Table 2 Weighted segregation analysis of slopes*

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 3.205 0.1814 3.500 0.1932 3.790 0.2246 4.139 0.1489 3.744 0.2081 4.265 0.1485
β cohort -3.541 0.2393 -3.819 0.2094 -3.785 0.2092 -3.788 0.2143 -3.793 0.2090 -3.726 0.2113
β Sex -1.621 0.1981 -1.623 0.1965 -1.584 0.2001 -1.682 0.1907 -1.580 0.1897 -1.620 0.1936
β AA 16.614 1.7795 16.625 2.4421 6.742 1.2112 14.296 2.1622 12.821 2.1109
β Aa 4.443 0.5003 3.525 0.8312 6.742A 0.000B 6.411C
q A 0.199 0.0584 0.110 0.0265 0.042 0.0195 0.130 0.0269 0.047 0.0188
σ2 0.485 0.1782 1.849 0.5964 2.384 0.7088 3.384 0.5886 2.206 0.5857 4.949 0.6492
τ aa 0.000 0.0000 0.000D 0.000D 0.000D 0.000D
τ Aa 0.390 0.0694 0.500D 0.500D 0.500D 0.500D
τ AA 0.000 0.0000 1.000D 1.000D 1.000D 1.000D
-2(log-likelihood) 17811.58 17824.66 17839.45 17828.49 17837.35 17867.83
p-valueE < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
AICF 17831.58 17838.66 17851.45 17840.49 17849.35 17875.83
1. *The outcome being modeled in equation (2) is 1000 × b i , the subject-specific slope from equation (1). AConstrained to equal β AA . BConstrained to equal 0. CConstrained to equal 1/2 β AA . DParameter 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).