School of Women's and Infants' Health, The University of Western Australia, Perth, Western Australia, Australia.
PLoS One. 2013;8(1):e53897. doi: 10.1371/journal.pone.0053897. Epub 2013 Jan 17.
The timing of associations between common genetic variants and changes in growth patterns over childhood may provide insight into the development of obesity in later life. To address this question, it is important to define appropriate statistical models to allow for the detection of genetic effects influencing longitudinal childhood growth.
Children from The Western Australian Pregnancy Cohort (Raine; n=1,506) Study were genotyped at 17 genetic loci shown to be associated with childhood obesity (FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, SEC16B, LYPLAL1, TFAP2B, MTCH2, BCDIN3D, NRXN3, SH2B1, MRSA) and an obesity-risk-allele-score was calculated as the total number of 'risk alleles' possessed by each individual. To determine the statistical method that fits these data and has the ability to detect genetic differences in BMI growth profile, four methods were investigated: linear mixed effects model, linear mixed effects model with skew-t random errors, semi-parametric linear mixed models and a non-linear mixed effects model. Of the four methods, the semi-parametric linear mixed model method was the most efficient for modelling childhood growth to detect modest genetic effects in this cohort. Using this method, three of the 17 loci were significantly associated with BMI intercept or trajectory in females and four in males. Additionally, the obesity-risk-allele score was associated with increased average BMI (female: β=0.0049, P=0.0181; male: β=0.0071, P=0.0001) and rate of growth (female: β=0.0012, P=0.0006; male: β=0.0008, P=0.0068) throughout childhood.
Using statistical models appropriate to detect genetic variants, variations in adult obesity genes were associated with childhood growth. There were also differences between males and females. This study provides evidence of genetic effects that may identify individuals early in life that are more likely to rapidly increase their BMI through childhood, which provides some insight into the biology of childhood growth.
常见遗传变异与儿童期生长模式变化之间的关联时间可能为了解肥胖在以后生活中的发展提供线索。为了解决这个问题，重要的是要定义适当的统计模型，以检测影响儿童期纵向生长的遗传效应。
在西澳大利亚妊娠队列研究（Raine；n=1506）中，对 17 个与儿童肥胖相关的遗传位点进行了基因分型（FTO、MC4R、TMEM18、GNPDA2、KCTD15、NEGR1、BDNF、ETV5、SEC16B、LYPLAL1、TFAP2B、MTCH2、BCDIN3D、NRXN3、SH2B1、MRSA），并计算了每个个体拥有的“风险等位基因”总数作为肥胖风险等位基因评分。为了确定适合这些数据的统计方法，并具有检测 BMI 生长曲线中遗传差异的能力，研究了四种方法：线性混合效应模型、具有偏斜 t 随机误差的线性混合效应模型、半参数线性混合模型和非线性混合效应模型。在这四种方法中，半参数线性混合模型方法最适合建模儿童生长，以检测该队列中的适度遗传效应。使用这种方法，在女性中，有 17 个位点中的 3 个与 BMI 截距或轨迹显著相关，而在男性中，有 4 个与 BMI 截距或轨迹显著相关。此外，肥胖风险等位基因评分与儿童期平均 BMI 增加相关（女性：β=0.0049，P=0.0181；男性：β=0.0071，P=0.0001）和生长速度（女性：β=0.0012，P=0.0006；男性：β=0.0008，P=0.0068）。
使用适当的检测遗传变异的统计模型，成年肥胖基因的变异与儿童生长有关。男性和女性之间也存在差异。这项研究提供了遗传效应的证据，这些证据可能会在生命早期识别出更有可能通过儿童期快速增加 BMI 的个体，这为儿童生长的生物学提供了一些见解。
