Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes

• Published in: BMC genetics Vol. 18; no. 1; p. 2
• Main Authors: Piccoli, Mario L, Brito, Luiz F, Braccini, José, Cardoso, Fernando F, Sargolzaei, Mehdi, Schenkel, Flávio S
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.01.2017; BioMed Central
• Subjects: Accuracy; Analysis; Animals; Beef cattle; Breeding; Cattle - genetics; Cattle industry; Dairy cattle; Genomics - methods; Genotype; Genotype & phenotype; Machine Learning; Phenotype; Quality control; Single nucleotide polymorphisms; Species Specificity; Weaning; Brazil; United States > US; Genomic breeding values; Imputation; Brazilian beef cattle; Genomic selection; Genotyping costs
• ISSN: 1471-2156; 1471-2156
• DOI: 10.1186/s12863-017-0475-9

Genomic selection (GS) has played an important role in cattle breeding programs. However, genotyping prices are still a challenge for implementation of GS in beef cattle and there is still a lack of information about the use of low-density Single Nucleotide Polymorphisms (SNP) chip panels for genomic predictions in breeds such as Brazilian Braford and Hereford. Therefore, this study investigated the effect of using imputed genotypes in the accuracy of genomic predictions for twenty economically important traits in Brazilian Braford and Hereford beef cattle. Various scenarios composed by different percentages of animals with imputed genotypes and different sizes of the training population were compared. De-regressed EBVs (estimated breeding values) were used as pseudo-phenotypes in a Genomic Best Linear Unbiased Prediction (GBLUP) model using two different mimicked panels derived from the 50 K (8 K and 15 K SNP panels), which were subsequently imputed to the 50 K panel. In addition, genomic prediction accuracies generated from a 777 K SNP (imputed from the 50 K SNP) were presented as another alternate scenario. The accuracy of genomic breeding values averaged over the twenty traits ranged from 0.38 to 0.40 across the different scenarios. The average losses in expected genomic estimated breeding values (GEBV) accuracy (accuracy obtained from the inverse of the mixed model equations) relative to the true 50 K genotypes ranged from -0.0007 to -0.0012 and from -0.0002 to -0.0005 when using the 50 K imputed from the 8 K or 15 K, respectively. When using the imputed 777 K panel the average losses in expected GEBV accuracy was -0.0021. The average gain in expected EBVs accuracy by including genomic information when compared to simple BLUP was between 0.02 and 0.03 across scenarios and traits. The percentage of animals with imputed genotypes in the training population did not significantly influence the validation accuracy. However, the size of the training population played a major role in the accuracies of genomic predictions in this population. The losses in the expected accuracies of GEBV due to imputation of genotypes were lower when using the 50 K SNP chip panel imputed from the 15 K compared to the one imputed from the 8 K SNP chip panel.