title: Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications.
abstract: In livestock, many studies have reported the results of imputation to 50k single nucleotide polymorphism (SNP) genotypes for animals that are genotyped with low-density SNP panels. The objective of this paper is to review different measures of correctness of imputation, and to evaluate their utility depending on the purpose of the imputed genotypes. Across studies, imputation accuracy, computed as the correlation between true and imputed genotypes, and imputation error rates, that counts the number of incorrectly imputed alleles, are commonly used measures of imputation correctness. Based on the nature of both measures and results reported in the literature, imputation accuracy appears to be a more useful measure of the correctness of imputation than imputation error rates, because imputation accuracy does not depend on minor allele frequency (MAF), whereas imputation error rate depends on MAF. Therefore imputation accuracy can be better compared across loci with different MAF. Imputation accuracy depends on the ability of identifying the correct haplotype of a SNP, but many other factors have been identified as well, including the number of genotyped immediate ancestors, the number of animals with genotypes at the high-density panel, the SNP density on the low- and high-density panel, the MAF of the imputed SNP and whether imputed SNP are located at the end of a chromosome or not. Some of these factors directly contribute to the linkage disequilibrium between imputed SNP and SNP on the low-density panel. When imputation accuracy is assessed as a predictor for the accuracy of subsequent genomic prediction, we recommend that: (1) individual-specific imputation accuracies should be used that are computed after centring and scaling both true and imputed genotypes; and (2) imputation of gene dosage is preferred over imputation of the most likely genotype, as this increases accuracy and reduces bias of the imputed genotypes and the subsequent genomic predictions.
authors.affiliation: 1Animal Breeding and Genomics Centre,Wageningen UR Livestock Research,PO Box 135,6700 AC Wageningen,The Netherlands.
authors.forename: M P L
authors.initials: MP
authors.lastname: Calus
authors.affiliation: 1Animal Breeding and Genomics Centre,Wageningen UR Livestock Research,PO Box 135,6700 AC Wageningen,The Netherlands.
authors.forename: A C
authors.initials: AC
authors.lastname: Bouwman
authors.affiliation: 2Royal (Dick) School of Veterinary Studies,The Roslin Institute,The University of Edinburgh,Easter Bush,Midlothian,EH25 9RG,Scotland,UK.
authors.forename: J M
authors.initials: JM
authors.lastname: Hickey
authors.affiliation: 1Animal Breeding and Genomics Centre,Wageningen UR Livestock Research,PO Box 135,6700 AC Wageningen,The Netherlands.
authors.forename: R F
authors.initials: RF
authors.lastname: Veerkamp
authors.affiliation: 3Animal Breeding and Genomics Centre,Wageningen University,PO Box 338,6700 AH Wageningen,The Netherlands.
authors.forename: H A
authors.initials: HA
authors.lastname: Mulder
mesh_terms: D000818:Animals; D001947:Breeding; D005787:Gene Frequency; D016678:Genome; D023281:Genomics / Q000379:methods*; D005838:Genotype; D006239:Haplotypes; D058751:Livestock / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide