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Influence of population structure on the compilation of the Bonsmara genomic reference population

Published online by Cambridge University Press:  03 October 2017

L. Bosman ,
R. R. van der Westhuizen ,
C. Visser  and
E. van Marle-Köster
L. Bosman
Affiliation:
Department of Animal and Wildlife Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
R. R. van der Westhuizen
Affiliation:
Department of Animal and Wildlife Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
C. Visser
Affiliation:
Department of Animal and Wildlife Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
E. van Marle-Köster*
Affiliation:
Department of Animal and Wildlife Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
*
†E-mail: [email protected]
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Abstract

The most popular beef breed in South Africa is the Bonsmara, a locally developed composite breed adapted to sub-tropical conditions. The establishment of a genomic reference population is currently ongoing for the application of genomic selection. To date, 583 Bonsmara cattle (388 bulls and 195 cows) have been genotyped with the GeneSeek® Genomic Profiler Bovine HD™ Chip (GGP-HD) 80 K chip, and the population structure of the reference population was studied. The average minor allele frequency for the Bonsmara was 0.280 across 56 248 autosomal single-nucleotide polymorphisms (SNPs), whereas the observed and expected heterozygosity values were 0.361 and 0.365, respectively. After pruning the data set for SNPs in linkage disequilibrium, 19 119 SNPs were retained, averaging 659 SNPs per autosomal chromosome. This generated an average SNP density of 1 SNP per 90 kb. Structure analysis revealed a non-homogenous population with a high level of genetic admixture, which may negatively influence genomic breeding value prediction accuracy. Genotyping of a further 990 Bonsmara cattle are pending, using the GeneSeek® GGP-HD 150 K chip. As more animals will be added to the reference population, the profile of the reference population are expected to change in such a way to ensure improved genomic estimated breeding value accuracies.

Keywords

admixturecattleselectionSNP
Type
Full Paper
Information
Advances in Animal Biosciences , Volume 8 , Supplement s1: Improving the quality and sustainability of beef production , October 2017 , pp. s45 - s47
Copyright
© The Animal Consortium 2017 

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References

Alexander, DH, Novembre, J and Lange, K 2009. Fast model-based estimation of ancestry in unrelated individuals. Genome Research 19, 1655–1664.Google Scholar
Bignardi, AB, Santana, ML, Eler, JP and Ferraz, JBS 2014. Models for genetic evaluation of growth of Brazilian Bonsmara cattle. Livestock Science 162, 50–58.CrossRefGoogle Scholar
Blasco, A and Toro, MA 2014. A short critical history of the application of genomics to animal breeding. Livestock Science 166, 4–9.Google Scholar
Boddhireddy, P, Kelly, MJ, Northcutt, S, Prayaga, KC, Rumph, J and DeNise, S 2014. Genomic predictions in Angus cattle: comparisons of sample size, response variables, and clustering methods for cross-validation. Journal of Animal Science 92, 485–497.CrossRefGoogle ScholarPubMed
Bonsma, JC 1980. Cross-breeding, breed creation and the genesis of the Bonsmara. In Livestock production – a global approach, pp. 90–110. Tafelberg Publishers Ltd: Tafelberg, Cape Town.Google Scholar
Buchmann, RW 2014. Genesis – PCA and Admixture Plot Viewer. 0.2.3 ed. University of the Witwatersrand, Johannesburg, South Africa.Google Scholar
Calus, MP 2010. Genomic breeding value prediction: methods and procedures. Animal 4, 157–164.CrossRefGoogle ScholarPubMed
Calus, MPL, de Haas, Y and Veerkamp, RF 2013. Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies. Journal of Dairy Science 96, 6703–6715.Google Scholar
Chang, CC, Chow, CC, Tellier, LC, Vattikuti, S, Purcell, SM and Lee, JJ 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7.Google Scholar
Clark, SA, Hickey, JM, Daetwyler, HD and van der Werf, JH 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. Genetics Selection Evolution 44, 1–9.Google Scholar
Cooper, TA, Wiggans, GR and VanRaden, PM 2013. Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle. Journal of Dairy Science 96, 3336–3339.Google Scholar
Illumina Inc 2016. Bovine SNP 50 Genotyping Bead Chip. Illumina, Inc. Retrieved on 18 May 2016 from www.illumina.com/Documents/products/datasheets/datasheet_bovine_snp50.pdf Google Scholar
Maiwashe, AN, Bradfield, MJ, Theron, HE and Van Wyk, JB 2002. Genetic parameter estimates for body measurements and growth traits in South African Bonsmara cattle. Livestock Production Science 75, 293–300.CrossRefGoogle Scholar
Makina, SO, Muchadeyi, FC, van Marle-Köster, E, MacNeil, MD and Maiwashe, A 2014. Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel. Frontiers in Genetics 5, 333.CrossRefGoogle ScholarPubMed
McClure, MC, Sonstegard, TS, Wiggans, GR, Van Eenennaam, AL, Weber, KL, Penedo, CT, Berry, DP, Flynn, J, Garcia, JF, Carmo, AS, Regitano, LC, Albuquerque, M, Silva, MV, Machado, MA, Coffey, M, Moore, K, Boscher, MY, Genestout, L, Mazza, R, Taylor, JF, Schnabel, RD, Simpson, B, Marques, E, McEwan, JC, Cromie, A, Coutinho, LL, Kuehn, LA, Keele, JW, Piper, EK, Cook, J and Williams, R, Bovine HapMap Consortium, Van Tassell, CP 2013. Imputation of microsatellite alleles from dense SNP genotypes for parentage verification across multiple Bos taurus and Bos indicus breeds. Frontiers in Genetics 4, 176.Google Scholar
Novembre, J, Johnson, T, Bryc, K, Kutalik, Z, Boyko, AR, Auton, A, Indap, A, King, KS, Bergmann, S, Nelson, MR, Stephens, M and Bustamante, CD 2008. Genes mirror geography within Europe. Nature 456, 98–101.Google Scholar
Pszczola, M, Strabel, T, Mulder, HA and Calus, MPL 2012. Reliability of direct genomic values for animals with different relationships within and to the reference population. Journal of Dairy Science 95, 389–400.Google Scholar
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MAR, Bender, D, Maller, J, Sklar, P, de Bakker, PIW, Daly, MJ and Sham, PC 2007. PLINK: a toolset for whole-genome association and population-based linkage analysis. American Journal of Human Genetics 81, 559–575.CrossRefGoogle Scholar
Qwabe, SO, Van Marle-Köster, E, Maiwashe, A and Muchadeyi, FC 2013. Evaluation of the Bovine SNP 50 genotyping array in four South African cattle populations. South African Journal of Animal Science 43, 64–67.Google Scholar
Steyn, Y, van Marle-Köster, E and Theron, HE 2014. Residual feed intake as selection tool in South African Bonsmara cattle. Livestock Science 164, 35–38.Google Scholar
Yang, J, Lee, SH, Goddard, ME and Visscher, PM 2011. GCTA: a tool for genome-wide complex trait analysis. American Journal of Human Genetics 88, 76–82.Google Scholar