Low-depth genotyping-by-sequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation

Table 4 Description of datasets used for testing different parameters on the accuracy of imputation of GBS missing data

Dataset ID	Description	Min. read depth	MinMAF^a	Min. call rate	Number of variants	Variants shared with SNP50	Imputation program	Estimated accuracy (%)
9	GBS	3	0.05	0.2	53,556	431	FImpute	71.1
10	GBS + SNP50	3	0.05	0.2	98,398	431	FImpute	84.8
11	GBS	3	0.05	0.3	40,208	334	FImpute	74.2
12	GBS + SNP50	3	0.05	0.3	88,573	334	FImpute	89.1
13	GBS	3	0.05	0.4	21,561	156	FImpute	77.5
14	GBS + SNP50	3	0.05	0.4	70,250	156	FImpute	91.9
15	GBS	4	0.02	0.2	55,727	395	FImpute	73.3
16	GBS + SNP50	4	0.02	0.2	100,297	395	FImpute	86.4
17	GBS	4	0.05	0.2	45,447	369	Beagle	70.9
	GBS	4	0.05	0.2	45,447	369	FImpute	71.2
18	GBS + SNP50	4	0.05	0.2	90.234	369	Beagle	78.5
	GBS + SNP50	4	0.05	0.2	90.234	369	FImpute	86.1
19	GBS	4	0.02	0.3	40,855	278	FImpute	75.9
20	GBS + SNP50	4	0.02	0.3	89,196	278	FImpute	90.3
21	GBS	4	0.05	0.3	31,662	254	Beagle	74.0
	GBS	4	0.05	0.3	31,662	254	FImpute	74.3
22	GBS + SNP50	4	0.05	0.3	80,190	254	Beagle	83.8
	GBS + SNP50	4	0.05	0.3	80,190	254	FImpute	89.3
23	GBS	4	0.02	0.4	22,331	98	FImpute	83.3
24	GBS + SNP50	4	0.02	0.4	70,983	98	FImpute	93.4
25	GBS	4	0.05	0.4	14,909	82	Beagle	81.2
	GBS	4	0.05	0.4	14,909	82	FImpute	80.8
26	GBS + SNP50	4	0.05	0.4	63,706	82	Beagle	89.8
	GBS + SNP50	4	0.05	0.4	63,706	82	FImpute	93.4
27	GBS	5	0.05	0.2	36,913	299	FImpute	70.0
28	GBS + SNP50	5	0.05	0.2	81,454	299	FImpute	85.4
29	GBS	5	0.05	0.3	22,685	166	FImpute	74.8
30	GBS + SNP50	5	0.05	0.3	71,360	166	FImpute	89.5
31	GBS	5	0.05	0.4	8818	42	FImpute	79.5
32	GBS + SNP50	5	0.05	0.4	57,719	42	FImpute	92.2

ISSN: 2730-6844