Short communication: effect of mutation age on genomic predictions.

Genomic selection relies on the whole-genome evaluation of single nucleotide polymorphisms (SNP), some of them linked to quantitative trait loci (QTL). Although statistical methodology has been developed for the analysis of genomic data, little is known about the performance of SNP association studies when trying to capture variability from QTL mutations of different ages. Within this context, the influence of mutation age was analyzed under a simulation design, assuming presence or absence of selection on mutant QTL alleles. Focusing on a unique chromosome with a single QTL located in the proximal end, the performance of the genomic selection analyses was evaluated in terms of standardized mean square error (MSE). For all simulation scenarios, MSE was highest for the youngest mutations. The MSE was progressively reduced with mutation age under random mating and soft selection, and reached its maximum performance with the oldest mutations. On the other hand, moderate and strong selection caused a quick reduction of the MSE from youngest mutations to mutations arising in generations 920 to 939, thus resulting in a progressive increase for older mutations. In both cases, very young mutations escaped from genomic selection analyses, releasing a relevant amount of genetic variability that could not be captured and used in genomic selection programs. This demonstrated the need for new analytical approaches to model relevant and recent sources of variation; if captured, these young mutations could substantially contribute to current breeding schemes.