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Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

  • D. A. Grossi (a1) (a2), L. F. Brito (a2), M. Jafarikia (a2) (a3), F. S. Schenkel (a2) and Z. Feng (a1)...

Abstract

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.

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Badke, YM, Bates, RO, Ernst, CW, Fix, J and Steibel, JP 2014. Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation. G3: Genes| Genomes| Genetics 4, 623631.
Badke, YM, Bates, RO, Ernst, CW, Schwab, C, Fix, J, Tassell, CVP and Steibel, JP 2013. Methods of tagSNP selection and other variables affecting imputation accuracy in swine. BMC Genetics 14, 8.
Brito, LF, Clarke, SM, McEwan, JC, Miller, SP, Pickering, NK, Bain, WE, Dodds, KG, Sargolzaei, M and Schenkel, FS 2017. Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip. BMC Genetics 18, 7.
Brøndum, RF, Rius-Vilarrasa, E, Strandén, I, Su, G, Guldbrandtsen, B, Fikse, W and Lund, MS 2011. Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. Journal of Dairy Science 94, 47004707.
Browning, BL and Browning, SR 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. The American Journal of Human Genetics 84, 210223.
Browning, BL and Browning, SR 2013. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194, 459471.
Calus, M, Bouwman, A, Hickey, J, Veerkamp, R and Mulder, H 2014. Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications. Animal 8, 17431753.
Carillier, C, Larroque, H, Palhière, I, Clément, V, Rupp, R and Robert-Granié, C 2013. A first step toward genomic selection in the multi-breed French dairy goat population. Journal of Dairy Science 96, 72947305.
Chesnais, J, Cooper, T, Wiggans, G, Sargolzaei, M, Pryce, J and Miglior, F 2016. Using genomics to enhance selection of novel traits in North American dairy cattle. Journal of Dairy Science 99, 24132427.
Cleveland, M and Hickey, J 2013. Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation. Journal of Animal Science 91, 35833592.
Cleveland, MA, Hickey, JM and Forni, S 2012. A common dataset for genomic analysis of livestock populations. G3: Genes|Genomes|Genetics 2, 429435.
Garrick, DJ, Taylor, JF and Fernando, RL 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution 41, 55.
Grossi, DA, Jafarikia, M, Brito, LF, Buzanskas, ME, Sargolzaei, M and Schenkel, FS 2017. Genetic diversity, extent of linkage disequilibrium and persistence of gametic phase in Canadian pigs. BMC Genetics 18, 6.
He, S, Wang, S, Fu, W, Ding, X and Zhang, Q 2015. Imputation of missing genotypes from low‐to high‐density SNP panel in different population designs. Animal Genetics 46, 17.
Hickey, JM, Kinghorn, BP, Tier, B, van der Werf, JH and Cleveland, MA 2012. A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genetics Selection Evolution 44, 9.
Howie, BN, Donnelly, P and Marchini, J 2009. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics 5, e1000529.
Huang, Y, Hickey, JM, Cleveland, MA and Maltecca, C 2012. Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost. Genetics Selection Evolution 44, 25.
Khatkar, MS, Moser, G, Hayes, BJ and Raadsma, HW 2012. Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics 13, 538.
Knol, EF, Nielsen, B and Knap, PW 2016. Genomic selection in commercial pig breeding. Animal Frontiers 6, 1522.
Lillehammer, M and Sonesson, A 2011. Genomic selection for maternal traits in pigs. Journal of Animal Science 89, 39083916.
Ma, P, Brøndum, RF, Zhang, Q, Lund, MS and Su, G 2013. Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle. Journal of Dairy Science 96, 46664677.
Meuwissen, T, Hayes, B and Goddard, M 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.
Meuwissen, T, Hayes, B and Goddard, M 2013. Accelerating improvement of livestock with genomic selection. Annual Review of Animal Biosciences 1, 221237.
Meuwissen, T, Hayes, B and Goddard, M 2016. Genomic selection: a paradigm shift in animal breeding. Animal Frontiers 6, 614.
NFACC 2017. Code of practice for the care and handling of pigs. Retrieved on 15 May from https://www.nfacc.ca/pdfs/cod es/pig_code_of_practice.pdf.
Sargolzaei, M, Chesnais, JP and Schenkel, FS 2014. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478.
Sargolzaei, M, Schenkel, FS and VanRaden, PM 2009. GEBV: genomic breeding value estimator for livestock. In Proceedings of the Dairy Cattle Breeding and Genetics Meeting, University of Guelph, Guelph, ON, Canada, pp. 3–11.
Silva, MV, dos Santos, DJ, Boison, SA, Utsunomiya, AT, Carmo, AS, Sonstegard, TS, Cole, JB and Van Tassell, CP 2014. The development of genomics applied to dairy breeding. Livestock Science 166, 6675.
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 44144423.
Ventura, RV, Miller, SP, Dodds, KG, Auvray, B, Lee, M, Bixley, M, Clarke, SM and McEwan, JC 2016. Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population. Genetics Selection Evolution 48, 71.
Wang, C, Habier, D, Peiris, BL, Wolc, A, Kranis, A, Watson, KA, Avendano, S, Garrick, DJ, Fernando, RL, Lamont, SJ and Dekkers, JCM 2013. Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens. Poultry Science 92, 17121723.
Wellmann, R, Preuß, S, Tholen, E, Heinkel, J, Wimmers, K and Bennewitz, J 2013. Genomic selection using low density marker panels with application to a sire line in pigs. Genetics Selection Evolution 45, 28.
Weng, Z, Zhang, Z, Zhang, Q, Fu, W, He, S and Ding, X 2013. Comparison of different imputation methods from low-to high-density panels using Chinese Holstein cattle. Animal 7, 729735.
Wiggans, G, Sonstegard, T, VanRaden, P, Matukumalli, L, Schnabel, R, Taylor, J, Schenkel, F and Van Tassell, C 2009. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. Journal of Dairy Science 92, 34313436.
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