Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.
Genet Epidemiol. 2012 Apr;36(3):206-13. doi: 10.1002/gepi.21613. Epub 2012 Feb 6.
It has been suggested that children with larger brains tend to perform better on IQ tests or cognitive function tests. Prenatal head growth and head growth in infancy are two crucial periods for subsequent intelligence. Studies have shown that environmental exposure to air pollutants during pregnancy is associated with fetal growth reduction, developmental delay, and reduced IQ. Meanwhile, genetic polymorphisms may modify the effect of environment on head growth. However, studies on gene-environment or gene-gene interactions on growth trajectories have been quite limited partly due to the difficulty to quantitatively measure interactions on growth trajectories. Moreover, it is known that assessing the significance of gene-environment or gene-gene interactions on cross-sectional outcomes empirically using the permutation procedures may bring substantial errors in the tests. We proposed a score that quantitatively measures interactions on growth trajectories and developed an algorithm with a parametric bootstrap procedure to empirically assess the significance of the interactions on growth trajectories under the likelihood framework. We also derived a Wald statistic to test for interactions on growth trajectories and compared it to the proposed parametric bootstrap procedure. Through extensive simulation studies, we demonstrated the feasibility and power of the proposed testing procedures. We applied our method to a real dataset with head circumference measures from birth to age 7 on a cohort currently being conducted by the Columbia Center for Children's Environmental Health (CCCEH) in Krakow, Poland, and identified several significant gene-environment interactions on head circumference growth trajectories.
有人认为，大脑较大的儿童在智商测试或认知功能测试中的表现往往更好。产前头部生长和婴儿期头部生长是随后智力发展的两个关键时期。研究表明，怀孕期间接触空气污染物与胎儿生长减少、发育迟缓以及智商降低有关。同时，遗传多态性可能会改变环境对头围生长的影响。然而，由于难以定量测量生长轨迹上的相互作用，因此关于基因-环境或基因-基因相互作用对生长轨迹的研究相当有限。此外，众所周知，使用置换程序从横截面上评估基因-环境或基因-基因相互作用对结果的显著性可能会导致检验中的实质性错误。我们提出了一种定量测量生长轨迹上相互作用的分数，并开发了一种带有参数自举程序的算法，根据似然框架在生长轨迹上实证评估相互作用的显著性。我们还推导出了一个 Wald 统计量来检验生长轨迹上的相互作用，并将其与提出的参数自举程序进行了比较。通过广泛的模拟研究，我们证明了所提出的检验程序的可行性和功效。我们将该方法应用于来自波兰克拉科夫哥伦比亚儿童环境健康中心（CCCEH）目前正在进行的一个队列的出生到 7 岁时的头围测量的真实数据集，并确定了头围生长轨迹上的几个显著的基因-环境相互作用。
