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Modelling BMI Trajectories in Children for Genetic Association Studies

Author

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  • Nicole M Warrington
  • Yan Yan Wu
  • Craig E Pennell
  • Julie A Marsh
  • Lawrence J Beilin
  • Lyle J Palmer
  • Stephen J Lye
  • Laurent Briollais

Abstract

Background: 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. Methods and Results: 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. Conclusions: 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.

Suggested Citation

  • Nicole M Warrington & Yan Yan Wu & Craig E Pennell & Julie A Marsh & Lawrence J Beilin & Lyle J Palmer & Stephen J Lye & Laurent Briollais, 2013. "Modelling BMI Trajectories in Children for Genetic Association Studies," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    • Handle: RePEc:plo:pone00:0053897
    DOI: 10.1371/journal.pone.0053897
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    1. Nicole M Warrington & Laura D Howe & Yan Yan Wu & Nicholas J Timpson & Kate Tilling & Craig E Pennell & John Newnham & George Davey-Smith & Lyle J Palmer & Lawrence J Beilin & Stephen J Lye & Debbie A, 2013. "Association of a Body Mass Index Genetic Risk Score with Growth throughout Childhood and Adolescence," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-10, November.
    2. Warrington Nicole M. & Tilling Kate & Howe Laura D. & Paternoster Lavinia & Pennell Craig E. & Wu Yan Yan & Briollais Laurent, 2014. "Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 567-587, October.

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