Modelling BMI Trajectories in Children for Genetic Association Studies |
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| Authors: | Nicole M. Warrington Yan Yan Wu Craig E. Pennell Julie A. Marsh Lawrence J. Beilin Lyle J. Palmer Stephen J. Lye Laurent Briollais |
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| Affiliation: | 1. School of Women’s and Infants’ Health, The University of Western Australia, Perth, Western Australia, Australia.; 2. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.; 3. School of Medicine and Pharmacology, The University of Western Australia, Perth, Western Australia, Australia.; 4. Ontario Institute for Cancer Research, Toronto, Ontario, Canada.; John Hopkins Bloomerg School of Public Health, United States of America, |
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| Abstract: | BackgroundThe 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.Methods and ResultsChildren 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.ConclusionsUsing 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. |
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