DOI QR코드

DOI QR Code

A genome-wide association study on growth traits of Korean commercial pig breeds using Bayesian methods

  • Received : 2023.10.25
  • Accepted : 2024.03.19
  • Published : 2024.05.01

Abstract

Objective: This study aims to identify the significant regions and candidate genes of growth-related traits (adjusted backfat thickness [ABF], average daily gain [ADG], and days to 90 kg [DAYS90]) in Korean commercial GGP pig (Duroc, Landrace, and Yorkshire) populations. Methods: A genome-wide association study (GWAS) was performed using single-nucleotide polymorphism (SNP) markers for imputation to Illumina PorcineSNP60. The BayesB method was applied to calculate thresholds for the significance of SNP markers. The identified windows were considered significant if they explained ≥1% genetic variance. Results: A total of 28 window regions were related to genetic growth effects. Bayesian GWAS revealed 28 significant genetic regions including 52 informative SNPs associated with growth traits (ABF, ADG, DAYS90) in Duroc, Landrace, and Yorkshire pigs, with genetic variance ranging from 1.00% to 5.46%. Additionally, 14 candidate genes with previous functional validation were identified for these traits. Conclusion: The identified SNPs within these regions hold potential value for future marker-assisted or genomic selection in pig breeding programs. Consequently, they contribute to an improved understanding of genetic architecture and our ability to genetically enhance pigs. SNPs within the identified regions could prove valuable for future marker-assisted or genomic selection in pig breeding programs.

Keywords

References

  1. Kang HS, Nam KC, Li Y, Kim KT, Yoon JT, Seo KS. Estimation of genetic parameters and genetic trends for major economic traits in swine. J Anim Sci Technol 2012;54:89-94. https://doi.org/10.5187/JAST.2012.54.2.89 
  2. Cho KH, Kim SH, Park KD. Changes rate in selection of Yorkshire pig for productive traits using the integrated test records among GGPs. J Korean Data Inform Sci Soc 2016;27:429-35. https://doi.org/10.7465/jkdi.2016.27.2.429 
  3. Choi JG, Cho CI, Choi IS, et al. Genetic parameter estimation in seedstock swine population for growth performances. Asian-Australas J Anim Sci 2013;26:470-5. https://doi.org/10.5713/ajas.2012.12454 
  4. Ruan D, Zhuang Z, Ding R, et al. Weighted single-step GWAS identified candidate genes associated with growth traits in a Duroc pig population. Genes 2021;12:117. https://doi.org/10.3390/genes12010117 
  5. Andersson L, Georges M. Domestic-animal genomics: deciphering the genetics of complex traits. Nat Rev Genet 2004;5:202-12. https://doi.org/10.1038/nrg1294 
  6. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet 2006;7:781-91. https://doi.org/10.1038/nrg1916 
  7. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005;6:95-108. https://doi.org/10.1038/nrg1521 
  8. Sun X, Habier D, Fernando RL, Garrick DJ, Dekkers JCM. Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian methods. BMC Proc 2011;5(Suppl 3):S13. https://doi.org/10.1186/1753-6561-5-S3-S13 
  9. Hoeschele I, Uimari P, Grignola FE, Zhang Q, Gage KM. Advances in statistical methods to map quantitative trait loci in outbred populations. Genetics 1997;147:1445-57. https://doi.org/10.1093/genetics/147.3.1445 
  10. Zou W, Zeng ZB. Statistical methods for mapping multiple QTL. Int J Plant Genomics 2008;2008:286561. https://doi.org/10.1155/2008/286561 
  11. Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 2014;15:478. https://doi.org/10.1186/1471-2164-15-478 
  12. Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson R. ASReml user guide release 4.1 structural specification. Hemel hempstead, England: VSN international ltd; 2015. 
  13. 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 
  14. Kizilkaya K, Fernando RL, Garrick DJ. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J Anim Sci 2010;88:544-51. https://doi.org/10.2527/jas.2009-2064 
  15. Garrick DJ, Fernando RL. Implementing a QTL detection study (GWAS) using genomic prediction methodology. In: Gondro C, van der Werf J, Hayes B, editors. Genome-wide association studies and genomic prediction. Totowa, New Jersey, USA: Humana Press; 2013. p. 275-98. https://doi.org/10.1007/978-1-62703-447-0_11 
  16. Lee JJ, Lee SH, Park JE, et al. Genome-wide association study and genomic predictions for exterior traits in Yorkshire pigs. J Anim Sci 2019;97:2793-802. https://doi.org/10.1093/jas/skz158 
  17. Keever MR. Comparison of the molecular phenotypes of pigs carrying different IGF2 alleles at four developmental time points [master's thesis]. Champaign, IL, USA: University of Illinois at Urbana-Champaign; 2017. 
  18. Casas-Agustench P, Arnett DK, Smith CE, et al. Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two US populations. J Acad Nutr Diet 2014;114:1954-66. https://doi.org/10.1016/j.jand.2014.03.014 
  19. Welzenbach J, Neuhoff C, Heidt H, et al. Integrative analysis of metabolomic, proteomic and genomic data to reveal functional pathways and candidate genes for drip loss in pigs. Int J Mol Sci 2016;17:1426. https://doi.org/10.3390/ijms17091426 
  20. Haque ME, Grasso D, Miller C, Spremulli LL, Saada A. The effect of mutated mitochondrial ribosomal proteins S16 and S22 on the assembly of the small and large ribosomal subunits in human mitochondria. Mitochondrion 2008;8:254-61. https://doi.org/10.1016/j.mito.2008.04.004 
  21. Comuzzie AG, Cole SA, Laston SL, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PloS one 2012;7:e51954. https://doi.org/10.1371/journal.pone.0051954 
  22. Melka MG, Bernard M, Mahboubi A, et al. Genome-wide scan for loci of adolescent obesity and their relationship with blood pressure. J Clin Endocrinol Metab 2012;97:E145-50. https://doi.org/10.1210/jc.2011-1801 
  23. Grindflek E, Szyda J, Liu Z, Lien S. Detection of quantitative trait loci for meat quality in a commercial slaughter pig cross. Mamm Genome 2001;12:299-304. https://doi.org/10.1007/s003350010278 
  24. Li X, Kim SW, Choi JS, et al. Investigation of porcine FABP3 and LEPR gene polymorphisms and mRNA expression for variation in intramuscular fat content. Mol Biol Rep 2010;37:3931-9. https://doi.org/10.1007/s11033-010-0050-1 
  25. Ovilo C, Fernandez A, Noguera JL, et al. Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross. Genet Res 2005;85:57-67. https://doi.org/10.1017/s0016672305007330 
  26. Chothe PP, Chutkan N, Sangani R, et al. Sodium-coupled vitamin C transporter (SVCT2): expression, function, and regulation in intervertebral disc cells. Spine J 2013;13:549-57. https://doi.org/10.1016/j.spinee.2013.01.022 
  27. Zhang T, Gao H, Sahana G, et al. Genome-wide association studies revealed candidate genes for tail fat deposition and body size in the Hulun Buir sheep. J Anim Breed Genet 2019;136:362-70. https://doi.org/10.1111/jbg.12402 
  28. Tozaki T, Kikuchi M, Kakoi H, Hirota KI, Nagata SI. A genome-wide association study for body weight in Japanese Thoroughbred racehorses clarifies candidate regions on chromosomes 3, 9, 15, and 18. J Equine Sci 2017;28:127-34. https://doi.org/10.1294/jes.28.127 
  29. Widmann P, Reverter A, Fortes MRS, et al. A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle. BMC Genomics 2013;14:798. https://doi.org/10.1186/1471-2164-14-798 
  30. D'Alessandro E, Sottile G, Sardina MT, et al. Genome-wide analyses reveal the regions involved in the phenotypic diversity in Sicilian pigs. Anim Genet 2020;51:101-5. https://doi.org/10.1111/age.12887 
  31. Fontanesi L, Schiavo G, Galimberti G, Calo DG, Russo V. A genomewide association study for average daily gain in Italian Large White pigs. J Anim Sci 2014;92:1385-94. https://doi.org/10.2527/jas.2013-7059 
  32. Silva EF, Lopes MS, Lopes PS, Gasparino E. A genome-wide association study for feed efficiency-related traits in a crossbred pig population. Animal 2019;13:2447-56. https://doi.org/10.1017/S1751731119000910 
  33. Blaj I, Tetens J, Preuss S, Bennewitz J, Thaller G. Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs. PLoS one 2018;13:e0205576. https://doi.org/10.1371/journal.pone.0205576 
  34. Tachmazidou I, Suveges D, Min JL, et al. Whole-genome sequencing coupled to imputation discovers genetic signals for anthropometric traits. Am J Hum Genet 2017;100:865-84. https://doi.org/10.1016/j.ajhg.2017.04.014 
  35. Wang LJ, Liu XL, Wang HI, He H, Li ZX, Chen L. Expression analysis, single nucleotide polymorphisms and combined genotypes in candidate genes and their associations with growth and carcass traits in Qinchuan cattle. Mol Biol Rep 2013;40:2335-46. https://doi.org/10.1007/s11033-012-2315-3 
  36. Hotta K, Nakamura M, Nakamura T, et al. Association between obesity and polymorphisms in SEC16B, TMEM18, GNPDA2, BDNF, FAIM2 and MC4R in a Japanese population. J Hum Genet 2009;54:727-31. https://doi.org/10.1038/jhg.2009.106 
  37. Leon-Mimila P, Villamil-Ramirez H, Villalobos-Comparan M, et al. Contribution of common genetic variants to obesity and obesity-related traits in mexican children and adults. PLoS one 2013;8:e70640. https://doi.org/10.1371/journal.pone.0070640 
  38. Serao NVL, Gonzalez-Pena D, Beever JE, et al. Bivariate genome-wide association analysis of the growth and intake components of feed efficiency. PLoS one 2013;8:e78530. https://doi.org/10.1371/journal.pone.0078530 
  39. Valente TS, Baldi F, Sant'Anna AC, Albuquerque LG, Paranhos da Costa MJR. Genome-wide association study between single nucleotide polymorphisms and flight speed in Nellore cattle. PLoS One 2016;11:e0156956. https://doi.org/10.1371/journal.pone.0156956 
  40. Puig-Oliveras A, Revilla M, Castello A, Fernandez AI, Folch JM, Ballester M. Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat. Sci Rep 2016;6:31803. https://doi.org/10.1038/srep31803 
  41. Jing L, Hou Y, Wu H, et al. Transcriptome analysis of mRNA and miRNA in skeletal muscle indicates an important network for differential residual feed intake in pigs. Sci Rep 2015;5:11953. https://doi.org/10.1038/srep11953 
  42. Chen M, Wang J, Wang Y, Wu Y, Fu J, Liu JF. Genome-wide detection of selection signatures in Chinese indigenous Laiwu pigs revealed candidate genes regulating fat deposition in muscle. BMC Genet 2018;19:31. https://doi.org/10.1186/s12863-018-0622-y 
  43. Puig-Oliveras A, Ramayo-Caldas Y, Corominas J, et al. Differences in muscle transcriptome among pigs phenotypically extreme for fatty acid composition. PLoS one 2014;9:e99720. https://doi.org/10.1371/journal.pone.0099720 
  44. Ramayo-Caldas Y, Mercade A, Castello A, et al. Genomewide association study for intramuscular fatty acid composition in an Iberian× Landrace cross. J Anim Sci 2012;90:2883-93. https://doi.org/10.2527/jas.2011-4900