ArticleFrontiers in genetics2019
Optimizing Selection of the Reference Population for Genotype Imputation From Array to Sequence Variants.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Effect of founder breeds on genotype imputation accuracy in Canchim cattle.Journal of applied genetics · 2026Article
- Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds.BMC genomics · 2025Article
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes.Genetics, selection, evolution : GSE · 2024Article
- Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs.Journal of animal science and biotechnology · 2023Article
- Development and validation of a horse reference panel for genotype imputation.Genetics, selection, evolution : GSE · 2022Article
- Optimizing Sequencing Resources in Genotyped Livestock Populations Using Linear Programming.Frontiers in genetics · 2021Article
- High confidence copy number variants identified in Holstein dairy cattle from whole genome sequence and genotype array data.Scientific reports · 2020Article
- Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling.Genetics, selection, evolution : GSE · 2020Article
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7 authors.
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Abstract
Imputation of high-density genotypes to whole-genome sequences (WGS) is a cost-effective method to increase the density of available markers within a population. Imputed genotypes have been successfully used for genomic selection and discovery of variants associated with traits of interest for the population. To allow for the use of imputed genotypes for genomic analyses, accuracy of imputation must be high. Accuracy of imputation is influenced by multiple factors, such as size and composition of the reference group, and the allele frequency of variants included. Understanding the use of imputed WGSs prior to the generation of the reference population is important, as accurate imputation might be more focused, for instance, on common or on rare variants. The aim of this study was to present and evaluate new methods to select animals for sequencing relying on a previously genotyped population. The Genetic Diversity Index method optimizes the number of unique haplotypes in the future reference population, while the Highly Segregating Haplotype selection method targets haplotype alleles found throughout the majority of the population of interest. First the WGSs of a dairy cattle population were simulated. The simulated sequences mimicked the linkage disequilibrium level and the variants' frequency distribution observed in currently available Holstein sequences. Then, reference populations of different sizes, in which animals were selected using both novel methods proposed here as well as two other methods presented in previous studies, were created. Finally, accuracies of imputation obtained with different reference populations were compared against each other. The novel methods were found to have overall accuracies of imputation of more than 0.85. Accuracies of imputation of rare variants reached values above 0.50. In conclusion, if imputed sequences are to be used for discovery of novel associations between variants and traits of interest in the population, animals carrying novel information should be selected and, consequently, the Genetic Diversity Index method proposed here may be used. If sequences are to be used to impute the overall genotyped population, a reference population consisting of common haplotypes carriers selected using the proposed Highly Segregating Haplotype method is recommended.
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