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Table 7 Log likelihood ratio statistics between models and the variance accounted for by the X chromosome and by residual polygenic effect, based on the real 54K dataset

From: Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers

Traits Log likelihood ratio Variance (SE) Variance %e
(A + X)/Aa (AX + P)/AXb X-Chrc Pold X-Chrc Pold
Milk 13.46* 16.62* 1.05 (0.48)* 14.34 (3.74)* 0.9 12.0
Fat 27.34* 8.03* 1.41 (0.53)* 9.27 (3.51)* 1.3 8.4
Protein 27.07* 34.62* 1.80 (0.62)* 20.54 (3.74)* 1.5 17.3
Growth 0.00 16.87* 0.00 (0.28) 17.84 (4.67)* 0.0 13.5
Fertility 27.59* 33.85* 5.21 (1.66)* 42.81 (8.19)* 3.6 27.9
Birth index 3.93* 2.76¤ 0.93 (0.68) 9.09 (6.14)* 0.8 7.7
Calving index 0.66 0.80 0.73 (0.86) 6.51 (7.21)* 0.7 5.9
Udder health 21.96* 18.6* 2.44 (0.84)* 16.58 (4.17)* 2.7 17.6
Other diseases 26.05* 47.93* 6.13 (2.13)* 70.01 (11.21)* 4.1 40.4
Body conformation 4.12* 5.08* 2.71 (1.42)* 15.82 (7.46)* 2.2 12.7
Feet and legs 3.62¤ 0.00 2.16 (1.60) 0.00 (9.97) 1.5 0.0
Udder conformation 9.60* 0.05 2.52 (1.10)* 1.34 (5.76) 1.8 1.2
Milking ability 9.97* 10.40* 2.57 (1.28)* 23.66 (8.01)* 1.2 11.0
Temperament 5.23* 22.22* 3.36 (1.78)* 43.94 (10.30)* 2.5 29.8
Longevity 3.87* 118.57* 1.07 (0.97) 87.50 (9.37)* 0.8 53.4
Average 12.10 22.43 2.27 (1.08) 25.28 (6.90) 1.7 17.2
  1. aLog likelihood ratio of model G(A) + G(X) to model G(A), where G(A) was the model with an autosomal G matrix and G(A) + G(X) was the model including an autosome G matrix and an X chromosome G matrix; bLog likelihood ratio of model Gc(A + X) + Pol to model Gc(A + X), where Gc(A + X) was the model with a G matrix built using all markers and Gc(A + X) + Pol included also residual polygenic effect; cVariance accounted by the X chromosome and estimated from model G(A) + G(X); dVariance of residual polygenic effect and estimated from model Gc(A + X) + Pol; eVariance in proportion to total additive genetic variance; *Significant at P < 0.05, where P was calculated as P( χ df = 1 2 ); ¤Significant at Pm < 0.05, where Pm was calculated as 0.5P( χ df = 1 2 ), e.g., when P < 0.05, Pm < 0.025.