Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction

Abstract

BackgroundThe objectives of this study were to investigate the accuracy of genotype imputation from low (12k) to medium (50k Illumina-Ovine) SNP (single nucleotide polymorphism) densities in purebred and crossbred Merino sheep based on a random or selected reference set and to evaluate the impact of using imputed genotypes on accuracy of genomic prediction.MethodsImputation validation sets were composed of random purebred or crossbred Merinos, while imputation reference sets were of variable sizes and included random purebred or crossbred Merinos or a group of animals that were selected based on high genetic relatedness to animals in the validation set. The Beagle software program was used for imputation and accuracy of imputation was assessed based on the Pearson correlation coefficient between observed and imputed genotypes. Genomic evaluation was performed based on genomic best linear unbiased prediction and its accuracy was evaluated as the Pearson correlation coefficient between genomic estimated breeding values using either observed (12k/50k) or imputed genotypes with varying levels of imputation accuracy and accurate estimated breeding values based on progeny-tests.ResultsImputation accuracy increased as the size of the reference set increased. However, accuracy was higher for purebred Merinos that were imputed from other purebred Merinos (on average 0.90 to 0.95 based on 1000 to 3000 animals) than from crossbred Merinos (0.78 to 0.87 based on 1000 to 3000 animals) or from non-Merino purebreds (on average 0.50). The imputation accuracy for crossbred Merinos based on 1000 to 3000 other crossbred Merino ranged from 0.86 to 0.88. Considerably higher imputation accuracy was observed when a selected reference set with a high genetic relationship to target animals was used vs. a random reference set of the same size (0.96 vs. 0.88, respectively). Accuracy of genomic prediction based on 50k genotypes imputed with high accuracy (0.88 to 0.99) decreased only slightly (0.0 to 0.67 % across traits) compared to using observed 50k genotypes. Accuracy of genomic prediction based on observed 12k genotypes was higher than accuracy based on lowly accurate (0.62 to 0.86) imputed 50k genotypes.

Harnessing genomic information for livestock improvement

“…M e is approximately 6,000 in Holstein-Friesian cattle and 45,000 in Merino sheep. assuming the same-sized reference population, genomic estimated Bv (GeBv) will be more accurate in cattle [205][206][207] . Note that this formula for the accuracy of GBLuP is population-based -M e is the number of independent chromosome segments in the population.…”

Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population

“…Several panels that vary in the number of SNPs they include are currently available on the market and the number of genotyped individuals is rapidly growing in livestock sectors due to the reduction in costs and the development of new genotyping tools [ 9 ]. Although the imputation efficiency of each SNP panel is well documented [ 1 , 18 , 19 ], few articles evaluated imputation accuracy across different panels using both crossbred and purebred populations [ 20 , 21 ] and, more specifically, strategies for the prediction of imputation accuracy are scarce.…”

Accuracy of genotype imputation in Labrador Retrievers

“…; Moghaddar et al . ). This can be explained by a decreased number of haplotypes in the reference population that overlap with the haplotypes in the validation population.…”