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Table 3 Accuracy comparisons between the two-step piecemeal and the classic one-step imputation on the Simmental datasets

From: Whole genome SNP genotype piecemeal imputation

 

5-Fold cross validation

 

Independent testing

BaseProgram

Imputation

a c c 1

a c c π

+

#Clusters

#TClusters

a c c 1

a c c π

+

 

6 K →50 K

69.35 %

70.81 %

1.46 %

100

100

60.68 %

61.39 %

0.71 %

Beagle

6 K →660 K

72.37 %

74.92 %

2.55 %

800

800

66.00 %

67.76 %

1.76 %

 

50 K →660 K

86.61 %

88.89 %

2.28 %

1000

1000

72.83 %

74.11 %

1.29 %

 

6 K →50 K

75.95 %

76.70 %

0.75 %

55

55

61.87 %

62.16 %

0.29 %

FImpute

6 K →660 K

79.11 %

80.11 %

1.00 %

1000

1000

68.43 %

68.95 %

0.52 %

 

50 K →660 K

90.31 %

90.74 %

0.43 %

1000

1999

77.11 %

77.33 %

0.22 %

  1. Results are on the Simmental datasets for markers on chromosome 14. Columns 3–7 contain the 5-fold cross validation results on the 82 animals, with the selected markers and their associated target marker clusters. Independent testing results on the 367 animals are in columns 8–10, using the selected markers and their associated target marker clusters from the cross validation. 1In the independent testing from 50K to 660K, 8 markers of the Affymetrix 660K chip were filtered out due to their genotype disagreeing with the alternating alleles specified by sequencing, and consequently only 999 target marker clusters were used. The columns labelled with + show the improvements, in bold, of the piecemeal imputation over the one-step imputation