We used stochastic simulation to test hypotheses that, (i) phenotyping proportion of high ranking selection candidates based on estimated breeding values (EBV) before genotyping could realize as much genetic gains as phenotyping all candidates, and (ii) there is diminishing return to selection as more candidates are phenotyped in genomic breeding programs. Three phenotyping criteria, namely, random (RS), EBV and true breeding value (TBV) were investigated under two schemes (across-population and within-litter) using traditional-BLUP and genomic-BLUP models. The EBV ranked above RS and realized maximum achievable gains in the breeding program at 80% phenotyping. There was diminishing return to selection as more candidates were phenotyped; indicating that information content is not so large in the last candidate compared to the first candidate phenotyped. These findings demonstrate the need to rank selection candidates and phenotype only top 80% in genomic selection program as no bias was detected.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Genetic Improvement Programs: Breeding objectives, economics of selection schemes, and advances in selection theory, , 026, 2014
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