Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/87869
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Type: Journal article
Title: Modelling BMI trajectories in children for genetic association studies
Author: Warrington, N.
Wu, Y.
Pennell, C.
Marsh, J.
Beilin, L.
Palmer, L.
Lye, S.
Briollais, L.
Citation: PLoS One, 2013; 8(1):e53897-1-e53897-12
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
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.
Keywords: Humans; Obesity; Genetic Predisposition to Disease; Body Mass Index; Risk Factors; Polymorphism, Single Nucleotide; Algorithms; Models, Genetic; Adolescent; Child; Child, Preschool; Infant; Female; Male; Genetic Association Studies
Rights: © 2013 Warrington et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0020136601
DOI: 10.1371/journal.pone.0053897
Grant ID: http://purl.org/au-research/grants/nhmrc/403981
Appears in Collections:Translational Health Science publications

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