title: Imputation of missing genotypes from low- to high-density SNP panel in different population designs.
abstract: Imputation of missing genotypes, in particular from low density to high density, is an important issue in genomic selection and genome-wide association studies. Given the marker densities, the most important factors affecting imputation accuracy are the size of the reference population and the relationship between individuals in the reference (genotyped with high-density panel) and study (genotyped with low-density panel) populations. In this study, we investigated the imputation accuracies when the reference population (genotyped with Illumina BovineSNP50 SNP panel) contained sires, halfsibs, or both sires and halfsibs of the individuals in the study population (genotyped with Illumina BovineLD SNP panel) using three imputation programs (fimpute v2.2, findhap v2, and beagle v3.3.2). Two criteria, correlation between true and imputed genotypes and missing rate after imputation, were used to evaluate the performance of the three programs in different scenarios. Our results showed that fimpute performed the best in all cases, with correlations from 0.921 to 0.978 when imputing from sires to their daughters or between halfsibs. In general, the accuracies of imputing between halfsibs or from sires to their daughters were higher than were those imputing between non-halfsibs or from sires to non-daughters. Including both sires and halfsibs in the reference population did not improve the imputation performance in comparison with when only including halfsibs in the reference population for all the three programs.
authors.affiliation: Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China; Department of Cytogenetics and Genome Analysis, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, 06466, Germany.
authors.forename: S
authors.initials: S
authors.lastname: He
authors.forename: S
authors.initials: S
authors.lastname: Wang
authors.forename: W
authors.initials: W
authors.lastname: Fu
authors.forename: X
authors.initials: X
authors.lastname: Ding
authors.forename: Q
authors.initials: Q
authors.lastname: Zhang
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D005260:Female; D056726:Genetic Association Studies; D005838:Genotype; D060005:Genotyping Techniques; D008297:Male; D020641:Polymorphism, Single Nucleotide; D011156:Population Density; D012016:Reference Values; D012984:Software
keywords: low density; reference population size; relationship