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http://dx.doi.org/10.5713/ajas.15.0291

Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population  

Jattawa, Danai (Department of Animal Science, Faculty of Agriculture, Kasetsart University)
Elzo, Mauricio A. (Department of Animal Sciences, University of Florida)
Koonawootrittriron, Skorn (Department of Animal Science, Faculty of Agriculture, Kasetsart University)
Suwanasopee, Thanathip (Department of Animal Science, Faculty of Agriculture, Kasetsart University)
Publication Information
Asian-Australasian Journal of Animal Sciences / v.29, no.4, 2016 , pp. 464-470 More about this Journal
Abstract
The objective of this study was to investigate the accuracy of imputation from low density (LDC) to moderate density SNP chips (MDC) in a Thai Holstein-Other multibreed dairy cattle population. Dairy cattle with complete pedigree information (n = 1,244) from 145 dairy farms were genotyped with GeneSeek GGP20K (n = 570), GGP26K (n = 540) and GGP80K (n = 134) chips. After checking for single nucleotide polymorphism (SNP) quality, 17,779 SNP markers in common between the GGP20K, GGP26K, and GGP80K were used to represent MDC. Animals were divided into two groups, a reference group (n = 912) and a test group (n = 332). The SNP markers chosen for the test group were those located in positions corresponding to GeneSeek GGP9K (n = 7,652). The LDC to MDC genotype imputation was carried out using three different software packages, namely Beagle 3.3 (population-based algorithm), FImpute 2.2 (combined family- and population-based algorithms) and Findhap 4 (combined family- and population-based algorithms). Imputation accuracies within and across chromosomes were calculated as ratios of correctly imputed SNP markers to overall imputed SNP markers. Imputation accuracy for the three software packages ranged from 76.79% to 93.94%. FImpute had higher imputation accuracy (93.94%) than Findhap (84.64%) and Beagle (76.79%). Imputation accuracies were similar and consistent across chromosomes for FImpute, but not for Findhap and Beagle. Most chromosomes that showed either high (73%) or low (80%) imputation accuracies were the same chromosomes that had above and below average linkage disequilibrium (LD; defined here as the correlation between pairs of adjacent SNP within chromosomes less than or equal to 1 Mb apart). Results indicated that FImpute was more suitable than Findhap and Beagle for genotype imputation in this Thai multibreed population. Perhaps additional increments in imputation accuracy could be achieved by increasing the completeness of pedigree information.
Keywords
Imputation Accuracy; Linkage Disequilibrium; Multibreed Dairy Cattle;
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1 Barrett, J. C., B. Fry, J. Maller, and M. J. Daly. 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263-265.   DOI
2 Browning, B. L. and S. R. Browning. 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84:210-223.   DOI
3 de Roos, A. P. W., C. Schrooten, R. F. Veerkamp, and J. A. M. van Arendonk. 2011. Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls. J. Dairy Sci. 94:1559-1567.   DOI
4 DPO. 1997. D.P.O. Sire and Dam Summary 1997. The Dairy Farming Promotion Organization of Thailand, Ministry of Agriculture and Cooperation, Bangkok, Thailand.
5 Druet, T., C. Schrooten, and A. P. W. de Roos. 2010. Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle. J. Dairy Sci. 93:5443-5454.   DOI
6 Fu, W., J. C. M. Dekkers, W. R. Lee, and B. Abasht. 2015. Linkage disequilibrium in crossbred and pure line chickens. Gent. Sel. Evol. 47:11.   DOI
7 Hayes, B. J., P. J. Bowman, H. D. Daetwyler, J. W. Kijas, and J. H. J. van der Werf. 2012. Accuracy of genotype imputation in sheep breeds. Anim. Genet. 43:72-80.
8 He, S., S. Wang, W. Fu, X. Ding, and Q. Zhang. 2015. Imputation of missing genotypes from low- to high-density SNP panel in different population designs. Anim. Genet. 46:1-17.   DOI
9 Hickey, J. M., J. Crossa, R. Babu, and G. de los Campos. 2012. Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Crop Sci. 52:654-663.   DOI
10 Johnston, J., G. Kistemaker, and P. G. Sullivan. 2011. Comparison of different imputation methods. In: Proceeding of the 2011 Interbull Meeting. Stavanger, Norway. pp. 25-33.
11 Khatkar, M. S., G. Moser, B. J. Hayes, and H. W. Raadsma. 2012. Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics 13:538.   DOI
12 Kong, A., G. Masson, M. L. Frigge, A. Gylfason, P. Zusmanovich, G. Thorleifsson, P. I. Olason, A. Ingason, S. Steinberg, T. Rafnar, P. Sulem, M. Mouy, F. Jonsson, U. Thorsteinsdottir, D. F. Gudbjartsson, H. Stefansson, and K. Stefansson. 2008. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat. Genet. 40:1068-1075.   DOI
13 Koonawootrittriron, S., M. A. Elzo, and T. Thongprapi. 2009. Genetic trends in a Holstein ${\times}$ other breeds multibreed dairy population in Central Thailand. Livest. Sci. 122:186-192.   DOI
14 Koonawootrittriron, S., M. A. Elzo, T. Suwanasopee, C. Chaimongkol, W. Chunpet, and T. Tongprapi. 2015. D.P.O. Sire & Dam Summary 2015. Dairy Farming Promotion Organization, Ministry of Agriculture and Cooperatives of Thailand, Bangkok, Thailand.
15 Koonawootrittriron, S., T. Suwanasopee, and M. A. Elzo. 2012. Development of a Dairy Genetic Genomic Evaluation System in Thailand. National Science and Technology Development Agency, Bangkok, Thailand.
16 Larmer, S. G., M. Sargolzaei, and F. S. Schenkel. 2014. Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds. J. Dairy Sci. 97:3128-3141.   DOI
17 Pimentel, E. C. G., M. Wensch-Dorendorf, S. Konig, and H. H. Swalve. 2013. Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture. Genet. Sel. Evol. 45:12.   DOI
18 Ma, P., R. F. Brondum, Q. Zhang, M. S. Lund, and G. Su. 2013. Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle. J. Dairy Sci. 96:4666-4677.   DOI
19 Mulder, H. A., M. P. L. Calus, T. Druet, and C. Schrooten. 2012. Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. J. Dairy Sci. 95:876-889.   DOI
20 Piccoli, M. L., J. Braccini, F. F. Cardoso, M. Sargolzaei, S. G. Larmer, and F. S. Schenkel. 2014. Accuracy of genome-wide imputation in Braford and Hereford beef cattle. BMC Genetics 15:157.   DOI
21 Pryce, J. E., J. Johnston, B. J. Hayes, G. Sahana, K. A. Weigel, S. McParland, D. Spurlock, N. Krattenmacher, R. J. Spelman, E. Wall, and M. P. L. Calus. 2014. Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations. J. Dairy Sci. 97: 1799-1811.   DOI
22 Ritsawai, P., S. Koonawootrittriron, D. Jattawa, T. Suwanasopee, and M. A. Elzo. 2014. Fraction of cattle breeds and their influence on milk production of Thai dairy cattle. In: Proceeding of 52rd Kasetsart conference, Kasetsart University, Bangkok, Thailand.
23 Sargolzaei, M., J. P. Chesnais, and F. S. Schenkel. 2014. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15:478.   DOI
24 VanRaden, P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor, and F. S. Schenkel. 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24.   DOI
25 Sun, C., X. -L. Wu, K. A. Weigel, G. J. M. Sosa, S. Bauck, B. W. Woodward, R. D. Schnabel, J. F. Taylor, and D. Gianola. 2012. An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle. Genet. Res. Camb. 94:133-150.   DOI
26 VanRaden, P. M. and C. Sun. 2014. Fast imputation using mediumor low-coverage sequence data. In: Proceeding of 10th World Congress of Genetics Applied to Livestock Production, Vancouver, Canada.
27 VanRaden, P. M., J. R. O'Connell, G. R. Wiggans, K. A. Weigel. 2011. Genomic evaluations with many more genotypes. Genet. Sel. Evol. 43:10.   DOI
28 Ventura, R. V., D. Lu, F. S. Schenkel, Z. Wang, C. Li, and S. P. Miller. 2014. Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbred beef cattle. J. Anim. Sci. 92: 1433-1444.   DOI
29 Weng, Z., Z. Zhang, Q. Zhang, W. Fu, S. He, and X. Ding. 2013. Comparison of different imputation methods from low- to highdensity panels using Chinese Holstein cattle. Animal 7: 729-735.   DOI
30 Zhang, Z. and T. Druet. 2010. Marker imputation with low-density marker panels in Dutch Holstein cattle. J. Dairy Sci. 93:5487-5494.   DOI