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. 2021 Apr 9;53:34. doi: 10.1186/s12711-021-00626-1

Table 1.

Characteristics of systems for ssGBLUP and ssSNPBLUP

Evaluation Modela #Equations #Iterations Smallest eig. Largest eig. κ b
FIN ssGBLUP (10) 11,373,208 4912 3.27010-6 3.924 1.200106
ssGBLUP (30) 11,373,208 4929 3.26910-6 3.934 1.203106
ssSNPBLUP (10) 11,627,330 5001 3.27010-6 3.921 1.199106
ssSNPBLUP (30) 11,627,330 4937 3.26910-6 3.931 1.202106
KAR ssGBLUP (10) 26,709,604 3902 2.88210-6 5.062 1.757106
ssGBLUP (30) 26,709,604 3968 2.37910-6 5.063 2.128106
ssSNPBLUP (10) 26,861,584 4037 2.81110-6 5.062 1.801106
ssSNPBLUP (30) 26,861,584 3988 2.36610-6 5.063 2.140106
LVD ssGBLUP (10) 96,688,714 5431 6.24810-6 8.635 1.382106
ssSNPBLUP (10) 96,916,684 5888 5.31210-6 8.935 1.682106
LVD + block ssGBLUP (10) 96,688,714 1761 1.67210-5 4.160 2.488105
ssGBLUP (30) 96,688,714 1959 1.29510-5 4.161 3.212105
ssSNPBLUP (10) 96,916,684 2542 1.14010-5 6.201 5.441105
ssSNPBLUP (30) 96,916,684 2336 1.26010-5 5.686 4.513105

aPercentage of variance (due to additive genetic effects) explained by residual polygenic effects

bκ = Effective spectral condition number of the preconditioned coefficient matrix