Estimation of genotype imputation accuracy using reference populations with varying degrees of relationship and marker density panel

Document Type : Original Research Article (Regular Paper)

Authors

1 Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran.

2 Department of Animal Science, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Genotype imputation from low-density to high-density (SNP) chips is an important step before applying genomic selection, because denser chips can provide more reliable genomic predictions. In the current research, the accuracy of genotype imputation from low and moderate-density panels (5K and 50K) to high-density panels in the purebred and crossbred populations was assessed. The simulated populations included two purebred populations (lines A and B) and two crossbred populations (cross and backcross). Three scenarios were assessed for selecting the subset of the references that used to impute un-genotyped loci of animals in the validation set, where: 1) high relationship with validation set, 2) randomly, and 3) high inbreeding selecting. Imputing the individuals of validation set 5K and 50K to marker density 777K using the various combinations of reference set was performed by FImpute software. The imputation accuracies were calculated using two methods including Pearson correlation coefficient (PCC) and concordance rate (CR). The results showed that imputation accuracy in the purebred populations lines A and B was higher than the cross and backcross populations. When the reference set has been selected based on high relationships, the genotype accuracy in lines A and B was the highest, and there was less difference between imputation from 5K and 50K density to 777K compared to the other subset selection methods. In the crossbred population with imputation from 50K to 777K, the imputation accuracy was the highest in the state of the randomly selected of the reference population (0.98 and 0.97 for PCC and CR, respectively). In the backcross population, the imputation accuracy was the lowest when the reference set selected according to the high inbreeding, which it could be resulting from the lower homozygosis in these populations.

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References

  • Bolormaa, S., Gore, K., van der Werf, J.H.J., Hayes, B.J., Daetwyler, H.D. 2015. Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy. Animal Genetics 46, 544–556.
  • Calus, M.P., Bouwman, A.C., Hickey, J.M., Veerkamp, R.F., Mulder, H.A. 2014. Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications. Animal 8, 1743-1753.
  • Dassonneville, R., Baur, A., Fritz, S., Boichard, D., Ducrocq, V. 2012. Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment. Genetics Selection Evolution 44, 40.
  • Habier, D., Fernando, R.L., Dekkers, J.C.M. 2009. Genomic selection using low-density marker panels. Genetics 182, 343–353.
  • Hayes, B., Bowman, P., Chamberlain, A., Verbyla, K., Goddard, M. 2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41, 51.
  • Hickey, J.M., Kinghorn, B.P., Tier, B., van der Werf, J.H., Cleveland, M.A. 2012. A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genetics Selection Evolution 44, 9.
  • Jattawa, D., Elzo, M.A., Koonawootrittriron, S., Suwanasopee, T. 2016. Imputation accuracy from low to moderate-density single nucleotide polymorphism chips in a Thai multi-breed dairy cattle population. Asian-Australasian Journal of Animal Sciences 29, 464–470.
  • Larmer, S.G., Sargolzaei, M., Schenkel, F.S. 2014. Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds. Journal of Dairy Science 97, 3128–3141.
  • Ma, P.P., Brøndum, R. F., Zhang, Q., Lund, M.S., Su, G. 2013. Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish red Cattle. Journal of Dairy Science 96, 4666–4677.
  • Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
  • Moghaddar, N., Gore, K.P., Daetwyler, H.D., Hayes, B.J., van der Werf, J.H.J. 2015. Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction. Genetics Selection Evolution 47, 97.
  • Momen, M., Ayatollahi Mehrgardi, A., Sheikhi, A., Kranis, A., Tusell, L., Morota, G., Rosa, G., Gianola, D. 2018a. Predictive ability of genome-assisted statistical models under various forms of gene action. Scientific Reports 8, 12309.
  • Momen, M., Ayatollahi Mehrgardi, A., Amiri Roudbar, M., Kranis, A., Mercuri Pinto, R., Valente, B.D., Morota, G., Rosa, G., Gianola, D. 2018b. Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models. Frontiers in Genetics 9, 455.
  • Oliveira Junior, G.A., Chud, T.C.S., Ventura, R.V., Garrick, D.J., Cole, J.B., Munari, D.P., Ferraz, J.B.S., Mullart, E., DeNise, S., Smith, S. 2017. Genotype imputation in a tropical crossbred dairy cattle population. Journal of Dairy Science 100, 9623-9634.
  • Pimentel, E.C., Wensch- Dorendorf, M., Konig, S., Swalve, H.H. 2013. Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture. Genetics Selection Evolution 45, 12.
  • Sargolzaei, M., Chesnais, J.P., Schenkel, F.S., 2011. FImpute-An efficient imputation algorithm for dairy cattle populations. Journal of Dairy Science 94 (E-Suppl. 1),421. (Abstract)
  • Sargolzaei, M., Schenkel, F.S. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics 25, 680-681.
  • Ventura, R. V., Miller, S.P, Dodds K.G., Auvray,B., Lee, M., Bixley, M., Clarke, S.M.,  McEwan, J.C. 2016. Assessing accuracy of imputation using different SNP panel densities in a multibreed sheep population. Genetics Selection Evolution 48, 71.
  • Ventura, R.V., Schenkel, D., Lu, F.S., Wang, Z., Li, C., Miller, S.P. 2014. Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle. Journal of Animal Science 92, 1433–1444.
Journal of Livestock Science and Technologies
Volume 7, Issue 1
June 2019
Pages 45-53

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