ArticleBMC genetics2014
Accuracy of genome-wide imputation in Braford and Hereford beef cattle.
Article in BMC genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Genotype imputation performance in Nellore cattle across different SNP panels and software tools.Tropical animal health and production · 2026Article
- Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation.Animal bioscience · 2025Article
- Genome-wide association study for growth traits in Blanco Orejinegro and Romosinuano cattle.Tropical animal health and production · 2023Article
- Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle.Genetics, selection, evolution : GSE · 2022Article
- Imputation of non-genotyped F1 dams to improve genetic gain in swine crossbreeding programs.Journal of animal science · 2022Article
- Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires.Journal of animal science · 2022Article
- Evaluation of imputation accuracy using the combination of two high-density panels in Nelore beef cattle.Scientific reports · 2019Article
- Tag SNP selection for prediction of tick resistance in Brazilian Braford and Hereford cattle breeds using Bayesian methods.Genetics, selection, evolution : GSE · 2017Article
- Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes.BMC genetics · 2017Article
- Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.Genetics, selection, evolution : GSE · 2016Article
- Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population.Asian-Australasian journal of animal sciences · 2016Article
- Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle.BMC genetics · 2016Article
- Ridge, Lasso and Bayesian additive-dominance genomic models.BMC genetics · 2015Article
- Strategies for genotype imputation in composite beef cattle.BMC genetics · 2015Article
- Linkage disequilibrium levels and allele frequency distribution in Blanco Orejinegro and Romosinuano Creole cattle using medium density SNP chip data.Genetics and molecular biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStrategies for imputing genotypes from the Illumina-Bovine3K, Illumina-BovineLD (6K), BeefLD-GGP (8K), a non-commercial-15K and IndicusLD-GGP (20K) to either Illumina-BovineSNP50 (50K) or to Illumina-BovineHD (777K) SNP panel, as well as for imputing from 50K, GGP-IndicusHD (90iK) and GGP-BeefHD (90tK) to 777K were investigated. Imputation of low density (<50K) genotypes to 777K was carried out in either one or two steps. Imputation of ungenotyped parents (n = 37 sires) with four or more offspring to the 50K panel was also assessed. There were 2,946 Braford, 664 Hereford and 88 Nellore animals, from which 71, 59 and 88 were genotyped with the 777K panel, while all others had 50K genotypes. The reference population was comprised of 2,735 animals and 175 bulls for 50K and 777K, respectively. The low density panels were simulated by masking genotypes in the 50K or 777K panel for animals born in 2011. Analyses were performed using both Beagle and FImpute software. Genotype imputation accuracy was measured by concordance rate and allelic R(2) between true and imputed genotypes.
resultsThe average concordance rate using FImpute was 0.943 and 0.921 averaged across all simulated low density panels to 50K or to 777K, respectively, in comparison with 0.927 and 0.895 using Beagle. The allelic R(2) was 0.912 and 0.866 for imputation to 50K or to 777K using FImpute, respectively, and 0.890 and 0.826 using Beagle. One and two steps imputation to 777K produced averaged concordance rates of 0.806 and 0.892 and allelic R(2) of 0.674 and 0.819, respectively. Imputation of low density panels to 50K, with the exception of 3K, had overall concordance rates greater than 0.940 and allelic R(2) greater than 0.919. Ungenotyped animals were imputed to 50K panel with an average concordance rate of 0.950 by FImpute.
conclusionFImpute accuracy outperformed Beagle on both imputation to 50K and to 777K. Two-step outperformed one-step imputation for imputing to 777K. Ungenotyped animals that have four or more offspring can have their 50K genotypes accurately inferred using FImpute. All low density panels, except the 3K, can be used to impute to the 50K using FImpute or Beagle with high concordance rate and allelic R(2).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.