Multi-generational imputation of SNP marker genotypes and accuracy of genomic selection
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Recent Advances in DNA sequencing technologies and the increased availability of high density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programs in livestock, poultry and plant species and to better understand the genetic basis of complex traits. Using this genomic information, more accurate breeding values are obtained. The superiority of genomic selection is possible only when high density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high density panels. In order to reduce the cost of genomic selection, it is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Furthermore, such rate of errors tends to increase with the increase of the generational interval between reference and testing generations. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by the imputation errors; especially their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation on the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between training and validation sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by around 0.5% per generations. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval increases, the imputation accuracies decay, although not at an alarming rate. In absence of updating of the reference population, accuracy of GEBVs decays substantially in one or two generations with a decrease rate of around 20-25% per generation. When the reference population is updated by 1 or 5% every generation, the decay in accuracy was only 8 to 11% for 7 generations using the true and imputed genotypes. These results indicate that imputed genotypes provide a viable alternative, even after several generations, as long the reference and training populations are appropriately updated to reflect the genetic change in the population.