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 |
βcohort | -3.541 | 0.2393 | -3.819 | 0.2094 | -3.785 | 0.2092 | -3.788 | 0.2143 | -3.793 | 0.2090 | -3.726 | 0.2113 |
βSex | -1.621 | 0.1981 | -1.623 | 0.1965 | -1.584 | 0.2001 | -1.682 | 0.1907 | -1.580 | 0.1897 | -1.620 | 0.1936 |
βAA | 16.614 | 1.7795 | 16.625 | 2.4421 | 6.742 | 1.2112 | 14.296 | 2.1622 | 12.821 | 2.1109 | — | — |
βAa | 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 |
τaa | 0.000 | 0.0000 | 0.000D | — | 0.000D | — | 0.000D | — | 0.000D | — | — | — |
τAa | 0.390 | 0.0694 | 0.500D | — | 0.500D | — | 0.500D | — | 0.500D | — | — | — |
τAA | 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 |
*The outcome being modeled in equation (2) is 1000 × bi, the subject-specific slope from equation (1). AConstrained to equal βAA. BConstrained to equal 0. CConstrained to equal 1/2 βAA. 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).