Table 1

Parameter estimation using aetiological model fitting on ocular refraction data after transformation to Gaussian distribution. Best fitting model in this case is the “AE model” (Additive genetic and unique Environmental effects), indicated in bold. (Abbreviations are explained in the footnote)

Right eye (n=53/59)	A	95% CI	D	95% CI	C	95% CI	E	95% CI	Chi²	dF	p	AIC	RM
ACE	0.88	(0.61 ; 0.93)			0.00	(0.00 ; 0.28)	0.11	(0.073 ; 0.18)	3.84	3	0.279	-2.16	0.06
ADE	0.74	(0.00 ; 0.93)	0.15	(0.00 ; 0.91)			0.11	(0.073 ; 0.18)	3.70	3	0.296	-2.30	0.06
AE	*0.89*	*(0.82 ; 0.93)*					*0.11*	*(0.073 ; 0.18)*	*3.84*	4	*0.428*	*-4.16*	*0.04*
CE					0.61	(0.48 ; 0.72)	0.39	(0.28 ; 0.52)	38.00	4	0.00	30.00	0.38
DE			0.89	(0.82 ; 0.93)			0.11	(0.075 ; 0.18)	6.49	4	0.166	-1.52	0.09

Left eye (n=49/61)
ACE	0.91	(0.69 ; 0.94)			0.00	(0.00 ; 0.22)	0.092	(0.059 ; 0.15)	3.93	3	0.269	-2.07	0.06
ADE	0.57	(0.00 ; 0.94)	0.34	(0.00 ; 0.93)			0.093	(0.060 ; 0.15)	3.18	3	0.365	-2.82	0.05
AE	0.91	(0.85 ; 0.94)					0.092	(0.059 ; 0.15)	3.93	4	0.415	-4.07	0.05
CE					0.59	(0.45 ; 0.70)	0.41	(0.30 ; 0.55)	44.76	4	0.00	36.76	0.42
DE			0.91	(0.85 ; 0.94)			0.094	(0.061 ; 0.15)	4.80	4	0.309	-3.20	0.07

N = number of MZ/DZ pairs.
The numbers differ from the total number of pairs because of the exclusion criteria and outliers, as explained in the text.
Estimated parameters:
A = proportion of phenotypic variation because of additive genetic effects.
D = proportion of phenotypic variation because of dominant genetic effects.
C = proportion of phenotypic variation because of common environmental effects.
E = proportion of phenotypic variation because of unique environmental effects.
95% CI = 95% confidence limits of estimate.
Goodness of fit test:
χ² = chi square.
dF = degrees of freedom.
p = level of significance, should be non-significant.
AIC = “Akaikes test value”, should be low and preferably negative.
RM = “RMSEA value,” should be low (< 0.1 for a “good fit” and < 0.05 for a “very good fit”).