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

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

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: Trait: W6 = Weight at week 6; W6m = Weight at week 6, missing maker genotypes were treated as 3^rd allele; W10 = Weight at week 10.
2. Model: (1) = polygenic; (2) = genomic; (3) = polygenic and genomic; with 100/10/2.5% of the markers allowed to have an effect.
3. W: cross-validation within families (all s.e. ≤ 0.01).
4. B: cross-validation between families (all s.e. ≤ 0.03).
5. B: cross-validation between families (all s.e. ≤ 0.04).

ISSN: 2730-6844