Accuracy of genotype imputation in Labrador Retrievers

J Friedrich, R Antolin, SM Edwards, E Sanchez-Molano, MJ Haskell, JM Hickey, P Wiener

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
25 Downloads (Pure)


The dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labrador Retriever dogs. The study involved 5826 SNPs on chromosome 1 representing the high density (HighD) array; the low‐density (LowD) array was simulated by masking different proportions of SNPs on the HighD array. The correlations between true and imputed genotypes for a realistic masking level of 87.5% ranged from 0.92 to 0.97, depending on the scenario used. A correlation of 0.92 was found for a likely scenario (10% of dogs genotyped using HighD, 87.5% of HighD SNPs masked in the LowD array), which indicates that genotype imputation in Labrador Retrievers can be a valuable tool to reduce experimental costs while increasing sample size. Furthermore, we show that genotype imputation can be performed successfully even without pedigree information and with low relatedness between dogs in the reference and validation sets. Based on these results, the impact of genotype imputation was evaluated in a genome‐wide association analysis and genomic prediction in Labrador Retrievers.
Original languageEnglish
Pages (from-to)303 - 311
Number of pages9
JournalAnimal Genetics
Issue number4
Early online date5 Jul 2018
Publication statusFirst published - 5 Jul 2018

Bibliographical note



  • Genome-wide association studies
  • Genomic prediction
  • Imputation accuracy
  • Low-density array design
  • Pedigree information
  • Reference set


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