Table 1.

Weighted segregation analysis of intercepts*

	Hypothesis
			Mendelian
Segregation Parameter	General		Codominant		Dominant		Recessive		Additive		No Major Gene
	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE
Intercept	4.769	0.0078	4.771	0.0072	4.802	0.0052	4.801	0.0051	4.776	0.0065	4.846	0.0041
βcohort	-0.088	0.0077	-0.092	0.0043	-0.092	0.0046	-0.091	0.0047	-0.092	0.0043	-0.085	0.0049
βSex	0.011	0.0046	0.011	0.0046	0.012	0.0046	0.010	0.0047	0.012	0.0046	0.005	0.0049
βAA	0.288	0.0143	0.283	0.0142	0.165	0.0066	0.167	0.0072	0.269	0.0107	—	—
βAa	0.118	0.0076	0.115	0.0078	0.165A	—	0.000B	—	0.135C	—	—	—
q A	0.323	0.0539	0.305	0.0373	0.139	0.0180	0.511	0.0304	0.257	0.0285	—	—
σ2	0.004	0.0004	0.004	0.0004	0.006	0.0004	0.006	0.0004	0.004	0.0004	0.011	0.0004
τaa	0.000	0.0000	0.000D	—	0.000D	—	0.000D	—	0.000D	—	—	—
τAa	0.476	0.0610	0.500D	—	0.500D	—	0.500D	—	0.500D	—	—	—
τAA	0.935	0.0611	1.000D	—	1.000D	—	1.000D	—	1.000D	—	—	—
-2(log-likelihood)	-3482.64		-3480.94		-3400.16		-3376.52		-3463.32		-3155.59
p-valueE	—		0.43		< 0.001		< 0.001		< 0.001		< 0.001
AICF	-3462.64		-3466.94		-3388.16		-3364.52		-3451.32		-3147.59

*The outcome being modeled in equation (2) is a_i from equation (1). ^AConstrained to equal β_AA. ^BConstrained to equal 0. ^C Constrained to equal 1/2 β_AA. ^D Parameter value is fixed. ^Ep-value based on a likelihood ratio test with the general model as the base model.^FAIC = -2(log-likelihood) + 2(number of free parameters).