Genome-wide Association Study (GWAS) and Its Application for Improving the Genomic Estimated Breeding Values (GEBV) of the Berkshire Pork Quality Traits

  • Lee, Young-Sup ;
  • Jeong, Hyeonsoo ;
  • Taye, Mengistie ;
  • Kim, Hyeon Jeong ;
  • Ka, Sojeong ;
  • Ryu, Youn-Chul ;
  • Cho, Seoae
  • Received : 2015.03.31
  • Accepted : 2015.06.24
  • Published : 2015.11.01


The missing heritability has been a major problem in the analysis of best linear unbiased prediction (BLUP). We introduced the traditional genome-wide association study (GWAS) into the BLUP to improve the heritability estimation. We analyzed eight pork quality traits of the Berkshire breeds using GWAS and BLUP. GWAS detects the putative quantitative trait loci regions given traits. The single nucleotide polymorphisms (SNPs) were obtained using GWAS results with p value <0.01. BLUP analyzed with significant SNPs was much more accurate than that with total genotyped SNPs in terms of narrow-sense heritability. It implies that genomic estimated breeding values (GEBVs) of pork quality traits can be calculated by BLUP via GWAS. The GWAS model was the linear regression using PLINK and BLUP model was the G-BLUP and SNP-GBLUP. The SNP-GBLUP uses SNP-SNP relationship matrix. The BLUP analysis using preprocessing of GWAS can be one of the possible alternatives of solving the missing heritability problem and it can provide alternative BLUP method which can find more accurate GEBVs.


Best Linear Unbiased Prediction;Genome Wide Association Study;Missing Heritability Problem;Sherman-Morrison-Woodbury Lemma;Single Nucleotide Polymorphism-Genomic Best Linear Unbiased Prediction;Berkshire Pigs


  1. Bolormaa, S., J. E. Pryce, K. Kemper, K. Savin, B. J. Hayes, W. Barendse, Y. Zhang, C. M. Reich, B. A. Mason, and R. J. Bunch et al. 2013. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in bos taurus, bos indicus, and composite beef cattle. J. Anim. Sci. 91:3088-3104.
  2. Davis, C. G. and B.-H. Lin. 2005. Factors affecting us pork consumption US Department of Agriculture, Economic Research Service, Washington, DC, USA.
  3. Eichler, E. E., J. Flint, G. Gibson, A. Kong, S. M. Leal, J. H. Moore, and J. H. Nadeau. 2010. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11:446-450.
  4. Endelman, J. B. 2011. Ridge regression and other kernels for genomic selection with r package rrblup. Plant Genome 4:250-255.
  5. Feero, W. G., A. E. Guttmacher. and T. A. Manolio. 2010. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363:166-176.
  6. Fernando, R. L., J. C. Dekkers, and D. J. Garrick. 2014. A class of bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genet. Sel. Evol. 46:50.
  7. Goddard, M., B. J. Hayes, and T. H. E. Meuwissen. 2011. Using the genomic relationship matrix to predict the accuracy of genomic selection.J. Anim. Breed. Genet. 128:409-421.
  8. Henderson, C. R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423-447.
  9. Hennig, C. 2010. Fpc: Flexible procedures for clustering. R package version 2:0-3., Accessed August 13, 2015.
  10. Jiang, J. 1997. A derivation of linear unbiased predictor. Stat. Probabil. Lett. 32:321-324.
  11. Lee, T., D.-H. Shin, S. Cho, H. S. Kang, S. H. Kim, H.-K. Lee, H. Kim, and K.-S. Seo. 2014a. Genome-wide association study of integrated meat quality-related traits of the duroc pig breed. Asian Australas. J. Anim. 27:303-309.
  12. Lee, Y.-S., H.-J. Kim, S. Cho, and H. Kim. 2014b. The usage of an snp-snp relationship matrix for best linear unbiased prediction (blup) analysis using a community-based cohort study. Genome. Inform. 12:254-260.
  13. Leeds, T. D. 2005. Pork Quality Improvement: Estimates of Genetic Parameters and Evaluation of Novel Selection Criteria. Ph.D. Thesis, The Ohio State University, Columbus, OH, USA.
  14. Legarra, A., I. Aguilar, and I. Misztal. 2009. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92:4656-4663.
  15. Manolio, T. A., F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff, D. J. Hunter, M. I. McCarthy, E. M. Ramos, L. R. Cardon, and A. Chakravarti et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747-753.
  16. Meuwissen, T. H., B. J. Hayes, and M. E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829.
  17. Purcell, S., B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. De Bakker, and M. J. Daly. 2007. Plink: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81:559-575.
  18. Sellier, P. 1998. Genetics of meat and carcass traits. The Genetics of the Pig (Eds. M. Rothschild and A. Ruvinsky). CAB International Wallingford, Oxon, UK: 463-510.
  19. Sherman, J. and W. J. Morrison. 1950. Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Ann. Math. Stat. 21:124-127.
  20. Soller, M., S. Weigend, M. N. Romanov, J. C. M. Dekkers, and S. J. Lamont. 2006. Strategies to assess structural variation in the chicken genome and its associations with biodiversity and biological performance. Poult. Sci. 85:2061-2078.
  21. Woodbury, M. A. 1950. Inverting modified matrices. Memorandum report, Princeton University, Princeton, NJ USA, 42:106.
  22. Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders, D. R. Nyholt, P. A. Madden, A. C. Heath, N. G. Martin, and G. W. Montgomery. 2010. Common snps explain a large proportion of the heritability for human height. Nat. Genet. 42:565-569.
  23. Zhang, H., Z. Wang, S. Wang, and H. Li. 2012a. Progress of genome wide association study in domestic animals. J. Anim. Sci. Biotech. 3:26.
  24. Zhang, Z., J. He, H. Zhang, P. Gao, M. Erbe, H. Simianer, and J. Li. 2014. Results of genome wide association studies improve the accuracy of genomic selection. 10th world congress on genetics applied to livestock production, The Westin Bayshore, Vancouver, BC, Canada, #695 (the poster number).

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Supported by : National Institute of Biological Resources (NIBR)