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Table 2 The estimated heritability for different traits in Bayesian and GBLUP models based on the reference dataset

From: Genomic prediction based on selected variants from imputed whole-genome sequence data in Australian sheep populations

Model

Trait

CCFAT

CEMD

PEMD

IMF

SF5

PWT

YCFW

YFD

Bayesian

 BayesR (top)

0.094 

0.065

0.129

0.163

0.105

0.112

0.219

0.611

 BayesR (50k)

0.196

0.155

0.228

0.376

0.189

0.219

0.393

0.547

 BayesR (50k + top)

0.201

0.160

0.242

0.378

0.189

0.224

0.400

0.634

 BayesRC (50k + top)

0.190

0.159

0.230

0.359

0.161

0.217

0.384

0.672

 BayesR (HD)

0.224

0.185

0.274

0.409

0.222

0.242

0.433

0.596

GBLUP

 GBLUP (top)

0.102

0.070

0.134

0.166

0.108

0.116

0.234

0.619

 GBLUP (50k)

0.200

0.150

0.229

0.380

0.189

0.216

0.399

0.569

 GBLUP (50k + top [1GRM])

0.217

0.158

0.250

0.400

0.203

0.232

0.409

0.679

 GBLUP (50k + top [2GRMs])a

0.191 (0.116 + 0.075)

0.152 (0.111 + 0.041)

0.226 (0.128 + 0.098)

0.356 (0.252 + 0.105)

0.142 (0.046 + 0.096)

0.212 (0.152 + 0.060)

0.388 (0.271 + 0.117)

0.681 (0.154 + 0.527)

 GBLUP (HD)

0.232

0.182

0.275

0.423

0.233

0.250

0.439

0.620

 GBLUP (WGS)

0.231

0.191

0.285

0.439

0.240

0.254

0.453

0.643

  1. CCFAT carcass fat depth at C site, CEMD carcass eye muscle depth, PEMD post-weaning eye muscle depth, IMF intermuscular fat percentage, SF5 shear force measured at day 5 after slaughter, PWT post-weaning weight, YCFW yearling clean fleece weight, YFD yearling fiber diameter
  2. aThe genetic variance explained by two GRM fitted in the model were divided to the phenotypic variance and then were added up to calculate the overal heritability. The first and the second value in the parentheses are the heritability estimates related to 50k and top SNPs, respectively