Accuracy of genome-wide imputation in Braford and Hereford beef cattle

Piccoli, M. L.; Braccini, José; Cardoso, F. F.; Sargolzaei, M; Larmer, Steven; Schenkel, F.S.

doi:10.1186/s12863-014-0157-9

Cited by 29 publications

(37 citation statements)

References 34 publications

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“…Previous studies on Holstein dairy cattle for imputation from 6K to 50K show an overall CR over 93% with Beagle 3.1.0 [67], over 97% with Fimpute [7], over 98% with Fimpute [68]; from 6K to 50K, our findings with several purebred/crossbred beef populations (overall mean CR 91.88% with FImpute) were similar to the ones from beef cattle reported by Piccoli et al [69], Ventura et al [70], and Chud et al [71]. Accuracies of imputation were in general higher in Holstein dairy breeds than in beef breeds based on previous reports and our studies as levels of LD were higher in Holstein dairy breeds than in beef breeds as Holsteins have a relatively small effective population size [72].…”

Section: Discussionsupporting

confidence: 88%

Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle

Lin

Stothard

2016

Springer Science Reviews

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In this study, we reviewed six imputation methods (Impute 2, FImpute 2.2, Beagle 4.1, Beagle 3.3.2, MaCH, and Bimbam) and evaluated the accuracy of imputation from simulated 6K bovine SNPs to 50K SNPs with 1800 beef cattle from two purebred and four crossbred populations and the impact of imputed genotypes on performance of genomic predictions for residual feed intake (RFI) in beef cattle. Accuracy of imputation was reported in both concordance rate (CR) and allelic r 2 and assessed via fivefold cross-validations. Running times of different methods were compared. Impute 2, FImpute and Beagle 4.1 yielded the most accurate imputation results (with CR [ 91%). FImpute was the fastest and had advantages over all other methods in imputing rare variants. Minor allele frequency (MAF) and genetic relatedness between individuals in reference and validation populations can affect accuracy of imputation. For all methods, imputation accuracy for genotypes carrying the minor allele increases as the MAF increases. Impute 2 outperformed all other methods on MAF [ 5% and onwards. FImpute and Impute 2 that adopted the nearest neighbour scheme coped better with individuals of distant relativeness. Bimbam yielded the poorest CR (76%) due to admixed reference panels. Imputed genotypes and actual 50K/6K genotypes were employed to predict genomic breeding values (GEBVs) of RFI using a Bayesian method and GBLUP. Accuracies of GEBV were similar using actual 50K genotypes or imputed genotypes, except those from Bimbam, and the imputation errors had minimal impact on the genomic predictions.

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Section: Discussionsupporting

confidence: 88%

Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle

Lin

Stothard

2016

Springer Science Reviews

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“…The quality control for imputation to the 777 K SNP panel was the same as the one described before for the imputation to the 50 K SNP panel. The 8 K and 15 K SNP chip panels were used for imputation to 50 K SNP chip panel, and the 50 K SNP panel was used for imputation to the 777 K SNP panel [19] using FImpute v.2.2 [24]. A quality control as described before plus Minor Allele Frequency (MAF ≥ 0.05) was implemented for the genomic prediction of breeding values.…”

Section: Methodsmentioning

confidence: 99%

“…The first two groups of scenarios were created based on animals genotyped (mimicked from 50 K SNP chip panel) with 8 K and 15 K SNP chip panels and imputed to the 50 K SNP chip panel [19]. For this study, the two best scenarios based on concordance rate and allelic R 2 were used: 8 K scenario (concordance rate: 0.952 and allelic R 2 : 0,927) and 15 K scenario (concordance rate: 0.973 and allelic R 2 : 0,962).…”

Section: Methodsmentioning

confidence: 99%

“…An alternative to reduce costs is to genotype individuals from commercial breeding programs with low-density single nucleotide polymorphisms (SNP) chip panels, which are more affordable for the producers. These low-density panels can then be imputed to a medium or high density SNP chip panel [18, 19] and used to predict genomic breeding values of the animals [20, 21]. It is important to investigate the impact of using imputed genotypes for genomic prediction of breeding values.…”

Section: Introductionmentioning

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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“…Based on previous evaluations of methodologies for GWS, GWAS, and QTL mapping conducted with simulation studies (Azevedo et al 2015, Piccoli et al 2014, Ribeiro et al 2005, we assessed the performance of the EVG method or population structure correction in terms of efficiency in estimating the free genomic values of relationships using simulated data under different scenarios (i.e., 2 heritability levels × 2 genetic architectures). We compared the proposed EVG method with other methods (i.e., BLUP, DP, and PC) in terms of prediction accuracy obtained from each scenario.…”

Section: Introductionmentioning

confidence: 99%