Skip to main content

Table 4 Average (standard deviation) imputation accuracy, for different imputation analyses using FImpute

From: Accuracy of genotype imputation in Nelore cattle

Analysis1

SNP chip2

Nb (%) SNPs to be imputed

CORR3

PERC4

1

7 K

435509 (99.1)

0.9257 (0.0346)

90.56 (4.09)

2

50 K

418581 (95.2)

0.9783 (0.0136)

97.14 (1.76)

3

GGP20Ki

426145 (96.9)

0.9771 (0.0143)

96.96 (1.87)

4

GGP75Ki

383426 (87.2)

0.9922 (0.0056)

98.93 (0.76)

5

15K_e

424451 (96.6)

0.9784 (0.0135)

97.15 (1.75)

6

15K_em

424422 (96.5)

0.9820 (0.0120)

97.58 (1.61)

7

15K_el

424422 (96.5)

0.9763 (0.0138)

96.87 (1.77)

8

15K_eml

424422 (96.5)

0.9840 (0.0107)

97.85 (1.43)

9

11a7 K

424305 (96.5)

0.9823 (0.0117)

97.63 (1.54)

10

17a7 K

418025 (95.1)

0.9864 (0.0093)

98.17 (1.24)

11

27a7 K

408204 (92.9)

0.9897 (0.0072)

98.60 (0.97)

12

48a7 K

387005 (88.0)

0.9931 (0.0049)

99.05 (0.67)

  1. 1Imputation analyses using FImpute (considering family information) and 202 young sires as the validation set; the numbers of each analysis refer to those in brackets from Figure 1; 2as described in the section ¿SNP chips¿ of ¿Methods¿; 3CORR: Pearson¿s correlation between imputed and observed genotypes; 4PERC: percentage of correctly imputed genotypes.