Accuracy of genotype imputation based on reference population size and marker density in Hanwoo cattle
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Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.
Abstract
Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.
참고문헌 (32)
- T H E Meuwissen, "Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps", Genetics, 157(4), pp.1819~1829 (2001), Oxford University Press (OUP)[DOI]
- B. J. HAYES, "Increased accuracy of artificial selection by using the realized relationship matrix", Genetics Research, 91(1), pp.47~60 (2009), Hindawi Limited[DOI]
- R Lande, "Efficiency of marker-assisted selection in the improvement of quantitative traits.", Genetics, 124(3), pp.743~756 (1990), Oxford University Press (OUP)[DOI]
- H. J. Zheng, "QTL mapping of maize (Zea mays) stay-green traits and their relationship to yield", Plant Breeding, 128(1), pp.54~62 (2009), Wiley[DOI]
- Z. Zhang, "Accuracy of genomic prediction using low-density marker panels", Journal of Dairy Science, 94(7), pp.3642~3650 (2011), American Dairy Science Association[DOI]
- Quanchao Wang, "Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei", BMC Genetics, 18(1), pp.45~ (2017), Springer Science and Business Media LLC[DOI]
- D Habier, "Genomic Selection Using Low-Density Marker Panels", Genetics, 182(1), pp.343~353 (2009), Oxford University Press (OUP)[DOI]
- Theo HE Meuwissen, "Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping", Genetics Selection Evolution, 41(1), pp.35~ (2009), Springer Science and Business Media LLC[DOI]
- K.A. Weigel, "Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers", Journal of Dairy Science, 92(10), pp.5248~5257 (2009), American Dairy Science Association[DOI]
- Charles Y.K. Cheung, "GIGI: An Approach to Effective Imputation of Dense Genotypes on Large Pedigrees", The American Journal of Human Genetics, 92(4), pp.504~516 (2013), Elsevier BV[DOI]
- Mohamad Saad, "Power of Family-Based Association Designs to Detect Rare Variants in Large Pedigrees Using Imputed Genotypes", Genetic Epidemiology, 38(1), pp.1~9 (2014), Wiley[DOI]
- Jonathan Marchini, "A new multipoint method for genome-wide association studies by imputation of genotypes", Nature Genetics, 39(7), pp.906~913 (2007), Springer Science and Business Media LLC[DOI]
- Bryan N. Howie, "A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies", PLoS Genetics, 5(6), pp.e1000529~ (2009), Public Library of Science (PLoS)[DOI]
- Jared O'Connell, "A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness", PLoS Genetics, 10(4), pp.e1004234~ (2014), Public Library of Science (PLoS)[DOI]
- Hickey JM, "Recursive long range phasing and long haplotype library imputation: building a global haplotype library for Holstein cattle", Proceedings of the World Congress on Genetics Applied to Livestock Production, pp.0944~ (2010)
- John M. Hickey, "Factors Affecting the Accuracy of Genotype Imputation in Populations from Several Maize Breeding Programs", Crop Science, 52(2), pp.654~663 (2012), Wiley[DOI]
- Paul Scheet, "A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase", The American Journal of Human Genetics, 78(4), pp.629~644 (2006), Elsevier BV[DOI]
- Sharon R. Browning, "Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering", The American Journal of Human Genetics, 81(5), pp.1084~1097 (2007), Elsevier BV[DOI]
- Bryan Howie, "Fast and accurate genotype imputation in genome-wide association studies through pre-phasing", Nature Genetics, 44(8), pp.955~959 (2012), Springer Science and Business Media LLC[DOI]
- VanRaden PM, "Genomic evaluations with many more genotypes and phenotypes", Proceedings of the World Congress on Genetics Applied to Livestock Production, pp.0027~ (2010)
- Sambrook J, "Molecular cloning: a laboratory manual" (1989), Cold Spring Harbor Laboratory Press
- Shaun Purcell, "PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses", The American Journal of Human Genetics, 81(3), pp.559~575 (2007), Elsevier BV[DOI]
- W. G. Hill, "Linkage disequilibrium in finite populations", Theoretical and Applied Genetics, 38(6), pp.226~231 (1968), Springer Science and Business Media LLC[DOI]
- Brian L. Browning, "Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies", The American Journal of Human Genetics, 85(6), pp.847~861 (2009), Elsevier BV[DOI]
- Sharon R Browning, "Population Structure With Localized Haplotype Clusters", Genetics, 185(4), pp.1337~1344 (2010), Oxford University Press (OUP)[DOI]
- Y. Uemoto, "Accuracy of high-density genotype imputation in Japanese Black cattle", Animal Genetics, 46(4), pp.388~394 (2015), Wiley[DOI]
- Hozé C, "High-density marker imputation accuracy in sixteen French cattle breeds", Genet Sel Evol, 45, pp.33~ (2013)[DOI]
- Hubert Pausch, "Imputation of high-density genotypes in the Fleckvieh cattle population", Genetics Selection Evolution, 45(1), pp.3~ (2013), Springer Science and Business Media LLC[DOI]
- M. Erbe, "Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels", Journal of Dairy Science, 95(7), pp.4114~4129 (2012), American Dairy Science Association[DOI]
- Liuhong Chen, "Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction", PLoS ONE, 9(7), pp.e101544~ (2014), Public Library of Science (PLoS)[DOI]
- Zhihui Zhang, "False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy", Human Molecular Genetics, 31(1), pp.146~155 (2021), Oxford University Press (OUP)[DOI]