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

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

Traits	Log likelihood ratio		Variance (SE)		Variance %^e
Traits	(A + X)/A^a	(AX + P)/AX^b	X-Chr^c	Pol^d	X-Chr^c	Pol^d
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

^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 G_c(A + X) + Pol to model G_c(A + X), where G_c(A + X) was the model with a G matrix built using all markers and G_c(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 G_c(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 P_m < 0.05, where P_m was calculated as 0.5P( $χ_{df = 1}^{2}$ ), e.g., when P < 0.05, P_m < 0.025.

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