DOI QR코드

DOI QR Code

Runs of homozygosity analysis for selection signatures in the Yellow Korean native chicken

  • Jaewon Kim (Division of Animal and Dairy Science, Chungnam National University) ;
  • John Kariuki Macharia (Division of Animal and Dairy Science, Chungnam National University) ;
  • Minjun Kim (Division of Animal and Dairy Science, Chungnam National University) ;
  • Jung Min Heo (Division of Animal and Dairy Science, Chungnam National University) ;
  • Myunghwan Yu (Division of Animal and Dairy Science, Chungnam National University) ;
  • Hyo Jun Choo (Poultry Research Institute, National Institute of Animal Science, Rural Development Administration) ;
  • Jun Heon Lee (Division of Animal and Dairy Science, Chungnam National University)
  • Received : 2024.02.14
  • Accepted : 2024.04.29
  • Published : 2024.10.01

Abstract

Objective: Yellow Korean native chicken (KNC-Y) is one of the five pure Korean indigenous chicken breeds that were restored through a government project in 1992. KNC-Y is recognized for its superior egg production performance compared to other KNC lines. In this study, we performed runs of homozygosity (ROH) analysis to discover selection signatures associated with egg production traits in the KNC-Y population. Methods: A total of 675 DNA samples from KNC-Y were genotyped to generate single nucleotide polymorphism (SNP) data using custom 60K Affymetrix SNP chips. ROH analysis was performed using PLINK software, with predefined parameters set for the analysis. The threshold of ROH island was defined as the top 1% frequency of SNPs withing the ROH among the population. Results: In the KNC-Y population, a total of 29,958 runs of homozygosity (ROH) fragments were identified. The average total length of ROH was 120.84 Mb, with each ROH fragment having an average length of 2.71 Mb. The calculated ROH-based inbreeding coefficient (FROH) was 0.13. Furthermore, we revealed the presence of ROH islands on chromosomes 1, 2, 4, 5, 7, 8, and 11. Within the identified regions, a total of 111 genes were annotated, and among them were genes related to economic traits, including PRMT3, ANO5, HDAC4, LSS, PLA2G4A, and PTGS2. Most of the overlapping quantitative trait locus regions with ROH islands were found to be associated with production traits. Conclusion: This study conducted a comprehensive analysis of ROH in the KNC-Y population. Notably, among the findings, the PTGS2 gene is believed to play a crucial role in influencing the laying performance of KNC-Y.

Keywords

Acknowledgement

This research study was funded by the project (RS-2021-RD009516) of the Rural Development Administration, Republic of Korea.

References

  1. Kim KG, Kang BS, Park BH, et al. A study on the change of production performance of 5 strains of Korean native chicken after establishment of varieties. Korean J Poult Sci 2019;46:193-204. https://doi.org/10.5536/KJPS.2019.46.3.193
  2. Jin S, Jayasena DD, Jo C, Lee JH. The breeding history and commercial development of the Korean native chicken. Worlds Poult Sci J 2017;73:163-74. https://doi.org/10.1017/S004393391600088X
  3. Food and Agriculture Organization of the United Nations. Domestic animal diversity information system (DAD-IS) [Internet]. Rome, Italy: FAO; c2024 [cited 2024 Jan 10]. Available from: https://www.fao.org/dad-is/browse-by-country-and-species/en/
  4. Cho S, Manjula P, Kim M, et al. Comparison of selection signatures between korean native and commercial chickens using 600K SNP array data. Genes 2021;12:824. https://doi.org/10.3390/genes12060824
  5. Cho E, Kim M, Kim JH, et al. Application of genomic big data to analyze the genetic diversity and population structure of Korean domestic chickens. J Anim Sci Technol 2023;65:912-21. https://doi.org/10.5187/jast.2023.e8
  6. Kim K, Park B, Jeon I, Choo H, Cha J. Comparison of body weight and egg production ability across nine combinations of Korean indigenous chicken breeds. Korean J Poult Sci 2021;48:161-8. https://doi.org/10.5536/KJPS.2021.48.4.161
  7. Sohn SH, Kim K, Shin KB, et al. Diallel cross combination test for improving the laying performance of Korean native chickens. Korean J Poult Sci 2023;50:133-41. https://doi.org/10.5536/KJPS.2023.50.3.133
  8. Peripolli E, Munari DP, Silva MVGB, Lima ALF, Irgang R, Baldi F. Runs of homozygosity: current knowledge and applications in livestock. Anim Genet 2017;48:255-71. https://doi.org/10.1111/age.12526
  9. Purfield DC, McParland S, Wall E, Berry DP. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds. PLoS ONE 2017;12:e0176780. https://doi.org/10.1371/journal.pone.0176780
  10. Gorssen W, Meyermans R, Janssens S, Buys N. A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species. Genet Sel Evol 2021;53:2. https://doi.org/10.1186/s12711-020-00599-7
  11. Meyermans R, Gorssen W, Buys N, Janssens S. How to study runs of homozygosity using PLINK? a guide for analyzing medium density SNP data in livestock and pet species. BMC Genomics 2020;21:94. https://doi.org/10.1186/s12864-020-6463-x
  12. Lencz T, Lambert C, DeRosse P, et al. Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proc Natl Acad Sci USA 2007;104:19942-7. https://doi.org/10.1073/pnas.0710021104
  13. Purfield DC, Berry DP, McParland S, Bradley DG. Runs of homozygosity and population history in cattle. BMC Genet 2012;13:70. https://doi.org/10.1186/1471-2156-13-70
  14. Mastrangelo S, Ciani E, Sardina MT, et al. Runs of homozygosity reveal genome-wide autozygosity in Italian sheep breeds. Anim Genet 2018;49:71-81. https://doi.org/10.1111/age.12634
  15. McQuillan R, Leutenegger AL, Abdel-Rahman R, et al. Runs of homozygosity in European populations. Am J Hum Genet 2008;83:359-72. https://doi.org/10.1016/j.ajhg.2008.08.007
  16. Kinsella RJ, Kahari A, Haider S, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011;2011:bar030. https://doi.org/10.1093/database/bar030
  17. Fonseca PAS, Suarez-Vega A, Marras G, Canovas A. GALLO: an R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci. GigaScience 2020;9:giaa149. https://doi.org/10.1093/gigascience/giaa149
  18. Hu ZL, Park CA, Wu XL, Reecy JM. Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res 2013;41:D871-9. https://doi.org/10.1093/nar/gks1150
  19. Ferencakovic M, Solkner J, Curik I. Estimating autozygosity from high-throughput information: effects of SNP density and genotyping errors. Genet Sel Evol 2013;45:42. https://doi.org/10.1186/1297-9686-45-42
  20. Ferencakovic M, Hamzic E, Gredler B, et al. Estimates of autozygosity derived from runs of homozygosity: empirical evidence from selected cattle populations. J Anim Breed Genet 2013;130:286-93. https://doi.org/10.1111/jbg.12012
  21. Qanbari S, Gianola D, Hayes B, et al. Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle. BMC Genomics 2011;12:318. https://doi.org/10.1186/1471-2164-12-318
  22. Guo Y, Su A, Tian H, et al. Transcriptomic analysis of spleen revealed mechanism of dexamethasone-induced immune suppression in chicks. Genes (Basel) 2020;11:513. https://doi.org/10.3390/genes11050513
  23. Lieber MR, Ma Y, Pannicke U, Schwarz K. Mechanism and regulation of human non-homologous DNA end-joining. Nat Rev Mol Cell Biol 2003;4:712-20. https://doi.org/10.1038/nrm1202
  24. Kanakachari M, Ashwini R, Chatterjee RN, Bhattacharya TK. Embryonic transcriptome unravels mechanisms and pathways underlying embryonic development with respect to muscle growth, egg production, and plumage formation in native and broiler chickens. Front Genet 2022;13:990849. https://doi.org/10.3389/fgene.2022.990849
  25. Otaify GA, Whyte MP, Gottesman GS, et al. Gnathodiaphyseal dysplasia: severe atypical presentation with novel heterozygous mutation of the anoctamin gene (ANO5). Bone 2018;107:161-71. https://doi.org/10.1016/j.bone.2017.11.012
  26. Zhao J, Shen X, Cao X, et al. HDAC4 regulates the proliferation, differentiation and apoptosis of chicken skeletal muscle satellite cells. Animals (Basel) 2020;10:84.
  27. Liu L, Liu X, Cui H, Liu R, Zhao G, Wen J. Transcriptional insights into key genes and pathways controlling muscle lipid metabolism in broiler chickens. BMC Genomics 2019;20:863. https://doi.org/10.1186/s12864-019-6221-0
  28. Chen X, Zhu W, Du Y, Liu X, Geng Z. Genetic parameters for yolk cholesterol and transcriptional evidence indicate a role of lipoprotein lipase in the cholesterol metabolism of the Chinese wenchang chicken. Front Genet 2019;10:902. https://doi.org/10.3389/fgene.2019.00902
  29. Luo F, Jia R, Ying S, Wang Z, Wang F. Analysis of genes that influence sheep follicular development by different nutrition levels during the luteal phase using expression profiling. Anim Genet 2016;47:354-64. https://doi.org/10.1111/age.12427
  30. Wu X, Jiang L, Xu F, et al. Long noncoding RNAs profiling in ovary during laying and nesting in Muscovy ducks (Cairina moschata). Anim Reprod Sci 2021;230:106762. https://doi.org/10.1016/j.anireprosci.2021.106762