ArticleBMC genetics2015
Strategies for genotype imputation in composite beef cattle.
Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 34 citations in OpenAlex.
- Genotype imputation performance in Nellore cattle across different SNP panels and software tools.Tropical animal health and production · 2026Article
- Low-Coverage Whole-Genome Sequencing (lcWGS) in Cattle: Analysis of Potential and Prospects for Application.Animals : an open access journal from MDPI · 2025Review
- Tailoring Genomic Selection forGenes · 2024Review
- Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs.Journal of animal science and biotechnology · 2023Article
- Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species.Frontiers in genetics · 2023Article
- Article
- Evaluation of imputation accuracy using the combination of two high-density panels in Nelore beef cattle.Scientific reports · 2019Article
- The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1.Journal of animal science · 2019Article
- Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population.BMC genomics · 2019Article
- Study on the introgression of beef breeds in Canchim cattle using single nucleotide polymorphism markers.PloS one · 2017Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R(2)).
resultsThe highest imputation accuracy scenario consisted of a reference population with males and females and a target population with young females. Among the SNP panels in the tested scenarios, from the 50K, GGP75Ki and GGP80K were the most adequate to impute to HD in Canchim cattle. FImpute reduced computation run-time to impute genotypes from 20 to 100 times when compared to BEAGLE.
conclusionThe genotyping panels possessing at least 50 thousands markers are suitable for genotype imputation to HD with acceptable accuracy. The FImpute algorithm demonstrated a higher efficiency of imputed markers, especially in lower density panels. These considerations may assist to increase genotypic information, reduce genotyping costs, and aid in genomic selection evaluations in crossbred animals.
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