Zur Seitenansicht


Accuracy of genotype imputation in Nelore cattle
VerfasserCarvalheiro, Roberto ; Boison, Solomon A. ; Neves, Haroldo H. R. ; Sargolzaei, Mehdi ; Schenkel, Flavio S. ; Utsunomiya, Yuri T. ; Pérez OBrien, Ana Maria ; Sölkner, Johann ; McEwan, John C. ; Van Tassell, Curtis P. ; Sonstegard, Tad S. ; Garcia, José Fernando
Erschienen in
Genetics Selection Evolution, 2014, Jg. 46,
ErschienenBioMed Central (BMC), 2014
DokumenttypAufsatz in einer Zeitschrift
URNurn:nbn:at:at-ubbw:3-112 Persistent Identifier (URN)
 Das Werk ist frei verfügbar
Accuracy of genotype imputation in Nelore cattle [1.19 mb]
Zusammenfassung (Englisch)


Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.


Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs FImpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina® BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.


Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. FImpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.


If the Illumina® BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with FImpute.