Costa, R. B. | Search bioRxiv

bioRxiv2020

Important effect of the X chromosome in genomic evaluations of reproductive traits in beef cattle

The aim of this study was to evaluate the efficiency of inclusion and the prediction ability of the X chromosome for reproductive (occurrence of early pregnancy - P16 and age at first calving - AFC) and andrological traits (scrotal circumference -SC) in a herd of Nellore beef cattle herd. 3,263 genotypes of females and males were used. Genomic prediction for SC, AFC and P16 was carried out considering two scenarios: 1) only autosomal markers or 2) autosomal + X chromosome markers. To evaluate the effect of inclusion of the X chromosome on selection, the responses to the selection performed were compared including or not the X chromosome in the evaluation of the traits. Higher heritability estimates were obtained for SC (0.40 and 0.31), AFC (0.11 and 0.09) and P16 (0.43 and 0.38) for the analyses including the X chromosome compared to those without. The percent reduction on mean genomic breeding values when selection was based on the results of analysis that did not include the X chromosome to 1, 5 and 10% of the top males, was for SC slightly more than 7% of the mean genomic breeding value of the selected animals. For P16, the loss can reach more than 4%, while this loss does not seem to be as important for AFC. Average predictive correlation of 0.79, 0.98 and 0.84 for SC, AFC and P16 was obtained, respectively. These estimates demonstrate that inclusion of the X chromosome in the analysis can improve the prediction of genomic breeding values, especially for SC.

genomics↗

bioRxiv2020

Investigating the accuracy of imputed genotypes in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

BackgroundImputation accuracy among other things depends on the size of the reference panel, the markers minor allele frequency (MAF), and the correct placement of variants on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50K to 777K SNP density, using map positions 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 empirical imputation accuracy. ResultsThe overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was virtually 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 and the marker density in the imputed data increased from 0.903 to 0.913 and from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals, respectively. The model-based imputation quality scores from Minimac3 (Rsq) were highly correlated to but systematically higher than empirically estimated accuracies. The correlation between these metrics increased with the size of the reference panel and MAF of imputed variants. ConclusionsAccurate 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 used and MAF of the imputed variants.

genomics↗