Modelling BMI trajectories in children for genetic association studies

Warrington, Nicole M., Wu, Yan Yan, Pennell, Craig E., Marsh, Julie A., Beilin, Lawrence J., Palmer, Lyle J., Lye, Stephen J. and Briollais, Laurent (2013) Modelling BMI trajectories in children for genetic association studies. PLoS One, 8 1: . doi:10.1371/journal.pone.0053897


Author Warrington, Nicole M.
Wu, Yan Yan
Pennell, Craig E.
Marsh, Julie A.
Beilin, Lawrence J.
Palmer, Lyle J.
Lye, Stephen J.
Briollais, Laurent
Title Modelling BMI trajectories in children for genetic association studies
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-01-17
Sub-type Article (original research)
DOI 10.1371/journal.pone.0053897
Open Access Status DOI
Volume 8
Issue 1
Total pages 12
Place of publication San Francisco, United States
Publisher Public Library of Science
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article number e53897.

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Diamantina Institute Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 12 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 16 May 2014, 19:34:20 EST by Nicole Warrington on behalf of UQ Diamantina Institute