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Table 2 Weighted segregation analysis of slopes*

From: Segregation and linkage analysis for longitudinal measurements of a quantitative trait

 

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).