Table 2 Summary of the ω estimation and likelihood ratio test (2ΔL) between two-ratio (ω 0 ≠ ω 1 = ω 2) and three-ratio (ω 0 ≠ ω 1 ≠ ω 2) models.

From: Functional divergence and intron variability during evolution of angiosperm TERMINAL FLOWER1 (TFL1) genes

Hypothesis

np

lnL

2ΔL

p

ω

Supporting hypothesis in Table 1

TFL1 vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

135

−12594.3259

  

ω 0 = 0.1044, ω 1 = ω 2 = 0.13221

ω 1 ≠ ω 2

136

−12593.9631

0.7256

0.3258

ω 0 = 0.1021, ω 1 = 0.1361, ω 2 = 0.1678

CEN vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

135

−12597.2165

  

ω 0 = 0.1173, ω 1 = ω 2 = 0.1072

ω 1 ≠ ω 2

136

−12597.2164

0.0002

0.9887

ω 0 = 0.1054, ω 1 = 0.1193, ω 2 = 0.1674

RCN1 vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

135

−12596.0957

  

ω 0 = 0.1170, ω 1 = ω 2 = 0.0891

ω 1 ≠ ω 2

136

−12595.6616

0.8682

0.2774

ω 0 = 0.1140, ω 1 = 0.0841, ω 2 = 0.1665

RCN2 vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

135

−12597.6561

  

ω 0 = 0.1137, ω 1 = ω 2 = 0.1164

ω 1 ≠ ω 2

136

−12597.5681

0.176

0.8708

ω 0 = 0.1137, ω 1 = 0.1078, ω 2 = 0.1240

RCN3 vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

135

−12597.2484

  

ω 0 = 0.1126, ω 1 = ω 2 = 0.1314

ω 1 ≠ ω 2

136

−12596.6628

1.1712

0.2052

ω 0 = 0.1126, ω 1 = 0.1079, ω 2 = 0.1535

Eudicots vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

135

−12592.2355

  

ω 0 = 0.0915, ω 1 = ω 2 = 0.1255

ω 1 ≠ ω 2

136

−12592.0526

0.3658

0.5494

ω 0 = 0.0916, ω 1 = 0.1085, ω 2 = 0.1264

Monocots vs. magnoliids

1. Functional constraint hypothesis

ω 1 = ω 2

65

−12592.9536

  

ω 0 = 0.1249, ω 1 = ω 2 = 0.0931

ω 1 ≠ ω 2

66

−12592.7766

0.354

0.5617

ω 0 = 0.1249, ω 1 = 0.1073, ω 2 = 0.0917

  1. ω 1, ω 2, and ω 0 are the Ka/Ks ratio of the branches of the eudicot TFL1 (or eudicot CEN, monocot RCNs), magnoliid TFL1-like, and background lineages, respectively.
  2. np: number of parameters
  3. p: p-value obtained from fitted model using χ 2 test.
Back to article page