Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle. (28th May 2015)

• Record Type: Journal Article
• Authors: Ogawa, Shinichiro; Matsuda, Hirokazu; Taniguchi, Yukio; Watanabe, Toshio; Takasuga, Akiko; Sugimoto, Yoshikazu; Iwaisaki, Hiroaki
• Is Part Of: Animal science journal. Volume 87:Number 1(2016:Jan.)
• Page Start: 3
• Page End: 12
• Publication Date: 2015-05-28
• Subjects: imputation accuracy -- Japanese Black cattle -- low‐density panel -- single nucleotide polymorphism; Animal culture -- Periodicals; Livestock -- Periodicals; 636
• DOI: 10.1111/asj.12393
• Languages: English
• ISSNs: 1344-3941

Abstract

Abstract: Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction.