DOI QR코드

DOI QR Code

Genome and chromosome wide association studies for growth traits in Simmental and Simbrah cattle

  • Rene, Calderon-Chagoya (Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autonoma de Mexico) ;
  • Vicente Eliezer, Vega-Murillo (Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana) ;
  • Adriana, Garcia-Ruiz (Centro Nacional de Investigacion Disciplinaria en Fisiologia y Mejoramiento Animal, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias) ;
  • Angel, Rios-Utrera (Campo Experimental La Posta, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias) ;
  • Guillermo, Martinez-Velazquez (Campo Experimental Santiago Ixcuintla, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias) ;
  • Moises, Montano-Bermudez (Centro Nacional de Investigacion Disciplinaria en Fisiologia y Mejoramiento Animal, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias)
  • Received : 2021.11.25
  • Accepted : 2022.06.27
  • Published : 2023.01.01

Abstract

Objective: The objective of this study was to perform genome (genome wide association studies [GWAS]) and chromosome (CWAS) wide association analyses to identify single nucleotide polymorphisms (SNPs) associated with growth traits in registered Simmental and Simbrah cattle. Methods: The phenotypes were deregressed BLUP EBVs for birth weight, weaning weight direct, weaning weight maternal, and yearling weight. The genotyping was performed with the GGP Bovine 150k chip. After the quality control analysis, 105,129 autosomal SNP from 967 animals (473 Simmental and 494 Simbrah) were used to carry out genotype association tests. The two association analyses were performed per breed and using combined information of the two breeds. The SNP associated with growth traits were mapped to their corresponding genes at 100 kb on either side. Results: A difference in magnitude of posterior probabilities was found across breeds between genome and chromosome wide association analyses. A total of 110, 143, and 302 SNP were associated with GWAS and CWAS for growth traits in the Simmental-, Simbrah- and joint -data analyses, respectively. It stands out from the enrichment analysis of the pathways for RNA polymerase (POLR2G, POLR3E) and GABAergic synapse (GABRR1, GABRR3) for Simmental cattle and p53 signaling pathway (BID, SERPINB5) for Simbrah cattle. Conclusion: Only 6,265% of the markers associated with growth traits were found using CWAS and GWAS. The associated markers using the CWAS analysis, which were not associated using the GWAS, represents information that due to the model and priors was not associated with the traits.

Keywords

Acknowledgement

This article was funded by the National Institute for Forestry, Agricultural and Livestock Research (INIFAP) of Mexico, through the project: 854434754. Also, the provision of information by the Mexican Simmental-Simbrah Breeders Association is greatly appreciated.

References

  1. Barwick SA, Henzell AL. Development successes and issues for the future in deriving and applying selection indexes for beef breeding. Aust J Exp Agric 2005;45:923-33. https://doi.org/10.1071/EA05068
  2. Buzanskas ME, Grossi DA, Ventura RV, et al. Genome-wide association for growth traits in Canchim beef cattle. PLoS One 2014;9:e94802. https://doi.org/10.1371/journal.pone.0094802
  3. Buzanskas ME, Grossi DA, Baldi F, et al. Genetic associations between stayability and reproductive and growth traits in Canchim beef cattle. Livest Sci 2010;132:107-12. https://doi.org/10.1016/j.livsci.2010.05.008
  4. Baldi F, Albuquerque LG, Alencar MM. Random regression models on Legendre polynomials to estimate genetic parameters for weights from birth to adult age in Canchim cattle. J Anim Breed Genet 2010;127:289-99. https://doi.org/10.1111/j.1439-0388.2010.00853.x
  5. Terakado APN, Costa RB, de Camargo GMF, et al. Genome-wide association study for growth traits in Nelore cattle. Animal 2018;12:1358-62. https://doi.org/10.1017/S1751731117003068
  6. Biegelmeyer P, Gulias-Gomes CC, Caetano AR, Steibel JP, Cardoso FF. Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle. BMC Genet 2016;17:32. https://doi.org/10.1186/s12863-016-0339-8
  7. Prakapenka D, Liang Z, Jiang J, Ma L, Da Y. A large-scale genome-wide association study of epistasis effects of production traits and daughter pregnancy rate in u.S. Holstein cattle. Genes (Basel) 2021;12:1089. https://doi.org/10.3390/genes12071089
  8. 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
  9. Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 1998; 7:434-55. https://doi.org/10.1080/10618600.1998.10474787
  10. Akanno EC, Chen L, Abo-Ismail MK, et al. Genome-wide association scan for heterotic quantitative trait loci in multi-breed and crossbred beef cattle. Genet Sel Evol 2018;50:48. https://doi.org/10.1186/s12711-018-0405-y
  11. Wilson MA, Iversen ES, Clyde MA, Schmidler SC, Schildkraut JM. Bayesian model search and multilevel inference for SNP association studies. Ann Appl Stat 2010;4:1342-64. https://doi.org/10.1214/09-AOAS322
  12. Villa-Angulo R, Matukumalli LK, Gill CA, Choi J, Van Tassell CP, Grefenstette JJ. High-resolution haplotype block structure in the cattle genome. BMC Genet 2009;10:19. https://doi.org/10.1186/1471-2156-10-19
  13. Gibbs RA, Taylor JF, Van Tassell CP, et al. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 2009;324:528-32. https://doi.org/10.1126/science.1167936
  14. Porto-Neto LR, Kijas JW, Reverter A. The extent of linkage disequilibrium in beef cattle breeds using high-density SNP genotypes. Genet Sel Evol 2014;46:22. https://doi.org/10.1186/1297-9686-46-22
  15. Canas-Alvarez JJ, Mouresan EF, Varona L, et al. Linkage disequilibrium, persistence of phase, and effective population size in Spanish local beef cattle breeds assessed through a high-density single nucleotide polymorphism chip. J Anim Sci 2016;94:2779-88. https://doi.org/10.2527/jas.2016-0425
  16. den Berg S, Vandenplas J, Eeuwijk FA, Lopes MS, Veerkamp RF. Significance testing and genomic inflation factor using high-density genotypes or whole-genome sequence data. J Anim Breed Genet 2019;136:418-29. https://doi.org/10.1111/jbg.12419
  17. Ball RD. Designing a GWAS: power, sample size, and data structure. In: Methods in molecular biology. Totowa, NJ, USA; Humana Press Inc.; 2013. Vol. 1019, pp. 37-98.
  18. Clyde M, George EI. Model uncertainty. Statist Sci 2004;19:81-94. https://doi.org/10.1214/088342304000000035
  19. Cheung SH, Beck JL. Calculation of posterior probabilities for bayesian model class assessment and averaging from posterior samples based on dynamic system data. Comput Civ Infrastruct Eng 2010;25:304-21. https://doi.org/10.1111/j.1467-8667.2009.00642.x
  20. Duggal P, Gillanders EM, Holmes TN, Bailey-Wilson JE. Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies. BMC Genomics 2008;9:516. https://doi.org/10.1186/1471-2164-9-516
  21. Nicodemus KK, Liu W, Chase GA, Tsai YY, Fallin MD. Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms. BMC Genet 2005;6:S78. https://doi.org/10.1186/1471-2156-6-S1-S78
  22. Paim TP, Hay EHA, Wilson C, et al. Dynamics of genomic architecture during composite breed development in cattle. Anim Genet 2020;51:224-34. https://doi.org/10.1111/age.12907
  23. Tang J, Hu W, Chen S, et al. The genetic mechanism of high prolificacy in small tail han sheep by comparative proteomics of ovaries in the follicular and luteal stages. J Proteomics 2019;204:103394. https://doi.org/10.1016/j.jprot.2019.103394
  24. Penagaricano F, Wang X, Rosa GJMJ, Radunz AE, Khatib H. Maternal nutrition induces gene expression changes in fetal muscle and adipose tissues in sheep. BMC Genomics 2014;15:1034. https://doi.org/10.1186/1471-2164-15-1034
  25. Zhang L, Wang F, Gao G, et al. Genome-wide association study of body weight traits in Inner Mongolia cashmere goats. Front Vet Sci 2021;8:752746. https://doi.org/10.3389/fvets.2021.752746
  26. Lee SH, van der Werf JHJ, Kim NK, et al. QTL and gene expression analyses identify genes affecting carcass weight and marbling on BTA14 in Hanwoo (Korean Cattle). Mamm Genome 2011;22:589. https://doi.org/10.1007/s00335-011-9331-9
  27. Iung LHS, Mulder HA, Neves HHR, Carvalheiro R. Genomic regions underlying uniformity of yearling weight in Nellore cattle evaluated under different response variables. BMC Genomics 2018;19:619. https://doi.org/10.1186/s12864-018-5003-4
  28. Weng Z, Su H, Saatchi M, et al. Genome-wide association study of growth and body composition traits in Brangus beef cattle. Livest Sci 2016;183:4-11. https://doi.org/10.1016/j.livsci.2015.11.011
  29. Londono-Gil M, Rincon Florez JC, Lopez-Herrera A, Gonzalez-Herrera LG. Genome-wide association study for growth traits in blanco orejinero (bon) cattle from Colombia. Livest Sci 2021;243:104366. https://doi.org/10.1016/j.livsci.2020.104366
  30. Lau LY, Nguyen LT, Reverter A, et al. Gene regulation could be attributed to TCF3 and other key transcription factors in the muscle of pubertal heifers. Vet Med Sci 2020;6:695-710. https://doi.org/10.1002/VMS3.278
  31. Kireeva ML, Kashlev M, Burton ZF. RNA polymerase structure, function, regulation, dynamics, fidelity, and roles in gene expression. Chem Rev 2013;113:8325-30. https://doi.org/10.1021/cr400436m
  32. Agrawal A, Khan MJ, Graugnard DE, et al. Prepartal energy intake alters blood polymorphonuclear leukocyte transcriptome during the peripartal period in Holstein cows. Bioinform Biol Insights 2017;11:1-17. https://doi.org/10.1177/1177932217704667
  33. Fan H, Wu Y, Zhou X, et al. Pathway-based genome-wide association studies for two meat production traits in Simmental cattle. Sci Rep 2016;5:18389. https://doi.org/10.1038/srep18389
  34. Seoane JR, Dumont F, Girard CL, Bedard L, Matte JJ. Effects of intraventricular injections of gamma-aminobutyric acid and related substances on feeding behavior in satiated sheep. Can J Physiol Pharmacol 1984;62:1296-9. https://doi.org/10.1139/y84-217
  35. Yoon J, Kim H. Multi-tissue observation of the long noncoding RNA effects on sexually biased gene expression in cattle. Asian-Australas J Anim Sci 2019;32:1044-51. https://doi.org/10.5713/ajas.18.0516
  36. Sun Y, Wang C, Sun X, Guo, M. Comparative proteomics of whey and milk fat globule membrane proteins of Guanzhong goat and Holstein cow mature milk. J Food Sci 2019;84:244-53. https://doi.org/10.1111/1750-3841.14428
  37. Zhang Z, Chen Z, Diao S, et al. Identifying the complex genetic architecture of growth and fatness traits in a Duroc pig population. J Integr Agric 2021;20:1607-14. https://doi.org/10.1016/S2095-3119(20)63264-6
  38. Martinez R, Gomez Y, Rocha, J. Genome-wide association study on growth traits in Colombian creole breeds and crossbreeds with Zebu cattle. Genet Mol Res 2014;13:6420-32. https://doi.org/10.4238/2014.August.25.5
  39. Santana MHA, Utsunomiya YT, Neves HHR, et al. Genome-wide association study for feedlot average daily gain in Nellore cattle (Bos indicus). J Anim Breed Genet 2014;131:210-6. https://doi.org/10.1111/JBG.12084
  40. Yang Y, Sadri H, Prehn C, et al. Proteasome activity and expression of mammalian target of rapamycin signaling factors in skeletal muscle of dairy cows supplemented with conjugated linoleic acids during early lactation. J Dairy Sci 2020;103:2829-46. https://doi.org/10.3168/jds.2019-17244
  41. Saatchi M, Schnabel RD, Taylor JF, Garrick DJ. Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds. BMC Genomics 2014;15:442. https://doi.org/10.1186/1471-2164-15-442
  42. Abi Habib W, Brioude F, Edouard T, et al. Genetic disruption of the oncogenic HMGA2-PLAG1-IGF2 pathway causes fetal growth restriction. Genet Med 2018;20:250-8. https://doi.org/10.1038/gim.2017.105
  43. Lee SH, Choi BH, Lim D, et al. Genome-Wide Association Study Identifies Major Loci for Carcass Weight on BTA14 in Hanwoo (Korean Cattle). PLoS One 2013;8:e74677. https://doi.org/10.1371/journal.pone.0074677
  44. Fortes MRS, Reverter A, Zhang Y, et al. Association weight matrix for the genetic dissection of puberty in beef cattle. Proc Natl Acad Sci 2010;107:13642-7. https://doi.org/10.1073/pnas.1002044107
  45. Morris CA, Wilson JA, Bennett GL, Cullen NG, Hickey SM, Hunter JC. Genetic parameters for growth, puberty, and beef cow reproductive traits in a puberty selection experiment. NZ J Agric Res 2000;43:83-91. https://doi.org/10.1080/00288233.2000.9513411