Wang Cheng, Roy-Gagnon Marie-Hélène, Lefebvre Jean-François, Burkett Kelly M, Dubois Lise
School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Cres, Ottawa, Ontario, K1G 5Z3, Canada.
Department of Mathematics and Statistics, University of Ottawa, 150 Louis-Pasteur Pvt, Ottawa, Ontario, K1N 6N5, Canada.
BMC Med Genet. 2019 Jan 11;20(1):9. doi: 10.1186/s12881-018-0739-x.
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.
胰岛素样生长因子(IGF)通路基因与环境的交互作用可能导致儿童肥胖。这种基因 - 环境相互作用可能呈现复杂的形式。利用纵向家庭研究来检测这些关系需要同时考虑个体和家庭内部的相关性。
我们研究了三种在纵向家庭研究中检测交互作用的方法。双生子模型和基于非参数划分的得分检验使用个体结局平均值,而线性混合模型使用所有可用的纵向数据点。进行模拟实验以评估这些方法检测不同基因 - 环境交互关系的效能。将这些方法应用于魁北克新生儿双生子研究数据,以检验IGF通路基因(IGF - 1、IGFALS)与环境因素(身体活动、日托参与情况和睡眠时间)对体重指数结局的交互作用。
对于模拟数据,采用平均时间汇总统计量的双生子模型总体表现良好。当真实模型具有不同关系时将交互作用建模为线性会影响效能;对于某些非线性交互作用,这三种方法均无效。我们对IGF通路基因的分析表明,12号染色体上位置102,791,894处的IGF - 1变异与身体活动的联合效应存在提示性关联。然而,在进行多重检验校正后,这种关联无统计学意义。
本研究中考虑的分析方法对不同的基因 - 环境交互作用并不稳健。需要方法学创新来提高当前方法检测非线性交互作用的性能。为了更好地理解IGF通路在儿童肥胖发展中的作用,还需要更多的研究。