Table 3 Predictive ability considering SNP markers selected from GWAS, explaining more than 0.5% and more than 1.0% of the genetic variance for growth (BWSel and ADG) and carcass-related (REA, BF, and RF) traits using GBLUP, bayesb, and ENet approaches.

From: Variable selection strategies for genomic prediction of growth and carcass related traits in experimental Nellore cattle herds under different selection criteria

Trait1

GBLUP

BayesB

ENet

0.50%

1.00%

0.50%

1.00%

0.50%

1.00%

Acc

Slope

Acc

Slope

Acc

Slope

Acc

Slope

Acc

Slope

Acc

Slope

BWSel

0.72

0.97

0.70

0.90

0.70

0.95

0.68

1.12

0.80

0.97

0.78

1.04

ADG

0.70

0.97

0.67

0.89

0.69

0.94

0.65

1.11

0.79

1.01

0.76

1.09

REA

0.74

0.97

0.72

0.92

0.72

1.04

0.67

1.14

0.83

1.02

0.81

1.09

BF

0.66

0.98

0.65

0.94

0.64

1.06

0.62

1.13

0.79

0.99

0.75

1.03

RF

0.75

0.97

0.75

0.92

0.72

0.95

0.69

1.13

0.83

0.99

0.82

1.06

  1. Predictive ability, cor\(\:({\widehat{\text{y}}}_{\text{i}}^{\text{*}},{y}_{i}^{*})\)) Pearsons’ correlation between phenotypes adjusted for fixed effects (\(\:{y}_{i}^{*}\)) and predicted adjusted phenotype (\(\:{\widehat{\text{y}}}_{\text{i}}^{\text{*}}\)).
  2. BWSel, Body weight at selection; ADG, average daily gain during the feed trial; REA, rib eye area obtained by ultrasound; BF, subcutaneous backfat thickness obtained by ultrasound; and RF, rump fat thickness obtained by ultrasound.
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