Table 3.

Maximum likelihood estimates from the biometric regression model.

Parameter	Estimate	SE	p
AI → Dem	−7.98	77.54	0.918
AS → Dem	−1.94	98.31	0.984
CI → Dem	3.20	3.54	0.904
CS → Dem	-	-	-
EI → Dem	−0.23	1.04	0.825
ES → Dem	3.39	8.68	0.696
${\sigma }_{A_I}^2$	0.02	0.04	0.560
${\sigma }_{A_S}^2$	0.001	0.004	0.870
${\sigma }_{C_I}^2$	0.04	0.03	0.242
${\sigma }_{C_S}^2$	-	-	-
${\sigma }_{E_I}^2$	0.15	0.04	<0.001
${\sigma }_{E_S}^2$	0.01	0.01	0.502
${\sigma }_{{Dem}_{MZW}}^2$	1.15	4.16	0.782
${\sigma }_{{Dem}_{MZB}}^2$	0.11	0.07	0.139
${\sigma }_{{Dem}_{DZW}}^2$	0.55	0.47	0.242
${\sigma }_{{Dem}_{DZB}}^2$	0.00	0.00	0.999
${\sigma }_{age}^2$	0.36	0.01	<0.001
σAI, AS	0.002	0.003	0.620
σCI, CS	-	-	-
σEI, ES	0.01	0.01	0.676

A, additive genetic effect; C, shared environmental effect; E, non-shared environmental effect; Dem, residualized dementia score; I, intercept; S, linear slope; MZ, monozygotic; DZ, dizygotic; W, within-family level; B, between-family level.