Effects of Different Methods and Genomic Relationship Matrices on Reliabilities of Genomic Selection in Dairy Cattle

Since genomic prediction is widely used in dairy cattle, we aimed to evaluate the performance of pedigree based (ABLUP), SNP based (GBLUP) and single-step GBLUP (ss-GBLUP) methods with different sets of information in terms of reliability of genomic prediction. Four different methods were evaluated: (Method 1) ABLUP with all available phenotypes and pedigree; (Method 2) GBLUP with SNP genotypes and phenotypes of genotyped cows; (Method 3) single-step GBLUP with SNP genotypes, phenotypes of genotyped cows and all pedigree and (Method 4) single-step GBLUP with SNP genotypes, all phenotypes of both genotyped and nongenotyped cows and all pedigree. SNP based methods also used different genomic relationship matrices (GRMs) formed by different approaches: vanRaden, Astle, Yang and Endelman. The simulated dataset replicates a common dairy cattle population. A significant increase in reliability of prediction was observed in ss-GBLUP with all phenotypes and pedigree beside genotyped cows. This increase was apparent for both first lactation milk yield (LMY) and milk fat percentage (Fat%). Combining all available information with ss-GBLUP gave about 1.6 and 1.2 times higher reliabilities for LMY and Fat%, respectively, compared to those obtained from the other three methods.

Keywords:

Dairy cattle, Genomic relationship matrix ssGBLUP, Reliability,

PDF

___

Astle, W., Balding, D. J. (2009). Population Structure and Cryptic Relatedness in Genetic Association Studies." Statistical Science, 24(4) 451-471. https://doi.org/10.1214/09-STS307
Covarrubias-Pazaran, G. (2016). “Genome assisted prediction of quantitative traits using the R package sommer.” PLoS ONE, 11, 1-15.
Christensen O. F., Lund M. S. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 2010, 42:2-8. http://www.gsejournal.org/content/42/1/2.
Endelman, J. B. (2011) Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP, The Plant Genome, 4(3), https://doi.org/10.3835/plantgenome2011.08.0024
Endelman, J. B. , Jannink, J. L. ( 2012) Shrinkage estimation of the realized relationship matrix. G3 (Bethesda). 2(11):1405-1413. https://doi.org/10.1534/g3.112.004259
Forni, S. , Aguilar, I, Misztal, I. (2011) Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol., 43(1):1 https://doi.org/10.1186/1297-9686-43-1
Fragomeni, B.O., Lourenco, D.A.L., Masuda, Y. et al.(2017) Incorporation of causative quantitative trait nucleotides in single-step GBLUP. Genet Sel Evol 49, 59 https://doi.org/10.1186/s12711-017-0335-0
Gaspa, G. , Veerkamp, R. F. ,Calus, M. P.L. , Windig, J. J. (2015) Assessment of genomic selection for introgression of polledness into Holstein Friesian cattle by simulation, Livestock Science, 179 86-95. https://doi.org/10.1016/j.livsci.2015.05.020.
Goddard, M.E. , Hayes, B.J. (2007) Genomic selection, Journal of Animal Breeding and Genetics, 124(6) 323-330. https://doi.org/10.1111/j.1439-0388.2007.00702.x
Gray K. A., Cassady J. P., Huang Y., Maltecca C. (2012). Effectiveness of genomic prediction on milk flow traits in dairy cattle. Genetics Selection Evolution, 44:24-29. http://www.gsejournal.org/content/44/1/24.
Henderson, C. R. (1976) A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding Values, Biometrics, 32(1) 69-83. https://doi.org/10.2307/2529339
International Human Genome Sequencing Consortium. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860–921. https://doi.org/10.1038/35057062
Karaman, E., Lund, M. S. , Anche, M. T. , Janss, L. , Su, G. (2018) Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome, G3 Genes|Genomes|Genetics, 8 (11), 3549–3558. https://doi.org/10.1534/g3.118.200673.
Legarra A. , Christensen O. F. , Aguilar I. , Misztal I. (2014) Single Step, a general approach for genomic selection, Livestock Science 166 54-65. https://doi.org/10.1016/j.livsci.2014.04.029.
Meuwissen, T. H. , Hayes, B. J. , Goddard, M. E. (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics.157(4):1819-1829. https://doi.org/10.1093/genetics/157.4.1819.
Misztal, I. , Lourenco, D. , Legarra, A. (2020) Current status of genomic evaluation, Journal of Animal Science, 98(4) , skaa101. https://doi.org/10.1093/jas/skaa101
Mrode, R. A. (2014) Linear models for the prediction of animal breeding values, CABI, Wallingford, Oxfordshire.
Pérez-Cabal, M. A. , Vazquez, A. I. , Gianola D. , Rosa G. J. , Weigel K. A. (2012) Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts. Front Genet. 3(27). https://doi.org/10.3389/fgene.2012.00027
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Schaeffer, L.R. (2006) “Strategy for applying genome-wide selection in dairy cattle”, Journal of Animal Breeding and Genetics, 123(4) 218-223. https://doi.org/10.1111/j.1439-0388.2006.00595.x.
Seno, L., Guidolin, D., Aspilcueta-Borquis, R., Nascimento, G., Silva, T., Oliveira, H., & Munari, D. (2018). Genomic selection in dairy cattle simulated populations. Journal of Dairy Research, 85(2), 125-132. https//doi.org/10.1017/S0022029918000304
Tsuruta, S. , Lourenco, D.A.L., Masuda, Y. , Lawlor, T.J. , Misztal, I. (2021) Reducing computational cost of large-scale genomic evaluation by using indirect genomic prediction, JDS Communications,2(6),356:360. https://doi.org/10.3168/jdsc.2021-0097
VanRaden, P.M. (2008) Efficient Methods to Compute Genomic Predictions, Journal of Dairy Science 91(11) 4414-4423. https://doi.org/10.3168/jds.2007-0980.
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Health AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM.(2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genet., 42,565–569. https://doi.org/10.1038/ng.608.
Wiggans, G. R. , Cole, J. B. , Hubbard, S. M., Sonstegard, T. S. (2017) Genomic Selection in Dairy Cattle: The USDA Experience, Annu Rev Anim Biosci.5:309-327. https://doi.org10.1146/annurev-animal-021815-111422