Comparing Different Marker Densities and Various Reference Populations Using Pedigree-Marker Best Linear Unbiased Prediction (BLUP) Model
الموضوعات :ش. برجسته 1 , غ.ر. داشاب 2 , م. رکوعی 3 , م.م. شریعتی 4 , م. وفای واله 5
1 - Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
2 - دانشگاه زابل
3 - Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
4 - Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
5 - Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
الکلمات المفتاحية: Simulation, Accuracy, genomic selection, marker density,
ملخص المقالة :
In order to have successful application of genomic selection, reference population and marker density should be chosen properly. This study purpose was to investigate the accuracy of genomic estimated breeding values in terms of low (5K), intermediate (50K) and high (777K) densities in the simulated populations, when different scenarios were applied about the reference populations selecting. After simulating the historical (undergoing drift and mutation) and recent (undergoing selection) population structures, 800 individuals were remained in reference population. Three scenarios were considered for reducing the reference population number including: 1) 400 individuals which had the highest relationships with the validation set, 2) 400 individuals which had the highest inbreeding, and 3) 400 selected individuals by random. The genomic breeding values were predicted for traits with two heritability levels (0.25 and 0.5) using best linear unbiased prediction (BLUP) with different markers and pedigree information combinations of included pedigree-based BLUP (ABLUP), which was used a numerator relationships matrix (A) only, genomic best linear unbiased prediction (GBLUP) which was used a genomic relationship matrix (G) only, and BLUP|GA, which combined both A and G by using a weight parameter (l). By increasing l, the prediction model was changed from GBLUP (l=0) to ABLUP (l=1). The results indicated that without considering the panel density effects, G matrix (l=0) and A matrix (l=1) usages had the highest and lowest prediction accuracy, respectively. Comparative analyses of different scenarios of reference population selection revealed that all individuals’ inclusion in reference population yielded the highest estimation accuracy for breeding values (p <0.05). On the contrary, using reduced single nucleotide polymorphism (SNP) panels considerably decreased the accuracy of breeding value prediction. Individuals selecting in the reference set with a high genetic relationship to target animals, considerably improved the reduction in genomic prediction accuracy because of small reference population size.
Amiri Roudbar M., Abdollahi-Arpanahi R., Mehrgardi A.A., Mohammadabadi M., TaheriYeganeh A. and Rosa G.J.M. (2018). Estimation of the variance due to parent-of-origin effects for productive and reproductive traits in Lori-Bakhtiari sheep. Small Rumin. Res. 160, 95-102.
Amiri Roudbar M., Mohammadabadi M., Mehrgardi A.A. and Abdollahi-Arpanahi R. (2017). Estimates of variance components due to parent of origin effects for body weight in Iran-Black sheep. Small Rumin. Res. 149, 1-5.
Boichard D., Ducrocq V., Croiseau P. and Fritz S. (2016). Genomic selection in domestic animals: Principles, applications and perspectives. C.R. Biol. 339, 274-277.
Boison S.A., Utsunomiya A.T.H., Santos D.J.A., Neves H.H.R., Carvalheiro R. and Mészáros G. (2017). Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle. J. Dairy Sci. 100, 1-12.
Calus M.P.L. (2010). Genomic breeding value prediction: Methods and procedures. Animal. 4, 157-164.
Calus M.P.L., Meuwissen T.H.E., de Roos A.P.W. and Veerkamp R.F. (2008). Accuracy of genomic selection using different methods to define haplotypes. Genetics. 178, 553-561.
Clark S.A., Hickey J.M., Daetwyler H.D. and Van der Werf J.H. (2012). The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet. Sel. Evol. 44, 4-14.
Daetwyler H.D., Villanueva B. and Woolliams J.A. (2008). Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One. 3, e3395.
De los Campos G., Hickey J.M., Pong-Wong R., Daetwyler H.D. and Calus M.P.L. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics. 193, 327-345.
Habier D., Fernando R.L. and Dekkers J.C.M. (2007). The impact of genetic relationship information on genome-assisted Breeding values. Genetics. 177, 2389-2397.
Hayes B.J. and Goddard M.E. (2008). Technical note: Prediction of breeding values using marker-derived relationship matrices. J. Anim. Sci. 86, 2089-2092.
Hayes B.J., Bowman P.J., Chamberlain A.C. and Goddard M.E. (2009a). Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92, 433-443.
Hayes B.J., Bowman P.J., Chamberlain A.C., Verbyla K. and Goddard M.E. (2009b). Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 24, 41-51.
Henderson C.R. (1984). Applications of Linear Model in Animal Breeding. University of Guelph, Guelph, Ontario, Canada.
Lourenco D.A.L., Fragomeni B.O., Tsuruta S., Aguilar I., Zumbach B., Hawken R.J., Legarra A. and Misztal I. (2015). Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chiken. Genet. Sel. Evol. 47, 56-66.
Lund M.S., De Roos A.P.W., De Vries A.G., Druet T., Ducrocq V., Fritz S. and Schrooten C. (2010). Improving genomic prediction by Euro Genomics collaboration. Pp. 24-29 in 10th World Conf. Genet. Appl. Livest. Prod., Leipzig, Germany.
Meuwissen T.H.E. (2009). Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping. Genet. Sel. Evol. 41, 35-44.
Momen M., AyatollahiMehrgardi A., AmiriRoudbar M., Kranis A., Mercuri Pinto R., Valente B.D., Morota G., Rosa G.J.M. and Gianola D. (2018). Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models. Front. Genet. 9, 455-463.
Mrode R., Ojango J.M.K., Okeyo A.M. and Mwacharo J.M. (2019). Genomic selection and use of molecular tools in breeding programs for indigenous and crossbred cattle in developing countries: Current status and future prospects. Front. Genet. 9, 694-702.
Pérez-Cabal M.A., Vazquez A.I., Gianola D., Rosa G.J.M. and Weigel K.A. (2012). Accuracy of genome-enabled prediction in a dairy cattle population using different cross-validation layouts. Front. Genet. 3, 27-35.
Sargolzaei M. and Schenkel F. (2009). QMSim: A large-scale genome simulator for livestock. Bioinformatics. 25, 680-681.
Silva M.V., dos Santos D.J.A., Solomon A.B., Utsunomiya A.T.H., Carmo A.S., Sonstegard T.S., Cole J.B. and Van Tassell C.P. (2014). The development of genomics applied to dairy breeding. Livest. Sci. 166, 66-75.
Solberg T.R., Sonesson A.K., Woolliams J.A. and Meuwissen T.H. (2008). Genomic selection using different marker types and densities. J. Anim. Sci. 86, 2447-2454.
Su G., Madsen P., Nielsen U.S., Mäntysaari E.A., Aamand G.P., Christensen O.F. and Lund M.S. (2012). Genomic prediction for Nordic Red cattle using one-step and selection index blending. J. Dairy Sci. 95, 909-917.
Teimourian M., Shariati M.M. and Aslaminejad A. (2015). Comparing various statistical methods in genomic selection of Holstein populations. Anim. Prod. Res. 7, 198-203.
Van Raden P.M. (2008). Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414-4423.
Van Raden P.M., Van Tassell C.P., Wiggans G.R., Sonstegard T.S., Schnabel R.D., Taylor J.F. and Schenkel F.S. (2009). Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92, 16-24.
Villumsen T.M., Janss L. and Lund M.S. (2009). The importance of haplotype length and heritability using genomic selection in dairy cattle. J. Anim. Breed. Genet. 126, 3-13.
Vitezica Z.G., Aguilar I., Misztal I. and Legarra A. (2011). Bias in genomic predictions for populations under selection. Genet. Res. 93, 357-366.
Wang Q., Yu Y., Yuan J., Zhang X., Huang H., Li F. and Xiang J. (2017). Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei. BMC Genet. 18, 45-51.
Wiggans G.R., Cole J.B., Hubbard S.M. and Sonstegard T.S. (2017). Genomic selection in dairy cattle: The USDA experience. Annu. Rev. Anim. Biosci. 5, 309-322.