Accuracy of genomic predictions in Bos indicus (Nellore) cattle

Neves, Haroldo Henrique de Rezende; Carvalheiro, Roberto; O'Brien, Ana M. Pérez; Utsunomiya, Yuri Tani; Carmo, Adriana Santana do; Schenkel, F.S.; Sölkner, Johann; McEwan, John C.; Tassell, Curtis P. Van; Cole, J.B.; Silva, Marcos V. G. B. da; Queiroz, Salete Linhares; Sonstegard, Tad S.; Garcia, José Fernando

doi:10.1186/1297-9686-46-17

Cited by 76 publications

(74 citation statements)

References 43 publications

(86 reference statements)

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“…The results found by Akanno et al [37], working with simulated data in swine strengthens this theory because they found much higher accuracy for the indigenous population (low selection pressure) in comparison with the exotic population (high selection pressure). However, Neves et al [32] studying Brazilian Nellore reported validation accuracy lower than those attained for the NW and NY traits in the current study. This could be due to a stronger selection pressure in Nellore breed compared to Braford and Hereford.…”

Section: Discussioncontrasting

confidence: 87%

“…The validation accuracy for traits in the selection index were lower than values reported in the literature for other breeds such as Angus, Limousin and Simmental [35, 36]. Neves et al [32], working with Brazilian Nellore and the same set of traits (included in the selection index) also reported greater validation accuracies, except for WGBW and CW. The lower values of validation accuracy in this study could be explained by the lower expected EBV accuracies in the training population ( r = 0.64).…”

Section: Discussionmentioning

confidence: 67%

“…The reason for using a combined relationship matrix is due to the fact that previous studies, also performed in Brazil, reported gains in accuracies when adding 20% pedigree-based relationship (e.g. [32]).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2017

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BackgroundGenomic 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.ResultsThe 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-017-0475-9) contains supplementary material, which is available to authorized users.

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Section: Discussioncontrasting

confidence: 87%

Section: Discussionmentioning

confidence: 67%

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

et al. 2017

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“…The genomic selection has the potential to increase the genetic gain for hard measure traits, like the FA proile, however, the most suitable model to evaluate those traits are still being studied. The divergence between studies suggests that the diference within the methods is due to the genetic architecture of the trait that is the accuracy tends to increase as the model adjusts itself to the genetic architecture of the trait [70] (Lund et al, 2009). For traits that are afected by moderate to major genes efect, higher accuracies can be reached through Bayesian methods [71].…”

Section: Genomic Selection and Genome-wide Association Studies For Bementioning

confidence: 99%

Genetic Factors that Determine the Meat Fatty Acids Composition

Lemos¹,

Pereira²,

Regatieri³

et al. 2017

Fatty Acids

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In relation the nutritional atributes of beef meat quality, the composition of faty acid is important not only because it afects the meat palatability, but also it can afect the human health. The faty acids harmful to human health have received atenuating atention in recent years. Some studies, with taurine breed, have shown that there is a genetic variation for the trait faty acid proile of the meat and, therefore, the possibility of genetic improvement of this trait in beef catle. Meantime, in zebu catle, the genetic parameter estimates for faty acid proile are scarce. Furthermore, the trait meat faty acid proile is something diicult and costly to measure and for this kind of trait is indicated the use of genomic selection, which is a type of marker-assisted selection. The objective of this chapter is showing the genetic variability of meat faty acid proile diferent catle breeds and makes an approach of the implement models and methods that use genomic information to improve the faty acid composition of beef meat.

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“…Neves et al (2014) documented the feasibility of using different genetic evaluation and validation methods for genomic selection in Nellore. Carvalheiro et al (2014) proposed the best imputation strategy for increasing the power of genomic information by predicting large SNP panels ( > 700,000) from panels as low as 15,000 SNPs.…”

Section: South Americamentioning

confidence: 99%