Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection

Abstract

Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. 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. 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 to 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. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. 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 at the rate of 20% to 25% per generation. When the reference population is updated by 1% or 5% every generation, the decay in accuracy was 8% to 11% after seven generations using 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.

Using a very low-density SNP panel for genomic selection in a breeding program for sheep

“…In GSs_Icd_pop, the annual genetic gain remained higher (+ 9.5%) than in the classical design but was substantially lower than in GSs_Icd (+ 45%). The contribution to the analysis of co-segregation between VLD and MD SNPs has already been reported in previous studies which highlight the beneficial effect of including relatives in the reference population for imputation [ 21 , 24 , 26 , 27 , 29 – 31 , 34 ], especially sires and grandsires [ 24 ]. The effect of including close relatives in the reference population on genomic prediction accuracy was also pointed out by previous studies on simulated and real data [ 38 , 56 ].…”

“…Population-based methods use the linkage disequilibrium (LD) between SNPs and haplotype frequencies only, whereas population- and family-based methods also include co-segregation information based on pedigree. The factors that affect imputation accuracy [ 21 – 35 ] and the relation between imputation accuracy and genomic prediction quality [ 21 , 25 – 27 , 30 , 31 , 34 , 35 ] are well documented. In sheep, Moghaddar et al [ 31 ] found a correlation close to 1 between GEBV computed from real versus imputed genotypes with an average imputation accuracy of 0.96.…”

“…In sheep, Moghaddar et al [ 31 ] found a correlation close to 1 between GEBV computed from real versus imputed genotypes with an average imputation accuracy of 0.96. The imputation accuracy depends on: (1) the characteristics of the low-density panel with respect to the minor allele frequency, the number, spacing and localization of SNPs [ 21 – 27 , 30 , 33 , 34 ]; (2) the linkage between adjacent SNPs [ 22 , 24 ]; and (3) the characteristics of the reference population including size, single or multi-breed population and relationship with imputed animals [ 21 , 24 , 26 , 27 , 29 – 31 , 34 ]. As observed with genomic prediction accuracy, imputation accuracy increases as close relatives are included in the reference population and pedigree information is used [ 21 , 24 , 26 , 29 , 30 , 34 ].…”

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

“…We used the FImpute software (v 2.2) [11], as many studies have already pinpointed its good performance for imputation when compared to many other alternatives [16,35,43,44]. FImpute can use different sizes of rolling windows with a given overlap to scan the genomes of target and reference datasets.…”

Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population

“…We used the FImpute software (v 2.2) [11], as many studies have already pinpointed its good performance for imputation when compared to many other alternatives [16, 35, 43, 44]. FImpute can use different sizes of rolling windows with a given overlap to scan the genomes of target and reference datasets.…”