Strategies for genotype imputation in composite beef cattle

BMC Genomic Data

Table 4 Imputation accuracy from low-density panel to high-density panel using FImpute and BEAGLE software

Scenarios^a	LD panel	FImpute		BEAGLE
Scenarios^a	LD panel	CR%^b	R^2c	CR%^b	R^2c
S1	3K	75.70	0.59	66.27	0.44
	6K	87.72	0.79	80.79	0.68
	GGP9K	88.64	0.81	82.19	0.70
	GGP20Ki	92.43	0.87	87.50	0.71
	50K	95.20	0.92	92.14	0.87
	GGP75Ki	96.68	0.94	95.03	0.92
	GGP80K	96.96	0.95	95.26	0.92
S2	3K	62.86	0.37	59.73	0.33
	6K	76.17	0.58	72.23	0.58
	GGP9K	77.54	0.61	73.78	0.55
	GGP20Ki	83.61	0.71	79.75	0.65
	50K	89.55	0.82	86.66	0.77
	GGP75Ki	92.48	0.87	90.85	0.84
	GGP80K	93.24	0.88	91.51	0.85
S3	3K	60.21	0.33	54.83	0.25
	6K	71.46	0.51	63.00	0.38
	GGP9K	72.93	0.54	64.15	0.40
	GGP20Ki	79.19	0.65	69.91	0.49
	50K	85.92	0.76	79.95	0.66
	GGP75Ki	89.54	0.82	85.79	0.76
	GGP80K	90.60	0.84	87.35	0.79
S4	3K	72.75	0.53	64.55	0.40
	6K	85.17	0.74	79.32	0.65
	GGP9K	86.12	0.76	80.85	0.67
	GGP20Ki	90.60	0.84	86.55	0.77
	50K	94.12	0.90	91.24	0.85
	GGP75Ki	95.94	0.93	94.36	0.90
	GGP80K	96.28	0.93	94.53	0.91
S5	3K	77.74	0.62	68.57	0.47
	6K	89.84	0.83	83.86	0.73
	GGP9K	90.67	0.84	85.23	0.75
	GGP20Ki	94.15	0.94	90.23	0.84
	50K	96.36	0.90	93.90	0.90
	GGP75Ki	97.55	0.96	96.10	0.94
	GGP80K	97.74	0.96	96.30	0.94
S6	3K	76.52	0.60	65.80	0.43
	6K	88.71	0.81	80.35	0.67
	GGP9K	89.56	0.82	81.71	0.70
	GGP20Ki	93.13	0.88	87.33	0.80
	50K	95.60	0.93	92.23	0.87
	GGP75Ki	96.98	0.95	95.25	0.92
	GGP80K	97.19	0.95	95.40	0.92
S7	3K	78.69	0.64	69.06	0.48
	6K	89.98	0.83	84.16	0.73
	GGP9K	90.76	0.85	85.42	0.76
	GGP20Ki	94.06	0.90	90.20	0.84
	50K	96.27	0.94	93.82	0.90
	GGP75Ki	97.47	0.96	96.04	0.93
	GGP80K	97.66	0.96	96.20	0.94

^aAs described in the section “Genotype imputation” of “Methods,^bCR = Concordance Rate, ^cR²: Allelic R square

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