Accuracy of imputation to whole-genome sequence in sheep

Abstract

BackgroundThe use of whole-genome sequence (WGS) data for genomic prediction and association studies is highly desirable because the causal mutations should be present in the data. The sequencing of 935 sheep from a range of breeds provides the opportunity to impute sheep genotyped with single nucleotide polymorphism (SNP) arrays to WGS. This study evaluated the accuracy of imputation from SNP genotypes to WGS using this reference population of 935 sequenced sheep.ResultsThe accuracy of imputation from the Ovine Infinium® HD BeadChip SNP (~ 500 k) to WGS was assessed for three target breeds: Merino, Poll Dorset and F1 Border Leicester × Merino. Imputation accuracy was highest for the Poll Dorset breed, although there were more Merino individuals in the sequenced reference population than Poll Dorset individuals. In addition, empirical imputation accuracies were higher (by up to 1.7%) when using larger multi-breed reference populations compared to using a smaller single-breed reference population. The mean accuracy of imputation across target breeds using the Minimac3 or the FImpute software was 0.94. The empirical imputation accuracy varied considerably across the genome; six chromosomes carried regions of one or more Mb with a mean imputation accuracy of < 0.7. Imputation accuracy in five variant annotation classes ranged from 0.87 (missense) up to 0.94 (intronic variants), where lower accuracy corresponded to higher proportions of rare alleles. The imputation quality statistic reported from Minimac3 (R2) had a clear positive relationship with the empirical imputation accuracy. Therefore, by first discarding imputed variants with an R2 below 0.4, the mean empirical accuracy across target breeds increased to 0.97. Although accuracy of genomic prediction was less affected by filtering on R2 in a multi-breed population of sheep with imputed WGS, the genomic heritability clearly tended to be lower when using variants with an R2 ≤ 0.4.ConclusionsThe mean imputation accuracy was high for all target breeds and was increased by combining smaller breed sets into a multi-breed reference. We found that the Minimac3 software imputation quality statistic (R2) was a useful indicator of empirical imputation accuracy, enabling removal of very poorly imputed variants before downstream analyses.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0443-5) contains supplementary material, which is available to authorized users.

Genome wide association analysis on semen volume and milk yield using different strategies of imputation to whole genome sequence in French dairy goats

“…4), and thus implies that an imputation error would have a major impact on the final correlation. Nonetheless, the correlations that we obtained even for high MAF were low in comparison with other studies in livestock [3,29,30] or humans [31] where the correlation between true and imputed genotypes could reach 0.8. As our imputation study involved very few sequenced individuals (33 and 40), a single imputation error would drastically reduce this correlation.…”

“…When possible, imputation to a whole-genome sequence is performed in a stepwise manner starting with the lowest density panel, before moving on to a medium density chip, then a highdensity chip and finally imputation to sequence level. In dairy cattle and sheep, this method has proved more efficient than direct imputation from lowest density to sequence [2,3]. In goats, the only genotyping tool available is a 50 k-chip (Illumina GoatSNP50 BeadChip) [4].…”

Genome wide association analysis on semen volume and milk yield using different strategies of imputation to whole genome sequence in French dairy goats

“…This could be linked to the small number of variants with a high MAF (Figure 4), and thus implies that an imputation error would have a major impact on the final correlation. Nonetheless, the correlations that we obtained even for high MAF were low in comparison with other studies in livestock [3,30,31] or humans [32] where the correlation between true and imputed genotypes could reach 0.8. As our imputation study involved very few sequenced individuals (33 and 40), a single imputation error would drastically reduce this correlation.…”

“…When possible, imputation to a whole-genome sequence is performed in a stepwise manner starting with the lowest density panel, before moving on to a medium density chip, then a high-density chip and finally imputation to sequence level. In dairy cattle and sheep, this method has proved more efficient than direct imputation from lowest density to sequence [2,3]. In goats, the only genotyping tool available is a 50k-chip (Illumina GoatSNP50…”

QuAdTrim: Overcoming computational bottlenecks in sequence quality control

“…QuAdTrim was first developed in 2011 for use in a metagenomic study that utilised Illumina paired end sequence data (Ross, et al, 2012). Subsequently QuAdTrim has been used for quality control of Illumina sequence data in from human, bacterial, virus, cattle, sheep, and salmon studies amoung others (Bolormaa, et al, 2019;Daetwyler, et al, 2017;Kamato, et al, 2017;Kijas, et al, 2019;Kijas, et al, 2018;Wei, et al, 2018).…”