Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

Abstract

Background Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy. Results The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R2dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants. Conclusions Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants.

The impact of reference composition and genome build on the accuracy of genotype imputation in Australian Angus cattle

“…A larger number of regions are expected to benefit from updating mapping positions to the new assembly if a higher density of markers is used. Using a genome scan of improved imputation accuracies in an imputation from 50 000 to 777 000, Hermisdorff et al (2020) reported more misplaced regions for UMD assembly than those from our study. The current study focused on the imputation of many lower density chips to a medium density set and, therefore, the identified regions in our study are influenced by a lower SNP density and limited to those regions covered by the panels in the reference.…”

Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans

“…In this study, we reported the estimated imputation accuracy from Minimac4 instead of calculation of the empirical imputation accuracy. Previous study have shown that the R-sq values estimated by Minimac3 (same as Minimac4) were highly correlated with correlation-based empirical measures [ 46 , 47 ]. Of course, using the estimated accuracy could limit the direct comparison between studies.…”