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

From: Strategies for genotype imputation in composite beef cattle

Scenariosa LD panel FImpute BEAGLE
CR%b R2c CR%b R2c
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
  1. aAs described in the section “Genotype imputation” of “Methods,bCR = Concordance Rate, cR2: Allelic R square
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