Comparison of conventional BLUP and single-step genomic BLUP evaluations for yearling weight and carcass traits in Hanwoo beef cattle using single trait and multi-trait models


Autoři: Hossein Mehrban aff001;  Deuk Hwan Lee aff002;  Masoumeh Naserkheil aff003;  Mohammad Hossein Moradi aff004;  Noelia Ibáñez-Escriche aff005
Působiště autorů: Department of Animal Sciences, Shahrekord University, Shahrekord, Iran aff001;  Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do, Korea aff002;  Department of Animal Sciences, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran aff003;  Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran aff004;  Institute for Animal Science and Technology, Universitat Politècnica de València, València, Spain aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223352

Souhrn

Hanwoo, an important indigenous and popular breed of beef cattle in Korea, shows rapid growth and has high meat quality. Its yearling weight (YW) and carcass traits (backfat thickness, carcass weight- CW, eye muscle area, and marbling score) are economically important for selection of young and proven bulls. However, measuring carcass traits is difficult and expensive, and can only be performed postmortem. Genomic selection has become an appealing procedure for genetic evaluation of these traits (by inclusion of the genomic data) along with the possibility of multi-trait analysis. The aim of this study was to compare conventional best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, using both single-trait (ST-BLUP, ST-ssGBLUP) and multi-trait (MT-BLUP, MT-ssGBLUP) models to investigate the improvement of breeding-value accuracy for carcass traits and YW. The data comprised of 15,279 phenotypic records for YW and 5,824 records for carcass traits, and 1,541 genotyped animals for 34,479 single-nucleotide polymorphisms. Accuracy for each trait and model was estimated only for genotyped animals by five-fold cross-validation. ssGBLUP models (ST-ssGBLUP and MT-ssGBLUP) showed ~19% and ~36% greater accuracy than conventional BLUP models (ST-BLUP and MT-BLUP) for YW and carcass traits, respectively. Within ssGBLUP models, the accuracy of the genomically estimated breeding value for CW increased (19%) when ST-ssGBLUP was replaced with the MT-ssGBLUP model, as the inclusion of YW in the analysis led to a strong genetic correlation with CW (0.76). For backfat thickness, eye muscle area, and marbling score, ST- and MT-ssGBLUP models yielded similar accuracy. Thus, combining pedigree and genomic data via the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions, especially among young animals, for ongoing Hanwoo cattle breeding programs. MT-ssGBLUP is highly recommended when phenotypic records are limited for one of the two highly correlated genetic traits.

Klíčová slova:

Animal genomics – Animal models – Beef – Cattle – Heredity – Meat – Phenotypes – Eye muscles


Zdroje

1. Choi TJ, Alam M, Cho CI, Lee JG, Park B, Kim S, et al. Genetic parameters for yearling weight, carcass traits, and primal-cut yields of Hanwoo cattle. J Anim Sci. 2015;93: 1511–1521. doi: 10.2527/jas.2014-7953 26020173

2. Choy YH, Park BH, Choi TJ, Choi JG, Cho KH, Lee SS, et al. Estimation of relative economic weights of Hanwoo carcass traits based on carcass market price. Asian-Australas J Anim Sci. 2012;25: 1667–1673. doi: 10.5713/ajas.2012.12397 25049531

3. Joo ST, Hwang YH, Frank D. Characteristics of Hanwoo cattle and health implications of consuming highly marbled Hanwoo beef. Meat Sci. 2017;132: 45–51. doi: 10.1016/j.meatsci.2017.04.262 28602574

4. Kim S, Alam M, Park NM. Breeding initiatives for Hanwoo cattle to thrive as a beef industry—A review study. J Anim Breed Genet. 2017;1: 102–124.

5. Park B, Choi T, Kim S, Oh SH. National genetic evaluation (system) of Hanwoo (Korean native cattle). Asian-Australas J Anim Sci. 2013;26: 151–156. doi: 10.5713/ajas.2012.12439 25049770

6. Chen L, Vinsky M, Li C. Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle. Anim Genet. 2015;46: 55–59. doi: 10.1111/age.12238 25393962

7. Rolf MM, Garrick DJ, Fountain T, Ramey HR, Weaber RL, Decker JE, et al. Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle. Genet Sel Evol. 2015;47: 23. doi: 10.1186/s12711-015-0106-8 25884158

8. Mehrban H, Lee DH, Moradi MH, IlCho C, Naserkheil M, Ibanez-Escriche N. Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: Impacts of the genetic architecture. Genet Sel Evol. 2017;49: 1. doi: 10.1186/s12711-016-0283-0 28093066

9. Misztal I, Aggrey SE, Muir WM. Experiences with a single-step genome evaluation. Poult Sci. 2013;92: 2530–2534. doi: 10.3382/ps.2012-02739 23960138

10. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157: 1819–1829. 11290733

11. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: Genomic selection in dairy cattle: Progress and challenges. J Dairy Sci. 2009;92: 433–443. doi: 10.3168/jds.2008-1646 19164653

12. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, et al. Invited review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009;92: 16–24. doi: 10.3168/jds.2008-1514 19109259

13. Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci. 2009;92: 4648–4655. doi: 10.3168/jds.2009-2064 19700728

14. Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. J Dairy Sci. 2009;92: 4656–4663. doi: 10.3168/jds.2009-2061 19700729

15. Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci. 2010;93: 743–752. doi: 10.3168/jds.2009-2730 20105546

16. Ibanez N, Davis S, Noguera JL, Varona L. Genomic information in pig breeding: Science meets industry needs. Livest Sci. 2014;166: 94–100.

17. VanRaden PM. Avoiding bias from genomic pre-selection in converting daughter information across countries. Interbull Bulletin. 2012;45.

18. Onogi A, Ogino A, Komatsu T, Shoji N, Simizu K, Kurogi K, et al. Genomic prediction in Japanese Black cattle: Application of a single-step approach to beef cattle. J Anim Sci. 2014;92: 1931–1938. doi: 10.2527/jas.2014-7168 24782393

19. Gordo DG, Espigolan R, Tonussi RL, Junior GA, Bresolin T, Magalhaes AF, et al. Genetic parameter estimates for carcass traits and visual scores including or not genomic information. J Anim Sci. 2016;94: 1821–1826. doi: 10.2527/jas.2015-0134 27285679

20. Lee J, Cheng H, Garrick D, Golden B, Dekkers J, Park K, et al. Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Genet Sel Evol. 2017;49: 2. doi: 10.1186/s12711-016-0279-9 28093065

21. Tsuruta S, Misztal I, Aguilar I, Lawlor TJ. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J Dairy Sci. 2011;94: 4198–4204. doi: 10.3168/jds.2011-4256 21787955

22. Forni S, Aguilar I, Misztal I. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol. 2011;43: 1.

23. Xiang T, Nielsen B, Su G, Legarra A, Christensen OF. Application of single-step genomic evaluation for crossbred performance in pig. J Anim Sci. 2016;94: 936–948. doi: 10.2527/jas.2015-9930 27065256

24. Chen CY, Misztal I, Aguilar I, Legarra A, Muir WM. Effect of different genomic relationship matrices on accuracy and scale. J Anim Sci. 2011;89: 2673–2679. doi: 10.2527/jas.2010-3555 21454868

25. Guo G, Zhao F, Wang Y, Zhang Y, Du L, Su G. Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet. 2014;15: 30. doi: 10.1186/1471-2156-15-30 24593261

26. Jia Y, Jannink JL. Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics. 2012;192: 1513–1522. doi: 10.1534/genetics.112.144246 23086217

27. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81: 1084–1097. doi: 10.1086/521987 17924348

28. Misztal I, Tsuruta S, Lourenco D, Aguilar I, Legarra A, Vitezica Z. Manual for BLUPF90 family of programs. 2015.

29. Aguilar I, Misztal I, Legarra A, Tsuruta S. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. J Anim Breed Genet. 2011;128: 422–428. doi: 10.1111/j.1439-0388.2010.00912.x 22059575

30. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91: 4414–4423. doi: 10.3168/jds.2007-0980 18946147

31. Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, et al. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol. 2011;43: 40. doi: 10.1186/1297-9686-43-40 22122853

32. Mrode RA. Linear models for the prediction of animal breeding values. Cabi. 2014.

33. Lee SH, Choi BH, Lim D, Gondro C, Cho YM, Dang CG, et al. Genome-wide association study identifies major loci for carcass weight on BTA14 in Hanwoo (Korean cattle). PLoS One. 2013;8: e74677. doi: 10.1371/journal.pone.0074677 24116007

34. Gordo DGM, Espigolan R, Bresolin T, Fernandes Junior GA, Magalhaes AFB, Braz CU, et al. Genetic analysis of carcass and meat quality traits in Nellore cattle. J Anim Sci. 2018; 96: 3558–3564.

35. Bhuiyan MSA, Kim HJ, Lee DH, Lee SH, Cho SH, Yang BS, et al. Genetic parameters of carcass and meat quality traits in different muscles (longissimus dorsi and semimembranosus) of Hanwoo (Korean cattle). J Anim Sci. 2017;95: 3359–3369. doi: 10.2527/jas.2017.1493 28805895

36. Visscher PM, Medland SE, Ferreira MA, Morley KI, Zhu G, Cornes BK, et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2006;2: e41. doi: 10.1371/journal.pgen.0020041 16565746

37. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal. 2012;6: 1565–1571. doi: 10.1017/S1751731112000742 22717310

38. Aguilar I, Misztal I, Tsuruta S, Wiggans GR, Lawlor TJ. Multiple trait genomic evaluation of conception rate in Holsteins. J Dairy Sci. 2011;94: 2621–2624. doi: 10.3168/jds.2010-3893 21524554

39. Baloche G, Legarra A, Salle G, Larroque H, Astruc JM, Robert-Granie C, et al. Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. J Dairy Sci. 2014;97: 1107–1116. doi: 10.3168/jds.2013-7135 24315320

40. Lourenco DA, Tsuruta S, Fragomeni BO, Masuda Y, Aguilar I, Legarra A, et al. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. J Anim Sci. 2015;93: 2653–2662. doi: 10.2527/jas.2014-8836 26115253

41. Calus MP, Veerkamp RF. Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol. 2011;43: 26. doi: 10.1186/1297-9686-43-26 21729282

42. Jiang J, Zhang Q, Ma L, Li J, Wang Z, Liu JF. Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model. Heredity. 2015;115: 29–36. doi: 10.1038/hdy.2015.9 25873147

43. Ismael A, Lovendahl P, Fogh A, Lund MS, Su G. Improving genetic evaluation using a multitrait single-step genomic model for ability to resume cycling after calving, measured by activity tags in Holstein cows. J Dairy Sci. 2017;100: 8188–8196. doi: 10.3168/jds.2017-13122 28780110

44. Cheng H, Kizilkaya K, Zeng J, Garrick D. Genomic prediction from multiple-trait Bayesian regression methods using mixture priors. Genetics 2018;209: 89–103. doi: 10.1534/genetics.118.300650 29514861

45. Karaman E, Lund MS, Anche MT, Janss L. Genomic prediction using multi-trait weighted GBLUP accounting for heterogeneous variances and covariances across the genome. Genetics 2018;8: 3549–3558.

46. Kemper KE, Bowman PJ, Hayes BJ, Visscher PM, Goddard ME. A multi-trait Bayesian method for mapping QTL and genomic prediction. Genet Sel Evol. 2018;50: 10. doi: 10.1186/s12711-018-0377-y 29571285

47. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet. 2009;10: 381–391. doi: 10.1038/nrg2575 19448663


Článek vyšel v časopise

PLOS One


2019 Číslo 10