Table 1.
The phenotypic characteristics of the Raine sample.
Table 2.
Characteristics of the best model for each method.
Figure 1.
Q-Q plot of residuals for each of the methods by females (top four) and males (bottom four).
Table 3.
Statistical measures used to compare model fit of the four methods.
Figure 2.
Distribution of obesity-risk allele score, with error bars for mean BMI at age 14 years.
The obesity-risk-allele score incorporates genotypes from 17 loci (FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, SEC16B, LYPLAL1, TFAP2B, MTCH2, BCDIN3D, NRXN3, SH2B1, and MRSA) in the 1,219 individuals from the Raine study with complete genetic data. The error bars display the mean (95% CI) BMI at age 14 years (the largest follow-up in adolescence) for each risk-allele score.
Table 4.
Results from association analysis of the obesity-risk allele score with BMI trajectory using the four methods.
Figure 3.
Population average curves from the SPLMM method in females and males.
Predicted population average BMI trajectories from 1–18 years for individuals with 15 (lower quartile), 17 (median), and 18 (upper quartile) risk alleles in the allele score.
Figure 4.
Associations between the risk-allele score and BMI at each follow-up in females and males.
Regression coefficients (95% CI) presented on ln(BMI) scale from the Semi-Parametric Linear Mixed Model (SPLMM) longitudinal model, derived at each of the average ages of follow-up. For example, a male with 17 obesity-risk-alleles is likely to have an ln(BMI) 0.005 units higher at age 6 than a male with 16 risk-alleles and by age 14 this difference will be increased to 0.010 units.