Modelling BMI Trajectories in Children for Genetic Association Studies

• DOI: 10.1371/journal.pone.0053897
• Publication Date: 2013-01-17T22:14:14Z
• Authors: Nicole M. Warrington; Yan Yan Wu; Craig E. Pennell; Julie A. Marsh; Lawrence J. Beilin; Lyle J. Palmer; Stephen J. Lye; Laurent Briollais
• Keywords: Mixed model; genetic model

Background The timing of associations between common genetic variants and changes in growth patterns over childhood may provide insight into the development obesity later life. To address this question, it is important to define appropriate statistical models allow for detection effects influencing longitudinal growth. Methods Results Children from Western Australian Pregnancy Cohort (Raine; n = 1,506) Study were genotyped at 17 loci shown be associated with (FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, SEC16B, LYPLAL1, TFAP2B, MTCH2, BCDIN3D, NRXN3, SH2B1, MRSA) an obesity-risk-allele-score was calculated as total number 'risk alleles' possessed by each individual. determine method that fits these data has ability detect differences BMI profile, four methods investigated: linear mixed model, model skew-t random errors, semi-parametric a non-linear model. Of methods, most efficient modelling modest cohort. Using method, three significantly intercept or trajectory females males. Additionally, obesity-risk-allele score increased average (female: β 0.0049, P 0.0181; male: 0.0071, 0.0001) rate 0.0012, 0.0006; 0.0008, 0.0068) throughout childhood. Conclusions variants, variations adult genes There also males females. This study provides evidence identify individuals early life are more likely rapidly increase their through childhood, which some biology