Using Structural Equation Modelling to Jointly Estimate Maternal and Fetal Effects on Birthweight in the UK Biobank
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International Journal of Epidemiology
Oxford University Press (OUP) for International Epidemiological Association
© The Author(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: To 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. Methods: We 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. Results: Unlike simple regression models, our approach is unbiased when there is both a maternal and 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, 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 maternal effect. We also identify a subset of birthweight associated SNPs that have opposing maternal and fetal effects in the UK Biobank. Conclusions: Our results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.
N.M.W. is supported by a National Health and Medical Research Council Early Career Fellowship (grant number APP1104818). D.M.E. is funded by an Australian Research Council Future Fellowship (grant number FT130101709) and an Medical Research Council programme grant (grant number MC_UU_12013/4). This research has been conducted using the UK Biobank Resource. Access to the UKBB study data was funded by University of Queensland Early Career Researcher Grant (2014002959).
This is the author accepted manuscript. The final version is available from OUP via the DOI in this record
Published online 13 February 2018