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Table 4 Predictive abilities for cross-validation within (W) or between (B) families for weight traits

From: Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

Trait1 W6 W6m W10
Model2 W3 B4 W3 B4 W3 B5
(1) 0.62 0.15 0.62 0.15 0.53 0.19
(2) 100% 0.63 0.24 0.63 0.23 0.57 0.29
(2) 70% 0.65 0.26 0.65 0.26 0.58 0.31
(2) 40% 0.65 0.27 0.65 0.27 0.59 0.32
(2) 10% 0.64 0.24 0.64 0.25 0.58 0.33
(2) 7.5% 0.64 0.24 0.64 0.24 0.58 0.33
(2) 5% 0.64 0.22 0.64 0.23 0.57 0.31
(2) 2.5% 0.63 0.20 0.63 0.20 0.56 0.31
(3) 100% 0.64 0.25 0.64 0.25 0.58 0.31
(3) 70% 0.65 0.27 0.65 0.27 0.59 0.33
(3) 40% 0.65 0.27 0.65 0.27 0.59 0.34
(3) 10% 0.65 0.27 0.64 0.25 0.59 0.34
(3) 7.5% 0.65 0.26 0.64 0.25 0.59 0.34
(3) 5% 0.65 0.25 0.64 0.24 0.58 0.33
(3) 2.5% 0.64 0.24 0.63 0.23 0.57 0.31
  1. 1: Trait: W6 = Weight at week 6; W6m = Weight at week 6, missing maker genotypes were treated as 3rd allele; W10 = Weight at week 10.
  2. 2. Model: (1) = polygenic; (2) = genomic; (3) = polygenic and genomic; with 100/10/2.5% of the markers allowed to have an effect.
  3. 3. W: cross-validation within families (all s.e. ≤ 0.01).
  4. 4. B: cross-validation between families (all s.e. ≤ 0.03).
  5. 5. B: cross-validation between families (all s.e. ≤ 0.04).