# 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
β cohort -0.088 0.0077 -0.092 0.0043 -0.092 0.0046 -0.091 0.0047 -0.092 0.0043 -0.085 0.0049
β Sex 0.011 0.0046 0.011 0.0046 0.012 0.0046 0.010 0.0047 0.012 0.0046 0.005 0.0049
β AA 0.288 0.0143 0.283 0.0142 0.165 0.0066 0.167 0.0072 0.269 0.0107
β Aa 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
τ aa 0.000 0.0000 0.000D 0.000D 0.000D 0.000D
τ Aa 0.476 0.0610 0.500D 0.500D 0.500D 0.500D
τ AA 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
1. *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).