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High-density marker imputation accuracy in sixteen French cattle breeds

BACKGROUND: Genotyping with the medium-density Bovine SNP50 BeadChip(®) (50K) is now standard in cattle. The high-density BovineHD BeadChip(®), which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibriu...

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Detalles Bibliográficos
Autores principales: Hozé, Chris, Fouilloux, Marie-Noëlle, Venot, Eric, Guillaume, François, Dassonneville, Romain, Fritz, Sébastien, Ducrocq, Vincent, Phocas, Florence, Boichard, Didier, Croiseau, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3846489/
https://www.ncbi.nlm.nih.gov/pubmed/24004563
http://dx.doi.org/10.1186/1297-9686-45-33
Descripción
Sumario:BACKGROUND: Genotyping with the medium-density Bovine SNP50 BeadChip(®) (50K) is now standard in cattle. The high-density BovineHD BeadChip(®), which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip. METHODS: Five thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software. RESULTS: Mean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed. CONCLUSION: In all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.