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Table 1 Comparison of four genomic methods (BayesB, BayesC, BayesC0 and GBLUP) for QTL detection using different generations (G1 to G7) of training data

From: Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

  BayesB BayesC BayesC0 GBLUP
Training Data chr_Mba %Varb p >0c chr_Mb %Var p >0 chr_Mb %Var chr_Mb %Var
G1-G7 4_78 23.7 1.00 4_78 14.4 1.00 4_77 0.62 4_77 0.62
G1 4_78 9.4 0.82 4_78 2.5 0.68 3_110 0.36 3_110 0.32
G2 4_78 6.1 0.73 4_78 6.1 0.73 Z_39 0.31 Z_39 0.32
G3 4_78 13.8 0.64 4_78 3.1 0.77 Z_9 0.32 Z_27 0.35
G4 4_78 22.7 0.98 4_78 4.1 0.90 Z_27 0.54 Z_27 0.58
G5 4_78 10.4 0.77 4_78 1.1 0.50 Z_27 0.35 Z_27 0.36
G6 4_78 7.1 0.77 3_42 3.2 0.93 3_110 0.38 3_110 0.31
G7 Z_23 4.7 0.77 Z_23 3.6 0.77 Z_23 0.32 Z_23 0.34
  1. aLocalization of the 1 Mb window that explained the largest amount of variance (chromosome_1 Mb window on that chromosome)
  2. bpercentage of variance explained by that window
  3. cproportion of models where this window accounted for more than 0 % of genetic variance
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