DOI QR코드

DOI QR Code

Comparison of genome-wide association and genomic prediction methods for milk production traits in Korean Holstein cattle

  • Lee, SeokHyun ;
  • Dang, ChangGwon ;
  • Choy, YunHo ;
  • Do, ChangHee ;
  • Cho, Kwanghyun ;
  • Kim, Jongjoo ;
  • Kim, Yousam ;
  • Lee, Jungjae
  • Received : 2018.11.12
  • Accepted : 2019.01.11
  • Published : 2019.07.01

Abstract

Objective: The objectives of this study were to compare identified informative regions through two genome-wide association study (GWAS) approaches and determine the accuracy and bias of the direct genomic value (DGV) for milk production traits in Korean Holstein cattle, using two genomic prediction approaches: single-step genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B. Methods: Records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected from 265,271 first parity cows. After quality control, 50,765 single-nucleotide polymorphic genotypes were available for analysis. In GWAS for ss-GBLUP (ssGWAS) and Bayes-B (BayesGWAS), the proportion of genetic variance for each 1-Mb genomic window was calculated and used to identify informative genomic regions. Accuracy of the DGV was estimated by a five-fold cross-validation with random clustering. As a measure of accuracy for DGV, we also assessed the correlation between DGV and deregressed-estimated breeding value (DEBV). The bias of DGV for each method was obtained by determining regression coefficients. Results: A total of nine and five significant windows (1 Mb) were identified for MY305 using ssGWAS and BayesGWAS, respectively. Using ssGWAS and BayesGWAS, we also detected multiple significant regions for FY305 (12 and 7) and PY305 (14 and 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32 to 0.34), FY305 (0.37 to 0.39), and PY305 (0.35 to 0.36) traits, respectively. The mean biases of DGVs determined using the single-step and Bayesian methods were $1.50{\pm}0.21$ and $1.18{\pm}0.26$ for MY305, $1.75{\pm}0.33$ and $1.14{\pm}0.20$ for FY305, and $1.59{\pm}0.20$ and $1.14{\pm}0.15$ for PY305, respectively. Conclusion: From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of ss-GBLUP in Korean Holstein populations.

Keywords

Bayesian Approach;Genomic Selection;Holstein Cattle;Milk Production;Single-step Genomic Best Linear Unbiased Prediction

References

  1. Weigel K, VanRaden P, Norman H, Grosu H. A 100-year review: methods and impact of genetic selection in dairy cattle-From daughter-dam comparisons to deep learning algorithms. J Dairy Sci 2017;100:10234-50. https://doi.org/10.3168/jds.2017-12954 https://doi.org/10.3168/jds.2017-12954
  2. Meuwissen T, Hayes B, Goddard M. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819-29.
  3. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980 https://doi.org/10.3168/jds.2007-0980
  4. Kachman SD. Incorporation of marker scores into national genetic evaluations. In: 9th genetic prediction workshop; Kansas City, MO, USA; 2008. p. 92-8.
  5. Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci 2009;92:4648-55. https://doi.org/10.3168/jds.2009-2064 https://doi.org/10.3168/jds.2009-2064
  6. Fernando RL, Cheng H, Golden BL, Garrick DJ. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet Sel Evol 2016;48:96. https://doi.org/10.1186/s12711-016-0273-2 https://doi.org/10.1186/s12711-016-0273-2
  7. Fragomeni B, Lourenco D, Tsuruta S, et al. Hot topic: use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes. J Dairy Sci 2015;98:4090-4. https://doi.org/10.3168/jds.2014-9125 https://doi.org/10.3168/jds.2014-9125
  8. Vitezica Z, Aguilar I, Misztal I, Legarra A. Bias in genomic predictions for populations under selection. Genet Res 2011;93:357-66. https://doi.org/10.1017/S001667231100022X https://doi.org/10.1017/S001667231100022X
  9. Lee J, Kachman SD, Spangler ML. The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle. J Anim Sci 2017;95:3406-14. https://doi.org/10.2527/jas.2017.1604
  10. Meuwissen T, Hayes B, Goddard M. Genomic selection: A paradigm shift in animal breeding. Anim Front 2016;6:6-14. https://doi.org/10.2527/af.2016-0002
  11. Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS. Genomic selection in dairy cattle: The USDA experience. Annu Rev Anim Biosci 2017;5:309-27. https://doi.org/10.1146/annurevanimal-021815-111422 https://doi.org/10.1146/annurev-animal-021815-111422
  12. Sargolzaei M, Chesnais J, Schenkel F. FImpute-An efficient imputation algorithm for dairy cattle populations. J Dairy Sci 2011;94:421.
  13. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T. BLUPF90 family of programs. Athens, GA, USA: University of Georgia; 2007.
  14. Wang H, Misztal I, Aguilar I, et al. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front Genet 2014;5:134. https://doi.org/10.3389/fgene.2014.00134
  15. Garrick DJ, Taylor JF, Fernando RL. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet Sel Evol 2009;41:55. https://doi.org/10.1186/1297-9686-41-55 https://doi.org/10.1186/1297-9686-41-55
  16. Saatchi M, Schnabel RD, Rolf MM, Taylor JF, Garrick DJ. Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle. Genet Sel Evol 2012;44:38. https://doi.org/10.1186/1297-9686-44-38 https://doi.org/10.1186/1297-9686-44-38
  17. Garrick DJ, Fernando RL. Implementing a QTL detection study (GWAS) using genomic prediction methodology. Genome-wide association studies and genomic prediction: Springer; 2013. p. 275-98.
  18. Fan B, Onteru SK, Du Z-Q, Garrick DJ, Stalder KJ, Rothschild MF. Genome-wide association study identifies loci for body composition and structural soundness traits in pigs. PloS one 2011;6:e14726. https://doi.org/10.1371/journal.pone.0014726 https://doi.org/10.1371/journal.pone.0014726
  19. Dematawewa C, Berger P. Genetic and phenotypic parameters for 305-day yield, fertility, and survival in Holsteins. J Dairy Sci 1998;81:2700-9. https://doi.org/10.3168/jds.S0022-0302(98)75827-8 https://doi.org/10.3168/jds.S0022-0302(98)75827-8
  20. Cho C, Cho K, Choy Y, et al. Estimation of genetic parameters for milk production traits in Holstein dairy cattle. J Anim Sci Technol 2013;55:7-11. https://doi.org/10.5187/JAST.2013.55.1.7 https://doi.org/10.5187/JAST.2013.55.1.7
  21. Nayeri S, Sargolzaei M, Abo-Ismail MK, et al. Genome-wide association for milk production and female fertility traits in Canadian dairy Holstein cattle. BMC Genet 2016;17:75. https://doi.org/10.1186/s12863-016-0386-1
  22. Jiang L, Liu J, Sun D, et al. Genome wide association studies for milk production traits in Chinese Holstein population. PloS one 2010;5:e13661. https://doi.org/10.1371/journal.pone.0013661 https://doi.org/10.1371/journal.pone.0013661
  23. Kaupe B, Brandt H, Prinzenberg E, Erhardt G. Joint analysis of the influence of CYP11B1 and DGAT1 genetic variation on milk production, somatic cell score, conformation, reproduction, and productive lifespan in German Holstein cattle. J Anim Sci 2007;85:11-21. https://doi.org/10.2527/jas.2005-753 https://doi.org/10.2527/jas.2005-753
  24. Van Hulzen K, Schopen G, van Arendonk J, et al. Genomewide association study to identify chromosomal regions associated with antibody response to Mycobacterium avium subspecies paratuberculosis in milk of Dutch Holstein-Friesians. J Dairy Sci 2012;95:2740-8. https://doi.org/10.3168/jds.2011-5005 https://doi.org/10.3168/jds.2011-5005
  25. Zeng J, Pszczola M, Wolc A, et al. Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods. BMC Proc 2012;6(Suppl 2):S7. https://doi.org/10.1186/1753-6561-6-S2-S7
  26. Wang H, Misztal I, Aguilar I, Legarra A, Muir W. Genomewide association mapping including phenotypes from relatives without genotypes. Genet Res 2012;94:73-83. https://doi.org/10.1017/S0016672312000274 https://doi.org/10.1017/S0016672312000274
  27. Lee SH, Clark S, van der Werf JH. Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship. PloS one 2017;12:e0189775. https://doi.org/10.1371/journal.pone.0189775 https://doi.org/10.1371/journal.pone.0189775
  28. Su G, Guldbrandtsen B, Gregersen V, Lund M. Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J Dairy Sci 2010;93:1175-83. https://doi.org/10.3168/jds.2009-2192 https://doi.org/10.3168/jds.2009-2192
  29. Ding X, Zhang Z, Li X, et al. Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. J Dairy Sci 2013;96:5315-23. https://doi.org/10.3168/jds.2012-6194 https://doi.org/10.3168/jds.2012-6194
  30. Luan T, Woolliams JA, Lien S, Kent M, Svendsen M, Meuwissen TH. The accuracy of genomic selection in Norwegian red cattle assessed by cross validation. Genetics 2009;183:1119-26. https://doi.org/10.1534/genetics.109.107391 https://doi.org/10.1534/genetics.109.107391

Acknowledgement

Grant : Development of selection technology using fetal genomic information

Supported by : National Institute of Animal Science