that the structural equation model is largely robust to measurement error and missing data for either the individuals own phenotype or the phenotype of their offspring.\nC_LIO_LIWe describe how the flexibility of the structural equation modelling framework will allow the inclusion of summary statistics from studies that are unable to share raw data.\nC_LIO_LIUsing the structural equation model to estimate the maternal and foetal effects of known birthweight associated loci in the UK Biobank, we identify three loci that have primary effects through the maternal genome and six loci that have opposite effects in the maternal and foetal genomes.\nC_LI