Table 4.

Variance component analysis of IGF1, testing for independent effect of age, SNP (additive model), and their interaction

VCAa—parameters of the model		rs855203	rs35765	rs2288378	rs7964748
Regression parameters	β 1 (age)	−0.46 ± 0.01	−0.46 ± 0.01	−0.46 ± 0.01	−0.46 ± 0.01
	β 2 (SNP)	−0.10 ± 0.03	0.09 ± 0.03	0.07 ± 0.02	0.07 ± 0.02
	β 3 (age × SNP)	−0.08 ± 0.03	0	0	0
p 1 value (LRT)		<0.001	0.26	0.68	0.80
Correlationb (r) (N, p value)	AA (major)	0.46 (3,630, <0.001)	0.48 (3,503, <0.001)	0.48 (2,516, <0.001)	0.48 (3,008, <0.001)
	AB	0.53 (770, <0.001)	0.48 (883, <0.001)	0.45 ± 0.01 (1,631, <0.001)	0.46 (1,292, <0.001)
	BB (minor)	0.22 (57, 0.08)	0.24 (70, 0.03)	0.52 ± 0.03 (307, <0.001)	0.49 (157, < 0.001)
p 2 value (χ 2)	AA/AB	0.009	0.241	0.20	0.22
	AA/BB	0.020	0.008	0.18	0.43
	AB/BB	0.004	0.010	0.09	0.32
V componentsc	Given in Table 3

VCA variance component analysis; β ₁ , β ₂ , β ₃ regression coefficients reflecting effect of age, genotype, and age*; p ₁ value (LRT) likelihood ratio test for heterogeneity of regression coefficients, showing genotype-specific age dependence of IGF-1 variation; p ₂ value (χ ² ) pairwise comparison of the corresponding genotype-specific correlations between IGF-1 level variations and age

^aTesting effects of covariates on IGF-1 variation

^bPresents genotype-specific correlation, by SNP, sample size, and p value for correlation coefficient estimate

^cVariance component estimated in the entire sample and for “all ages” given in Table 3