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Table 1 Genomic predictive accuracies obtained using FBM-BayesA, TBM-BayesA, FBM-BayesCπ, TBM-BayesCπ, and GBLUP in Scenario 1

From: Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis

Chains FBM-BayesA TBM-BayesA FBM-BayesCπ TBM-BayesCπ GBLUP
1 0.5239 0.5231 0.6316 0.6317 0.6016
2 0.5227 0.5229 0.6301 0.6296
4 0.5230 0.5229 0.6304 0.6314
6 0.5230 0.5230 0.6310 0.6304
8 0.5231 0.5232 0.6313 0.6311
10 0.5232 0.5231 0.6309 0.6307
12 0.5228 0.5232 0.6315 0.6305
14 0.5230 0.5231 0.6306 0.6313
16 0.5230 0.5231 0.6307 0.6303
18 0.5230 0.5231 0.6310 0.6311
  1. FBM-BayesA fixed burn-in, multiple-chain BayesA, TBM-BayesA tunable burn-in, multiple-chain BayesA, FBM-BayesCπ fixed burn-in, multiple-chain BayesCπ, TBM-BayesCπ tunable burn-in, multiple-chain BayesCπ, and Chains number of parallel MCMC running for each genomic prediction model. Simulation parameters are as follows: population size = 1000; number of QTL = 200; heritability = 0.1; number of chromosomes = 5; and number of markers per chromosome = 4000