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Marine Biotechnology

, Volume 20, Issue 5, pp 559–565 | Cite as

Genomic Selection Using BayesCπ and GBLUP for Resistance Against Edwardsiella tarda in Japanese Flounder (Paralichthys olivaceus)

  • Yang Liu
  • Sheng Lu
  • Feng Liu
  • Changwei Shao
  • Qian Zhou
  • Na Wang
  • Yangzhen Li
  • Yingming Yang
  • Yingping Zhang
  • Hejun Sun
  • Weiwei Zheng
  • Songlin ChenEmail author
Short Communication

Abstract

The Japanese flounder is one of the most widely farmed economic flatfish species throughout eastern Asia including China, Korea, and Japan. Edwardsiella tarda is a major species of pathogenic bacteria that causes ascites disease and, consequently, a huge economy loss for Japanese flounder farming. After generation selection, traditional breeding methods can hardly improve the E. tarda resistance effectively. Genomic selection is an effective way to predict the breeding potential of parents and has rarely been used in aquatic breeding. In this study, we chose 931 individuals from 90 families, challenged by E. tarda from 2013 to 2015 as a reference population and 71 parents of these families as selection candidates. 1,934,475 markers were detected via genome sequencing and applied in this study. Two different methods, BayesCπ and GBLUP, were used for genomic prediction. In the reference population, two methods led to the same accuracy (0.946) and Pearson’s correlation results between phenotype and genomic estimated breeding value (GEBV) of BayesCπ and GBLUP were 0.912 and 0.761, respectively. In selection candidates, GEBVs from two methods were highly similar (0.980). A comparison of GEBV with the survival rate of families that were structured by selection candidates showed correlations of 0.662 and 0.665, respectively. This study established a genomic selection method for the Japanese flounder and for the first time applied this to E. tarda resistance breeding.

Keywords

Genomic selection BayesCπ GBLUP Edwardsiella tarda Japanese flounder 

Notes

Acknowledgments

We sincerely thank Prof. Xijiang Yu and Prof. Hengde Li for their assistance with the improvement of our genomic selection algorithm.

Author Contributions

SC initiated, managed, and conceived the research; YL, SL, and FL analyzed the data; QZ, CS, and NW discovered SNPs; YL, YY, YZ, HS, and WZ prepare the sample; YL and SL wrote the paper; and SC revised the paper . All the authors reviewed the manuscript.

Funding

This study was supported by grants from the following: (1) the Central Public-interest Scientific Institution Basal Research Fund, CAFS (No. 2016HY-ZD02); (2) the National Natural Science Foundation of China (31461163005, 31570078); (3) the Taishan Scholar Climbing Program of Shandong Province, China.

Compliance with Ethical Standards

Ethics Statement

The collection and handling of the animals in the study was approved by the Animal Care and Use Committee at the Chinese Academy of Fishery Sciences, and all experimental animal protocols were carried out in accordance with the guidelines for the care and use of laboratory animals at the Chinese Academy of Fishery Sciences.

Competing Interest

The authors declare that there is no conflict of interest.

References

  1. Bangera R, Correa K, Lhorente JP, Figueroa R, Yáñez JM (2017) Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar). BMC Genomics 18(1):121CrossRefPubMedPubMedCentralGoogle Scholar
  2. Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotypephase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84:210–223CrossRefPubMedPubMedCentralGoogle Scholar
  3. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual. The State of Queensland, Department of Primary Industries and Fisheries, BrisbaneGoogle Scholar
  4. Chamberlain AJ, McPartlan HC, Goddard ME (2007) The number of loci that affect milk production traits in dairy cattle. Genetics 177(2):1117–1123CrossRefPubMedPubMedCentralGoogle Scholar
  5. Chen S, Zhang G, Shao C, Huang Q, Liu G, Zhang P, Song W, An N, Chalopin D, Volff JN, Hong Y, Li Q, Sha Z, Zhou H, Xie M, Yu Q, Liu Y, Xiang H, Wang N, Wu K, Yang C, Zhou Q, Liao X, Yang L, Hu Q, Zhang J, Meng L, Jin L, Tian Y, Lian J, Yang J, Miao G, Liu S, Liang Z, Yan F, Li Y, Sun B, Zhang H, Zhang J, Zhu Y, Du M, Zhao Y, Schartl M, Tang Q, Wang J (2014) Whole-genome sequence of a flatfish provides insights into ZW sex chromosome evolution and adaptation to a benthic lifestyle. Nat Genet 46:253–260CrossRefPubMedGoogle Scholar
  6. Cros D, Denis M, Sánchez L, Cochard B, Flori A, Durand-Gasselin T, Nouy B, Omoré A, Pomiès V, Riou V, Suryana E, Bouvet J (2015) Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.). Theor Appl Genet 128(3):397–410CrossRefPubMedGoogle Scholar
  7. de Campos CF, Lopes MS, e Silva FF, Veroneze R, Knol EF, Lopes PS, Guimarães SE (2015) Genomic selection for boar taint compounds and carcass traits in a commercial pig population. Livest Sci 174:10–17CrossRefGoogle Scholar
  8. Dong L, Xiao S, Chen J, Wan L, Wang Z (2016a) Genomic selection using extreme phenotypes and pre-selection of SNPs in large yellow croaker (Larimichthys crocea). Mar Biotechnol 18(5):575–583CrossRefPubMedGoogle Scholar
  9. Dong L, Xiao S, Wang Q, Wang Z (2016b) Comparative analysis of the GBLUP, emBayesB, and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea). BMC Genomics 17(1):460CrossRefPubMedPubMedCentralGoogle Scholar
  10. Forni S, Aguilar I, Misztal I (2011) Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol 43(1):1CrossRefPubMedPubMedCentralGoogle Scholar
  11. Fuji K, Kobayashi K, Hasegawa O, Coimbra MRM, Sakamoto T, Okamoto N (2006) Identification of a single major genetic locus controlling the resistance to lymphocystis disease in Japanese flounder (Paralichthys olivaceus). Aquaculture 254(1):203–210CrossRefGoogle Scholar
  12. Fuji K, Hasegawa O, Honda K, Kumasaka K, Sakamoto T, Okamoto N (2007) Marker-assisted breeding of a lymphocystis disease-resistant Japanese flounder (Paralichthys olivaceus). Aquaculture 272(1):291–295CrossRefGoogle Scholar
  13. Gao H, Christensen OF, Madsen P, Nielsen US, Zhang Y, Lund MS, Su G (2012) Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genet Sel Evol 44(1):8CrossRefPubMedPubMedCentralGoogle Scholar
  14. Geng X, Liu SK, Yuan ZH, Jiang YL, Zhi DG, Liu ZJ (2017) A genome-wide association study reveals that genes with functions for bone development are associated with body conformation in catfish. Mar Biotechnol 19(6):570–578CrossRefPubMedGoogle Scholar
  15. Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinf 12(1):186CrossRefGoogle Scholar
  16. Hayes BJ, Visscher PM, Goddard ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 91(01):47–60CrossRefGoogle Scholar
  17. Knol EF, Nielsen B, Knap PW (2016) Genomic selection in commercial pig breeding. Anim Front 6(1):15–22CrossRefGoogle Scholar
  18. Legarra A, Robert-Granié C, Manfredi E, Elsen JM (2008) Performance of genomic selection in mice. Genetics 180:611–618CrossRefPubMedPubMedCentralGoogle Scholar
  19. Legarra A, Calenge F, Mariani P, Velge P, Beaumont C (2011) Use of a reduced set of single nucleotide polymorphisms for genetic evaluation of resistance to Salmonella carrier state in laying hens. Poult Sci 90(4):731–736CrossRefPubMedGoogle Scholar
  20. Liu F (2015) Genetic analysis and a preliminary genomic selection research of economic traits in Cynoglossus semilaevis. Ph. D. Dissertation. Shanghai: Shanghai Ocean University. (In Chinese)Google Scholar
  21. Longin CFH, Mi X, Würschum T (2015) Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding. Theor Appl Genet 128(7):1297–1306CrossRefPubMedGoogle Scholar
  22. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedPubMedCentralGoogle Scholar
  23. Nguyen TTT, Bowman PJ, Haile-Mariam M, Pryce JE, Hayes BJ (2016) Genomic selection for tolerance to heat stress in Australian dairy cattle. J Dairy Sci 99(4):2849–2862CrossRefPubMedGoogle Scholar
  24. Oliveira HR, Silva FF, Brito LF, Guarini AR, Jamrozik J, Schenkel FS (2018) Comparing deregression methods for genomic prediction of test-day traits in dairy cattle. J Anim Breed Genet 135(2):97–106CrossRefPubMedGoogle Scholar
  25. Pérez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–495CrossRefPubMedPubMedCentralGoogle Scholar
  26. Shao C, Niu Y, Rastas P, Liu Y, Xie Z, Li H, Wang L, Jiang Y, Tai S, Tian Y, Sakamoto T, Chen S (2015) Genome-wide SNP identification for the construction of a high-resolution genetic map of Japanese flounder (Paralichthys olivaceus): applications to QTL mapping of Vibrio anguillarum disease resistance and comparative genomic analysis. DNA Res 22(2):161–170CrossRefPubMedPubMedCentralGoogle Scholar
  27. Shao C, Bao B, Xie Z, Chen X, Li B, Jia X, Yao Q, Orti G, Li W, Li X, Hamre K, Xu J, Wang L, Chen F, Tian Y, Schreiber AM, Wang N, Wei F, Zhang J, Dong Z, Gao L, Gai J, Sakamoto T, Mo S, Chen W, Shi Q, Li H, Xiu Y, Li Y, Xu W, Shi Z, Zhang G, Power DM, Wang Q, Schartl M, Chen S (2017) The genome and transcriptome of Japanese flounder provide insights into flatfish asymmetry. Nat Genet 49(1):119–124CrossRefPubMedGoogle Scholar
  28. Shumbusho F, Raoul J, Astruc JM, Palhiere I, Lemarié S, Fugeray-Scarbel A, Elsen M (2016) Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program. Animal 10(6):1033–1041CrossRefPubMedGoogle Scholar
  29. Song W, Li Y, Zhao Y, Liu Y, Niu Y, Pang R, Miao G, Liao X, Shao C, Gao F, Chen S (2012a) Construction of a high-density microsatellite genetic linkage map and mapping of sexual and growth-related traits in half-smooth tongue sole (Cynoglossus semilaevis). PLoS One 7:e52097CrossRefPubMedPubMedCentralGoogle Scholar
  30. Song W, Pang R, Niu Y, Gao F, Zhao Y, Zhang J, Sun J, Shao C, Liao X, Wang L, Tian Y, Chen S (2012b) Construction of high-density genetic linkage maps and mapping of growth-related quantitative trail loci in the Japanese flounder (Paralichthys olivaceus). PLoS One 7(11):e50404CrossRefPubMedPubMedCentralGoogle Scholar
  31. Tsai HY, Hamilton A, Tinch AE, Guy DR, Gharbi K, Stear MJ, Matika O, Bishop SC, Houston RD (2015) Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array. BMC Genomics 16:969CrossRefPubMedPubMedCentralGoogle Scholar
  32. Vallejo RL, Leeds TD, Fragomeni BO, Gao G, Hernandez AG, Misztal I, Welch TJ, Wiens GD, Palti Y (2016) Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in rainbow trout: insights on genotyping methods and genomic prediction models. Front Genet 7:96CrossRefPubMedPubMedCentralGoogle Scholar
  33. VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423CrossRefPubMedPubMedCentralGoogle Scholar
  34. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92(1):16–24CrossRefPubMedPubMedCentralGoogle Scholar
  35. Wang L, Fan C, Liu Y, Zhang Y, Liu S, Sun D, Deng H, Xu Y, Tian Y, Liao X, Xie M, Li W, Chen S (2014) A genome scan for quantitative trait loci associated with Vibrio anguillarum infection resistance in Japanese flounder (Paralichthys olivaceus) by bulked segregant analysis. Mar Biotechnol 16(5):513–521CrossRefPubMedGoogle Scholar
  36. Wang L, Liu P, Huang S, Ye B, Chua E, Wan Z, Yue G (2017) Genome-wide association study identifies loci associated with resistance to viral nervous necrosis disease in Asian seabass. Mar Biotechnol 16(3):255–265CrossRefGoogle Scholar
  37. Weigel KA, Pralle RS, Adams H, Cho K, Do C, White HM (2017) Prediction of whole-genome risk for selection and management of hyperketonemia in Holstein dairy cattle. J Anim Breed Genet 134(3):275–285CrossRefPubMedGoogle Scholar
  38. Whittaker JC, Thompson R, Denham MC (2000) Marker-assisted selection using ridge regression. Genet Res 75(02):249–252CrossRefPubMedGoogle Scholar
  39. Wolc A, Stricker C, Arango J, Settar P, Fulton JE, O’Sullivan NP, Preisinger R, Habier D, Fernando R, Garrick D, Lamont SJ, Dekkers JCM (2011) Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genet Sel Evol 43(1):5CrossRefPubMedPubMedCentralGoogle Scholar
  40. Yue GH (2014) Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish Fish 15(3):376–396CrossRefGoogle Scholar
  41. Zhang G, Fang X, Guo X, Li L, Luo R, Xu F, Yang P, Zhang L, Wang X, Qi H, Xiong Z, Que H, Xie Y, Holland PW, Paps J, Zhu Y, Wu F, Chen Y, Wang J, Peng C, Meng J, Yang L, Liu J, Wen B, Zhang N, Huang Z, Zhu Q, Feng Y, Mount A, Hedgecock D, Xu Z, Liu Y, Domazet-Lošo T, Du Y, Sun X, Zhang S, Liu B, Cheng P, Jiang X, Li J, Fan D, Wang W, Fu W, Wang T, Wang B, Zhang J, Peng Z, Li Y, Li N, Wang J, Chen M, He Y, Tan F, Song X, Zheng Q, Huang R, Yang H, Du X, Chen L, Yang M, Gaffney PM, Wang S, Luo L, She Z, Ming Y, Huang W, Zhang S, Huang B, Zhang Y, Qu T, Ni P, Miao G, Wang J, Wang Q, Steinberg CE, Wang H, Li N, Qian L, Zhang G, Li Y, Yang H, Liu X, Wang J, Yin Y, Wang J (2012) The oyster genome reveals stress adaptation and complexity of shell formation. Nature 490(7418):49–54CrossRefPubMedGoogle Scholar
  42. Zhao L, Li YP, Li YJ, Yu JC, Liao H, Wang SY, Lv J, Liang J, Huang XT, Bao ZM (2017) A genome-wide association study identifies the genomic region associated with shell color in yesso scallop, Patinopecten yessoensis. Mar Biotechnol 19(3):301–309CrossRefPubMedGoogle Scholar
  43. Zheng WW, Chen SL, Li ZY, Wei ZF, Gao J, Li YZ, Liu Y, Tian YS, Liu ST, Sun DQ, Yang YM, Wang L (2016) Analyzing of heritability and breeding value of disease resistance for Edwardsiella tarda in Japanese flounder (Paralichthys olivaceus). J Agric Biotechnol 24(8):1181–1189 (In Chinese)Google Scholar
  44. Zhong XX, Wang XZ, Zhou T, Jin YL, Tan SX, Jiang C, Geng X, Li N, Shi HT, Zeng QF (2017) Genome-wide association study reveals multiple novel QTL associated with low oxygen tolerance in hybrid catfish. Mar Biotechnol 19(4):379–390CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yang Liu
    • 1
    • 2
  • Sheng Lu
    • 1
    • 2
    • 3
  • Feng Liu
    • 1
    • 2
    • 4
  • Changwei Shao
    • 1
    • 2
  • Qian Zhou
    • 1
    • 2
  • Na Wang
    • 1
    • 2
  • Yangzhen Li
    • 1
    • 2
  • Yingming Yang
    • 1
    • 2
  • Yingping Zhang
    • 1
    • 2
  • Hejun Sun
    • 1
    • 2
  • Weiwei Zheng
    • 1
    • 2
  • Songlin Chen
    • 1
    • 2
    Email author
  1. 1.Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences (CAFS), Key Laboratory for Sustainable Development of Marine Fisheries, Ministry of AgricultureQingdaoChina
  2. 2.Laboratory for Marine Fisheries Science and Food Production ProcessesQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  3. 3.College of Marine Life ScienceOcean University of ChinaQingdaoChina
  4. 4.Marine and Fishery Institute of Zhejiang Ocean UniversityZhoushanChina

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