Modeling gene-environment interactions in longitudinal family studies: a comparison of methods and their application to the association between the IGF pathway and childhood obesity

摘要

The interactive effect of the IGF pathway genes with the environment may contribute to childhood obesity. Such gene-environment interactions can take on complex forms. Detecting those relationships using longitudinal family studies requires simultaneously accounting for correlations within individuals and families. We studied three methods for detecting interaction effects in longitudinal family studies. The twin model and the nonparametric partition-based score test utilized individual outcome averages, whereas the linear mixed model used all available longitudinal data points. Simulation experiments were performed to evaluate the methods’ power to detect different gene-environment interaction relationships. These methods were applied to the Quebec Newborn Twin Study data to test for interaction effects between the IGF pathway genes (IGF-1, IGFALS) and environmental factors (physical activity, daycare attendance and sleep duration) on body mass index outcomes. For the simulated data, the twin model with the mean time summary statistic yielded good performance overall. Modelling an interaction as linear when the true model had a different relationship influenced power; for certain non-linear interactions, none of the three methods were effective. Our analysis of the IGF pathway genes showed suggestive association for the joint effect of IGF-1 variant at position 102,791,894 of chromosome 12 and physical activity. However, this association was not statistically significant after multiple testing correction. The analytical approaches considered in this study were not robust to different gene-environment interactions. Methodological innovations are needed to improve the current methods’ performances for detecting non-linear interactions. More studies are needed in order to better understand the IGF pathway’s role in childhood obesity development.