Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle

Binsbergen, R. van; Bink, M.C.A.M.; Calus, M.P.L.; Eeuwijk, F.A. van; Hayes, Ben J.; Hulsegge, Ina; Veerkamp, R.F.

doi:10.1186/1297-9686-46-41

Cited by 144 publications

(199 citation statements)

References 40 publications

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“…As MAF increased, CRs of all methods for imputing genotypes ''AB'' and ''BB'' increased. The trends of imputation accuracy with MAF classes were consistent with reports from other studies in maize populations [56] and whole-genome sequencing Holstein-Friesian cattle [59]. Greater differences among different methods were observed across variant MAF classes in the CRs of genotypes ''AB'' and ''BB''.…”

Section: Effect Of Minor Allele Frequency (Maf) On Accuracy Of Genotysupporting

confidence: 87%

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: Effect Of Minor Allele Frequency (Maf) On Accuracy Of Genotysupporting

confidence: 87%

Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle

Lin

Stothard

2016

Springer Science Reviews

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“…Generally, the use of allele dosages (values ranging from 0 to 2) instead of the most probable genotypes have been deemed to be a way to reduce the overall effect of poorly imputed SNP (Marchini and Howie, 2010;Calus et al, 2014). This lowered the accuracy of imputing rare haplotypes and the overall imputation accuracy has been lower than those of HD-777K or 50K Brøndum et al, 2014;van Binsbergen et al, 2014). However, the use of dosages might not be satisfactory.…”

Section: Excluding Snp Based On Predicted Accuracy (Allelic R 2 )mentioning

confidence: 99%

Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips

Boison

Santos

Utsunomiya

et al. 2015

Journal of Dairy Science

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Genotype imputation is widely used as a cost-effective strategy in genomic evaluation of cattle. Key determinants of imputation accuracies, such as linkage disequilibrium patterns, marker densities, and ascertainment bias, differ between Bos indicus and Bos taurus breeds. Consequently, there is a need to investigate effectiveness of genotype imputation in indicine breeds. Thus, the objective of the study was to investigate strategies and factors affecting the accuracy of genotype imputation in Gyr (Bos indicus) dairy cattle. Four imputation scenarios were studied using 471 sires and 1,644 dams genotyped on Illumina BovineHD (HD-777K; San Diego, CA) and BovineSNP50 (50K) chips, respectively. Scenarios were based on which reference high-density single nucleotide polymorphism (SNP) panel (HDP) should be adopted [HD-777K, 50K, and GeneSeek GGP-75Ki (Lincoln, NE)]. Depending on the scenario, validation animals had their genotypes masked for one of the lower-density panels: Illumina (3K, 7K, and 50K) and GeneSeek (SGGP-20Ki and GGP-75Ki). We randomly selected 171 sires as reference and 300 as validation for all the scenarios. Additionally, all sires were used as reference and the 1,644 dams were imputed for validation. Genotypes of 98 individuals with 4 and more offspring were completely masked and imputed. Imputation algorithms FImpute and Beagle v3.3 and v4 were used. Imputation accuracies were measured using the correlation and allelic correct rate. FImpute resulted in highest accuracies, whereas Beagle 3.3 gave the least-accurate imputations. Accuracies evaluated as correlation (allelic correct rate) ranged from 0.910 (0.942) to 0.961 (0.974) using 50K as HDP and with 3K (7K) as low-density panels. With GGP-75Ki as HDP, accuracies were moderate for 3K, 7K, and 50K, but high for SGGP-20Ki. The use of HD-777K as HDP resulted in accuracies of 0.888 (3K), 0.941 (7K), 0.980 (SGGP-20Ki), 0.982 (50K), and 0.993 (GGP-75Ki). Ungenotyped individuals were imputed with an average accuracy of 0.970. The average top 5 kinship coefficients between reference and imputed individuals was a strong predictor of imputation accuracy. FImpute was faster and used less memory than Beagle v4. Beagle v4 outperformed Beagle v3.3 in accuracy and speed of computation. A genotyping strategy that uses the HD-777K SNP chip as a reference panel and SGGP-20Ki as the lower-density SNP panel should be adopted as accuracy was high and similar to that of the 50K. However, the effect of using imputed HD-777K genotypes from the SGGP-20Ki on genomic evaluation is yet to be studied.

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“…Moderate to high imputation accuracies were found in cattle (ranging from 0.77 to 0.83) when imputing from a high‐density SNP panel (777 K) to sequence data (van Binsbergen et al . ).…”

Section: Introductionmentioning

confidence: 97%

“…When using imputed sequence data for genomic predictions, the imputation accuracy is a crucial factor in determining a possible increase in prediction accuracy. Moderate to high imputation accuracies were found in cattle (ranging from 0.77 to 0.83) when imputing from a high-density SNP panel (777 K) to sequence data (van Binsbergen et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

Heidaritabar

Calus

Megens

et al. 2016

J Animal Breeding Genetics

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There is an increasing interest in using whole-genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole-genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole-genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non-synonymous and non-coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non-synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.

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Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle

Cited by 144 publications

References 40 publications

Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle

Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle

Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

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