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Table 1 Univariate Analyses for Combined Cohort 1, Exam 20 and Cohort 2, Exam 4

From: Strategy and model building in the fourth dimension: a null model for genotype × age interaction as a Gaussian stationary stochastic process

ModelsA

Ln Likelihood

AICB

Evidence RatioC

p-Value

Ratio TestD

1. Polygenic (2)

-6714.557

13433.11

5.7 × 1077

1.35 × 10-5

1 vs. 6

2. P × age (5)

-6537.371

13084.74

128.8433

1.69 × 10-76

1 vs. 2

3. Conα G (4)

-6583.193

13174.39

3.77 × 1021

1.04 × 10-21

3 vs. 2

4. Conβ G (4)

-6542.711

13093.42

9888.592

0.00108

4 vs. 2

5. Conλ (4)

-6538.801

13085.6

198.1198

0.09078

5 vs. 2

6. QTL (3)

-6705.086

13416.17

1.2 × 1074

2.68 × 10-74

6 vs. 7

7. QTL × age (7)

-6530.512

13075.02

1

0.00105

2 vs. 7

8. Conα QTL (6)

-6534.916

13081.83

30.09494

0.00300

8 vs. 7

9. Conβ QTL (6)

-6533.796

13079.59

9.819076

0.01038

9 vs. 7

  1. AModels: QTL: model for polygenic + QTL component. P × age model: polygenic G × age. Con: Constrained parameter for P × age, Q × age, or bivariate model, where the parameters may be α, β, λ, or ρ and where the suffix G indicates the polygenic component. The numbers of parameters per model are in parentheses following each one. BAIC: Akaike's Information Criterion = -2 Ln L (θ| data) + 2K, where θ is a parameter vector and K is the number of parameters. CEvidence Ratio: wmin/wi = exp(Δi/2), where wmin is set to 1, wi = [exp(-Δi /2)]/Σr = 1exp(-Δr/2) and Δi = AICi - AICmin. DModels compared.