ArticlePloS one2014
Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Article
- Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population.Evolutionary applications · 2024Article
- Restriction site-associated DNA sequencing technologies as an alternative to low-density SNP chips for genomic selection: a simulation study in layer chickens.BMC genomics · 2023Article
- Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population.Animals : an open access journal from MDPI · 2023Article
- Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study.Animals : an open access journal from MDPI · 2023Article
- Genomic selection to improve husk tightness based on genomic molecular markers in maize.Frontiers in plant science · 2023Article
- Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.Planta · 2022Review
- First genomic prediction and genome-wide association for complex growth-related traits in Rock Bream (Evolutionary applications · 2022Article
- Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfishFrontiers in genetics · 2022Article
- Accuracy of genotype imputation based on reference population size and marker density in Hanwoo cattle.Journal of animal science and technology · 2021Article
- Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels.BMC genomics · 2021Article
- FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.Frontiers in genetics · 2021Article
- Interest of using imputation for genomic evaluation in layer chicken.Poultry science · 2020Article
- Optimizing genomic selection for blight resistance in American chestnut backcross populations: A trade-off with American chestnut ancestry implies resistance is polygenic.Evolutionary applications · 2020Article
- Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia (G3 (Bethesda, Md.) · 2019Article
- Genomic prediction of the polled and horned phenotypes in Merino sheep.Genetics, selection, evolution : GSE · 2018Article
- Genomic Prediction Accuracy for Resistance AgainstG3 (Bethesda, Md.) · 2018Article
- Article
- Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population.Journal of animal science and biotechnology · 2018Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%, 33% and 66% of the animals were randomly selected from the training sets to have low density genotypes which were then imputed into 50 k genotypes. A GBLUP and a Bayesian method were used to predict direct genomic values (DGV) for validation animals using imputed or their actual 50 k genotypes. Traits studied included milk yield, fat percentage, protein percentage and somatic cell score (SCS). Results showed that performance of both GBLUP and Bayesian methods was influenced by imputation errors. For traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy due to imputation errors than GBLUP. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of using the 50 k panel even when 66% of the training population was genotyped on the 6 k panel. These results justified the application of the 6 k panel for genomic prediction. Imputations from lower density panels were more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy.
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