Animal Bioscience

  • 제37권12호
  • /
  • Pages.2054-2065
  • /
  • 2024
  • /
  • 2765-0189(pISSN)
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  • 2765-0235(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
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Genome-wide association study comparison analysis based on Hanwoo full-sib family

  • Ji-Yeong Kim (Department of Animal Science, Gyeongsang National University) ;
  • Eun-Ho Kim (Department of Animal Science, Gyeongsang National University) ;
  • Ho-Chan Kang (Department of Animal Science, Gyeongsang National University) ;
  • Cheol-Hyun Myung (Department of Animal Science, Gyeongsang National University) ;
  • Il-Keun Kong (Division of Applied Life Science (BK21 Four), Gyeongsang National University) ;
  • Hyun-Tae Lim (Department of Animal Science, Gyeongsang National University)
  • 투고 : 2024.05.07
  • 심사 : 2024.06.13
  • 발행 : 2024.12.01
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초록

Objective: The improvement of carcass traits is essential for the Hanwoo industry because of the Hanwoo grade determination system, and genome-wide association study (GWAS) analysis is an instrumental tool for identifying the genetic factors that impact these traits. While GWAS analysis utilizing family data offers advantages in minimizing genetic bias, research on family-based GWAS in Hanwoo is currently lacking. Methods: This study classified Group A using both parental and offspring genetic information, and Group B based solely on offspring genetic information, to compare GWAS analysis results of Hanwoo carcass traits. Results: A total of 16 significant single nucleotide polymorphism (SNP) markers were identified in Group A, comprising 7 for carcass weight (CWT), 3 for back fat thickness (BFT), and 6 for marbling score (MS). In Group B, 7 significant SNP markers were identified, including 3 for CWT, 1 for eye muscle area, 1 for BFT, and 2 for MS. Functional annotation analysis revealed only one common function related to carcass traits between the groups, while protein-protein interaction analysis indicated more gene interactions in Group A. The reliability of estimated values for common SNP markers identified between the groups was higher in Group A. Conclusion: GWAS analysis utilizing parental genetic information holds greater potential for application, owing to its higher reliability of estimated values and the ability to explore numerous candidate genes.

키워드

과제정보

This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0162182021)" Rural Development Administration, Republic of Korea.

참고문헌

  1. Lee SH, Park BH, Sharma A, et al. Hanwoo cattle: origin, domestication, breeding strategies and genomic selection. J Anim Sci Technol 2014;56:2. https://doi.org/10.1186/2055-0391-56-2
  2. Statistics Korea. Korean statistical information service (KOSIS) [Internet]. Daejeon, Korea: Statistics Korea; c2023 [cited 2024 Mar 1]. Available from: https://kosis.kr/index/index.do
  3. NIAS (National Institute of Animal Science). The materials related animal genetic improvement. Wanju, Korea: National Institute of Animal Science; 2017. pp. 166-202.
  4. Yeo JH, Lin QL. Economic impacts of selection and dissemination of Korean proven bulls. J Korea Acad Indust Coop Soc 2015;16:1101-8. https://doi.org/10.5762/KAIS.2015.16.2.1101
  5. Korea Institute of Animal Products Quality Evaluation (KAPE). Statistical yearbook for livestock product grade determination in 2022 [Internet]. Sejong, Korea: Korea Institute of Animal Products Quality Evaluation; c2023 [cited 2023 Sept 1]. Available from: https://www.ekape.or.kr/board/list.do?menuId=menu138629&nextUrl=
  6. Lee JJ, Choi SD, Dang CG, Kang SN, Kim NS. The effect of carcass traits on economic values in Hanwoo. Korean J Food Sci Anim Resour 2011; 31:603-8. https://doi.org/10.5851/kosfa.2011.31.4.603
  7. Park HR, Eum SH, Park JH, et al. Contribution analysis of carcass traits on auction price in Gyeongsangnam-do Hanwoo. J Agric Life Sci 2015;49:187-95. https://doi.org/10.14397/jals.2015.49.6.187
  8. Lee T, Cho S, Seo KS, Chang J, Kim H, Yoon D. Genetic variants and signatures of selective sweep of Hanwoo population (Korean native cattle). Korean Soc Biochem Mol Biol - BMB Rep 2013;46:346-51. https://doi.org/10.5483/bmbrep.2013.46.7.211
  9. Han JM. Kong HS. Identification of a SNP in cattle HGD gene with its effect on economic trait in Hanwoo. J Life Sci 2014;24:1168-73. https://doi.org/10.5352/JLS.2014.24.11.1168
  10. An NR, Lee SS, Park JE, Chai HH, Cho YM, Lim DJ. Current status of genomic prediction using Multi-omics data in livestock. J Biomed Transl Res 2017;18:151-6. https://doi.org/10.12729/jbtr.2017.18.4.151
  11. Lee SH, Lim D, Jang GW, et al. Genome wide association study to identity QTL for growth traits in Hanwoo. J Anim Sci Technol 2012;54:323-9. https://doi.org/10.5187/jast.2012.54.5.323
  12. Bhuiyan MSA, Lim D, Park M, et al. Functional partitioning of genomic variance and genome-wide association study for carcass traits in Korean Hanwoo cattle using imputed sequence level SNP data. Front Genet 2018;9:217. https://doi.org/10.3389/fgene.2018.00217
  13. Srikanth K, Lee SH, Chung KY, et al. A gene-set enrichment and protein-protein interaction network-based GWAS with regulatory SNPs identifies candidate genes and pathways associated with carcass traits in Hanwoo cattle. Genes 2020;11:316. https://doi.org/10.3390/genes11030316
  14. Connolly S, Heron EA. Review of statistical methodologies for the detection of parent-of-origin effects in family trio genome-wide association data with binary disease traits. Brief Bioinform 2015;16:429-48. https://doi.org/10.1093/bib/bbu017
  15. Howe LJ, Nivard MG, Morris TT, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022;54:581-92. https://doi.org/10.1038/s41588-022-01062-7
  16. Brumpton B, Sanderson E, Heilbron K, et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 2020;11:3519. https://doi.org/10.1038/s41467-020-17117-4
  17. Ramayo-Caldas Y, Renand G, Ballester M, Saintilan R, Rocha D. Multi-breed and multi-trait co-association analysis of meat tenderness and other meat quality traits in three French beef cattle breeds. Genet Sel Evol 2016;48:37. https://doi.org/10.1186/s12711-016-0216-y
  18. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2011;40:D109-14. https://doi.org/10.1093/nar/gkr988
  19. The Gene Ontology Consortium. Gene ontology consortium: going forward. Nucleic Acids Res 2015;43:D1049-56. https://doi.org/10.1093/nar/gku1179
  20. dos Santos Silva DB, Fonseca LFS, Pinheiro DG, et al. Prediction of hub genes associated with intramuscular fat content in Nelore cattle. BMC Genomics 2019;20:520. https://doi.org/10.1186/s12864-019-5904-x
  21. Yuan F, Pan X, Chen L, Zhang YH, Huang T, Cai YD. Analysis of protein-protein functional associations by using gene ontology and KEGG pathway. Biomed Res Int 2019;2019:4963289. https://doi.org/10.1155/2019/4963289
  22. Purcell S, Neale B, Todd-Brown K, et al. Plink: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75. https://doi.org/10.1086/519795
  23. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet 2012;44:821-4. https://doi.org/10.1038/ng.2310
  24. Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou LP, Mi H. Panther: making genome-scale phylogenetics accessible to all. Protein Sci 2022;31:8-22. https://doi.org/10.1002/pro.4218
  25. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. https://doi.org/10.1038/nprot.2008.211
  26. Sherman BT, Hao M, Qiu J, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 2022;50:W216-21. https://doi.org/10.1093/nar/gkac194
  27. Nwogwugwu CP, Kim Y, Chung YJ, et al. Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle. Asian-Australas J Anim Sci 2020;33:1057-67. https://doi.org/10.5713/ajas.19.0021
  28. Alam M, Lee SH, Lee DH, Cho C, Park MN. Genetic analysis of major carcass traits of Korean Hanwoo males raised for thirty months. Animals 2021;11:1792. https://doi.org/10.3390/ani11061792
  29. Eu-Ahsunthornwattana J, Miller EN, Fakiola M, Wellcome Trust Case Control Consortium 2, Jeronimo SM, Blackwell JM, Cordell HJ. Comparison of methods to account for relatedness in genome-wide association studies with family-based data. PLoS Genet 2014;10:e1004445. https://doi.org/10.1371/journal.pgen.1004445
  30. Srikanth K, Lee SH, Chung KY, et al. A gene-set enrichment and protein-protein interaction network-based GWAS with regulatory SNPs identifies candidate genes and pathways associated with carcass traits in hanwoo cattle. Genes 2020;11:316. https://doi.org/10.3390/genes11030316
  31. 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
  32. Nazar M, Abdalla IM, Chen Z, et al. genome-wide association study for udder conformation traits in Chinese Holstein cattle. Animals 2022;12:2542. https://doi.org/10.3390/ani12192542
  33. Li R, Li C, Chen H, et al. Genome-wide scan of selection signatures in Dehong humped cattle for heat tolerance and disease resistance. Anim Genet 2020;51:292-9. https://doi.org/10.1111/age.12896
  34. Rehfeldt C, Te Pas MFW, et al. Advances in research on the prenatal development of skeletal muscle in animals in relation to the quality of muscle-based food. I. regulation of myogenesis and environmental impact. Animal 2011;5:703-17. https://doi.org/10.1017/S1751731110002089
  35. Zhang S, Knight TJ, Reecy JM, et al. Associations of polymorphisms in the promoter I of bovine acetyl-CoA carboxylasealpha gene with beef fatty acid composition. Anim Genet 2010;41:417-20. https://doi.org/10.1111/j.1365-2052.2009.02006.x
  36. Shin SC, Heo JP, Chung ER. Effect of single nucleotide polymorphisms of acetyl-CoA carboxylase α (ACACA) gene on carcass traits in Hanwoo (Korean Cattle). Asian-Australas J Anim Sci 2011;24:744-51. https://doi.org/10.5713/ajas.2011.10396
  37. Du L, Li K, Chang T, et al. Integrating genomics and transcriptomics to identify candidate genes for subcutaneous fat deposition in beef cattle. Genomics 2022;114:110406. https://doi.org/10.1016/j.ygeno.2022.110406
  38. Santana MHA, Gomes RC, Utsunomiya YT, et al. Genome-wide association with residual body weight gain in Bos indicus cattle. Genet Mol Res 2015;14:5229-33. https://doi.org/10.4238/2015.May.18.14
  39. Cai C, Xu J, Huang Y, et al. Differential expression of ACTL8 gene and association study of its variations with growth traits in Chinese cattle. Animals 2019;9:1068. https://doi.org/10.3390/ani9121068
  40. Page G, Lodige I, Kogel D, Scheidtmann KH. AATF, a novel transcription factor that interacts with Dlk/ZIP kinase and interferes with apoptosis. FEBS Lett 1999;462:187-91. https://doi.org/10.1016/s0014-5793(99)01529-x
  41. Rodrigues RTdS, Chizzotti ML, Vital CE, et al. Differences in beef quality between Angus (Bos taurus taurus) and Nellore (Bos taurus indicus) cattle through a proteomic and phosphoproteomic approach. PLoS ONE 2017;12:e0170294. https://doi.org/10.1371/journal.pone.0170294
  42. He M, Wang C, Long XH, et al. Mesencephalic astrocytederived neurotrophic factor ameliorates steatosis in HepG2 cells by regulating hepatic lipid metabolism. World J Gastroenterol 2020;26:1029-41. https://doi.org/10.3748/wjg.v26.i10.1029
  43. Ruiz-Ojeda FJ, Aguilera CM, Ruperez AI, Gil A, Gomez-Llorente C. An analogue of atrial natriuretic peptide (CANP4-23) modulates glucose metabolism in human differentiated adipocytes. Mol Cell Endocrinol 2016;431:101-8. https://doi.org/10.1016/j.mce.2016.05.011
  44. Smith JH. Relation of body size to muscle cell size and number in the chicken. Poult Sci 1963;42:283-90. https://doi.org/10.3382/ps.0420283
  45. Mendoza MN, Raudsepp T, Alshanbari F, Gutierrez G, Ponce de Leon FA. Chromosomal localization of candidate genes for fiber growth and color in alpaca (Vicugna pacos). Front Genet 2019;10:583. https://doi.org/10.3389/fgene.2019.00583
  46. Santri IN, Irham LM, Djalilah GN, et al. Identification of hub genes and potential biomarkers for childhood asthma by utilizing an established bioinformatic analysis approach. Biomedicines 2022;10:2311. https://doi.org/10.3390/biomedicines10092311