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 (R2).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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0251-7) contains supplementary material, which is available to authorized users.
Highlights
Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs
Born between 1999 and 2005, were genotyped with the BovineHD BeadChip (Illumina, Inc., San Diego, CA) panel, consisting of 786,799 single nucleotide polymorphisms (SNP) distributed throughout the genome
We found that when the concordance rate (CR) is high, the allelic Allelic R square (R2) value approaches this rate
Summary
Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. The improvement in accuracy of genomic selection in beef cattle, which often includes data from different breeds and crossbred animals, depends on conservation of linkage disequilibrium, consistency of the linkage phase between QTL (quantitative trait loci) and genetic markers across breeds, and similarity of QTL effects between breeds [3, 4]. According to de Roos et al [6], more than 300,000 informative SNPs are required to detect conserved linkage disequilibrium and allow multibreed genomic selection. High-density panels have higher coverage of SNPs in smaller genomic distances, greater linkage disequilibrium and conserved linkage disequilibrium across breeds, and are better for genomic selection and genome-wide association studies in beef cattle and crossbred animals [3, 7]. An alternative that reduces these costs is genotype imputation [8, 9]
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