Comparison of Different Genomic Relationship Matrices for Multibreed Weighted Single Step GWAS

Document Type : Research Articles


Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran


Introduction: Genomic best linear unbiased prediction (GBLUP) and Bayesian methods are used for genomic selection (GS) and genome wide association study (GWAS) in animal and plant breeding. The main objective of GWAS is detection of quantitative trait loci (QTL) that is affecting trait. The GBLUP assumes equal variance for all markers but the Bayesian methods assumes specific variance for each marker. When trait are affecting by major QTLs, Bayesian methods have the benefit of marker selection. In weighted GBLUP (WGBLUP), disparate variance of marker specific weights for weighting of all markers are used. If only a deduction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. The weighting factors were calculated using marker effect derived Bayesian methods or iteratively based on single step marker effect. In multibreed genomic evaluation, there are several specific genetic structures into a genomic relationship matrix (G). The block wise genomic relationship matrix (BG) consist of several specific relationship blocks for each and pair breeds. BG can more accurately calculated relationships among animals than G matrix in multibreed genomic evaluations. The aim of this study is comparison G and BG and weighted BG (WBG) in weighted single step GWAS.
Materials and Methods:To conduct our study, we initially simulated two distinct populations, labeled as A and B, utilizing the QMsim software. The simulation involved the creation of two chromosomes, each spanning a length of two morgans. Within each chromosome, we simulated 2500 single nucleotide polymorphisms (SNPs). Subsequently, four traits were simulated, each possessing heritabilities of 0.05 and 0.3, along with varying numbers of quantitative trait loci (QTLs) set at 50 and 500. Following the simulation, we calculated the genetic value (G), the breeding value given by markers (BG), and the weighted breeding value given by markers (WBG) using SNP genotypes for all animals in the study. This comprehensive approach allowed us to evaluate and analyze the genetic and breeding values associated with the simulated traits across the populations. Genomic relationship matrices were used for single step GWAS (SSGWAS) analysis for each trait. 10 iterations was considered for single step SNP effect analyses. Moreover, the SNP effects were obtained by BayesB approach. BayesB effects was used for calculated weighting factors in WBG. Accuracies of methods and number of identified SNPs with explained genetic variance higher than 1% were reported.         
Results and Discussion: using G and BG and WBG in SSGWAS led to identify 14, 16 and 21 SNP with higher than one percentage variance explained, respectively. Moreover, convergent accuracies of WssGBLUP using G and BG and WBG were 0.36, 0.39 and 0.43, respectively. WssGBLUP using WBG could be converged faster than using G and BG. Furthermore, accuracy of WssGBLUP using WBG was significantly more than using G and BG. Multibreed GWAS is led to increase power of model because phenotypic information is severely increase. In multibreed GWAS, relationships among breeds usually are rare or zero but there are several locations among breeds that shared among them and should use those for genomic relationship calculation. In WBG and BG could be accurately calculate pair breeds genomic relationships using sharing pair breeds genomic locations. Principal component analyses showed that WBG was let to strongly increase genomic relationship among animals that is led to improved power of WssGWAS.      
Conclusion: According to recent studies, multibreed genomic evaluation with the WssGBLUP can improve the accuracy of multibreed genomic evaluation, and the results of our study showed that for multibreed genomic evaluation and WssGWAS with the WssGBLUP , instead of the genomic relationship matrix (G), BG or WBG genomic relationship matrices are the better to use.



Main Subjects

  1. Aguilar , I., Misztal, I., Johnson, D. L., Legarra, A., Tsuruta, S., & Lawlor, T. J. (2010). Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93(2),743–752.
  2. Alvarenga, A. B., Veroneze, R., Oliveira, H. R., Marques, D. B. D., Lopes, P. S., Silva, F. F., & Brito, L. F. (2020). Comparing alternative single-step GBLUP approaches and training population designs for genomic evaluation of crossbred animals. Frontiers in Genetics, 11(April),1–19.
  3. Campos, G. D. L., Vazquez, A. I., Fernando, R., Klimentidis, Y. C., Sorensen, D., de los Campos, G., Vazquez, A. I., Fernando, R., Klimentidis, Y. C., & Sorensen, D. (2013). Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genetics, 9(7).
  4. Cesarani, A., Lourenco, D., Tsuruta, S., Legarra, A., Nicolazzi, E. L., VanRaden, P. M., & Misztal, I. (2022). Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor. Journal of Dairy Science, 105(6),5141–5152.
  5. Cole, J. B., van Raden, P. M., O’Connell, J. R., van Tassell, C. P., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F., & Wiggans, G. R. (2009). Distribution and location of genetic effects for dairy traits. Journal of Dairy Science, 92(6),2931–2946.
  6. Daetwyler, H. D., Kemper, K. E., van der Werf, J. H. J., & Hayes, B. J. (2012). Components of the accuracy of genomic prediction in a multi-breed sheep population. Journal of Animal Science, 90(10),3375–3384.
  7. De Roos, A. P. W., Hayes, B. J., Spelman, R. J., & Goddard, M. E. (2008). Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics, 179(3),1503–1512.
  8. Erbe, M., Hayes, B. J., Matukumalli, L. K., Goswami, S., Bowman, P. J., Reich, C. M., Mason, B. A., & Goddard, M. E. (2012). Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science, 95(7),4114–4129.
  9. Gao, H., Christensen, O. F., Madsen, P., Nielsen, U. S., Zhang, Y., Lund, M. S., & Su, G. (2012). Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics Selection Evolution, 44(1),8.
  10. Goddard, M. E., & Hayes, B. J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 10(6),381–391.
  11. Gualdrón Duarte, J. L., Gori, A. S., Hubin, X., Lourenco, D., Charlier, C., Misztal, I., & Druet, T. (2020). Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle. BMC Genomics, 21(1).
  12. Kemper, K. E., Reich, C. M., Bowman, P. J., Vander Jagt, C. J., Chamberlain, A. J., Mason, B. A., Hayes, B. J., & Goddard, M. E. (2015). Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions. Genetics Selection Evolution, 47(1),1–17.
  13. Legarra, A., Christensen, O. F., Aguilar, I., & Misztal, I. (2014). Single Step, a general approach for genomic selection. Livestock Science, 166(1),54–65.
  14. Legarra, A., Christensen, O. F., Vitezica, Z. G., Aguilar, I., & Misztal, I. (2015). Ancestral relationships using metafounders: Finite ancestral populations and across population relationships. Genetics, 200(2),455–468.
  15. Liu, X., Tian, D., Li, C., Tang, B., Wang, Z., Zhang, R., Pan, Y., Wang, Y., Zou, D., Zhang, Z., & Song, S. (2023). GWAS Atlas: An updated knowledgebase integrating more curated associations in plants and animals. Nucleic Acids Research, 51(D1),D969–D976.
  16. Lourenco, D., Legarra, A., Tsuruta, S., Masuda, Y., Aguilar, I., & Misztal, I. (2020). Single-step genomic evaluations from theory to practice: using snp chips and sequence data in blupf90. Genes, 11(7),1–32.
  17. Lund, M. S., Sahana, G., de Koning, D. J., Su, G., & Carlborg, Ö. (2009). Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection. BMC Proceedings, 3(S1),1–8.
  18. Meuwissen, T. H., Solberg, T. R., Shepherd, R., & Woolliams, J. A. (2009). A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value. Genetics Selection Evolution, 41(2),1–10.
  19. Pedrosa, V. B., Boerman, J. P., Gloria, L. S., Chen, S.-Y., Montes, M. E., Doucette, J. S., & Brito, L. F. (2023). Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle. Journal of Dairy Science, 106(4), 2613-2629.
  20. Pérez, P., & De Los Campos, G. (2014). Genome-wide regression and prediction with the BGLR statistical package. Genetics, 198(2),483–495.
  21. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., De Bakker, P. I. W., Daly, M. J., & Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3),559–575.
  22. Raven, L. A., Cocks, B. G., & Hayes, B. J. (2014). Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle. BMC Genomics, 15(1).
  23. Reales, G., & Wallace, C. (2023). Sharing GWAS summary statistics results in more citations. Communications Biology, 6(1),6–11.
  24. Sanchez, M. P., Govignon-Gion, A., Croiseau, P., Fritz, S., Hozé, C., Miranda, G., Martin, P., Barbat-Leterrier, A., Letaïef, R., Rocha, D., Brochard, M., Boussaha, M., & Boichard, D. (2017). Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genetics Selection Evolution, 49(1),68.
  25. Su, G., Christensen, O. F., Janss, L., & Lund, M. S. (2014). Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. Journal of Dairy Science, 97,6547–6559.
  26. Su, G., Guldbrandtsen, B., Gregersen, V. R., & Lund, M. S. (2010). Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. Journal of Dairy Science, 93(3),1175–1183.
  27. Tiezzi, F., & Maltecca, C. (2015). Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix. Genetics Selection Evolution, 47(1),1–13.
  28. van den Berg, I., Boichard, D., & Lund, M. S. (2016). Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds. Journal of Dairy Science, 99(11),8932–8945.
  29. Vanraden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11),4414–4423.
  30. Veroneze, R., Lopes, P. S., Lopes, M. S., Hidalgo, A. M., Guimarães, S. E. F., Harlizius, B., Knol, E. F., van Arendonk, J. A. M., Silva, F. F., & Bastiaansen, J. W. M. (2016). Accounting for genetic architecture in single- and multipopulation genomic prediction using weights from genomewide association studies in pigs. Journal of Animal Breeding and Genetics, 133(3),187–196.
  31. Wang, H., Misztal, I., Aguilar, I., Legarra, A., Fernando, R. L., Vitezica, Z., Okimoto, R., Wing, T., Hawken, R., & Muir, W. M. (2014). Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Frontiers in Genetics, 5(MAY),134.
  32. Wang, H., Misztal, I., Aguilar, I., Legarra, A., & Muir, W. M. (2012). Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research, 94(2),73–83.
  33. Wientjes, Y. C. J., Bijma, P., Vandenplas, J., & Calus, M. P. L. (2017). Multi-population genomic relationships for estimating current genetic variances within and genetic correlations between populations. Genetics, 207(2),503–515.
  34. Wientjes, Y. C. J., Calus, M. P. L., Goddard, M. E., & Hayes, B. J. (2015). Impact of QTL properties on the accuracy of multi-breed genomic prediction. Genetics Selection Evolution, 47(1),42.
  35. Wijesena, H. R., Nonneman, D. J., Snelling, W. M., Rohrer, G. A., Keel, B. N., & Lents, C. A. (2023). gBLUP-GWAS identifies candidate genes, signaling pathways, and putative functional polymorphisms for age at puberty in gilts. Journal of Animal Science.
  36. Wu, X., Lund, M. S., Sun, D., Zhang, Q., & Su, G. (2015). Impact of relationships between test and training animals and among training animals on reliability of genomic prediction. Journal of Animal Breeding and Genetics, 132(5),366–375.
  37. Zhang, X., Lourenco, D., Aguilar, I., Legarra, A., & Misztal, I. (2016). Weighting strategies for single-step genomic BLUP: An iterative approach for accurate calculation of GEBV and GWAS. Frontiers in Genetics, 7(AUG),151.
  38. Zhang, Z., Liu, J., Ding, X., Bijma, P., de Koning, D. J., & Zhang, Q. (2010). Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS ONE, 5(9),1–8.
  39. Zhang, Z., Ober, U., Erbe, M., Zhang, H., Gao, N., He, J., Li, J., & Simianer, H. (2014). Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of Genome Wide Association Studies. PLoS ONE, 9(3),1–12.
  40. Zhou, L., Lund, M. S., Wang, Y., & Su, G. (2014). Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices. Journal of Animal Breeding and Genetics, 131(4).


Volume 15, Issue 4 - Serial Number 56
December 2023
Pages 585-597
  • Receive Date: 15 November 2022
  • Revise Date: 17 April 2023
  • Accept Date: 21 May 2023
  • First Publish Date: 21 May 2023