Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Characterisation of runs of homozygosity and inbreeding coefficients in the red-brown Korean native chickens

  • John Kariuki Macharia (Division of Animal and Dairy Science, Chungnam National University) ;
  • Jaewon Kim (Division of Animal and Dairy Science, Chungnam National University) ;
  • Minjun Kim (Division of Animal and Dairy Science, Chungnam National University) ;
  • Eunjin Cho (Department of Bio-AI Convergence, Chungnam National University) ;
  • Jean Pierre Munyaneza (Division of Animal and Dairy Science, Chungnam National University) ;
  • Jun Heon Lee (Division of Animal and Dairy Science, Chungnam National University)
  • Received : 2023.12.10
  • Accepted : 2024.02.27
  • Published : 2024.08.01
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Abstract

Objective: The analysis of runs of homozygosity (ROH) has been applied to assess the level of inbreeding and identify selection signatures in various livestock species. The objectives of this study were to characterize the ROH pattern, estimate the rate of inbreeding, and identify signatures of selection in the red-brown Korean native chickens. Methods: The Illumina 60K single nucleotide polymorphism chip data of 651 chickens was used in the analysis. Runs of homozygosity were analysed using the PLINK v1.9 software. Inbreeding coefficients were estimated using the GCTA software and their correlations were examined. Genomic regions with high levels of ROH were explored to identify selection signatures. Results: A total of 32,176 ROH segments were detected in this study. The majority of the ROH segments were shorter than 4 Mb. The average ROH inbreeding coefficients (FROH) varied with the length of ROH segments. The means of inbreeding coefficients calculated from different methods were also variable. The correlations between different inbreeding coefficients were positive and highly variable (r = 0.18-1). Five ROH islands harbouring important quantitative trait loci were identified. Conclusion: This study assessed the level of inbreeding and patterns of homozygosity in Red-brown native Korean chickens. The results of this study suggest that the level of recent inbreeding is low which indicates substantial progress in the conservation of red-brown Korean native chickens. Additionally, Candidate genomic regions associated with important production traits were detected in homozygous regions.

Keywords

Acknowledgement

This study was made possible courtesy of the support from the Rural Development Administration (project No: RS-2021-RD010125(PJ016205)) and Chungnam National University, Republic of Korea.

References

  1. Cendron F, Mastrangelo S, Tolone M, Perini F, Lasagna E, Cassandro M. Genome-wide analysis reveals the patterns of genetic diversity and population structure of 8 Italian local chicken breeds. Poult Sci 2021;100:441-51. https://doi.org/10.1016/j.psj.2020.10.023 
  2. Cha J, Choo H, Srikanth K, et al. Genome-wide association study identifies 12 loci associated with body weight at age 8 weeks in Korean native chickens. Genes 2021;12:1170. https://doi.org/10.3390/genes12081170 
  3. Jin S, Park HB, Seo DW, et al. Association of MC1R genotypes with shank color traits in Korean native chicken. Livest Sci 2014;170:1-7. https://doi.org/10.1016/j.livsci.2014.10.001 
  4. Jung S, Bae YS, Kim HJ, et al. Carnosine, anserine, creatine, and inosine 5'-monophosphate contents in breast and thigh meats from 5 lines of Korean native chicken. Poult Sci 2013;92:3275-82. https://doi.org/10.3382/ps.2013-03441 
  5. Kim M, Munyaneza JP, Cho E, et al. Genome-wide association study on the content of nucleotide-related compounds in Korean native chicken breast meat. Animals (Basel) 2023;13:2966. https://doi.org/10.3390/ani13182966 
  6. Caballero A, Rodriguez-Ramilo ST, Avila V, Fernandez J. Management of genetic diversity of subdivided populations in conservation programmes. Conserv Genet 2009;11:409-19. https://doi.org/10.1007/s10592-009-0020-0 
  7. Suh S, Sharma A, Lee S, et al. Genetic diversity, and relationships of Korean chicken breeds based on 30 microsatellite markers. Asian-Australas J Anim Sci 2014;27:1399-405. https://doi.org/10.5713/ajas.2014.14016 
  8. Krupa E, Zakova E, Krupova Z. Evaluation of inbreeding and genetic variability of five pig breeds in czech republic. Asian-Australas J Anim Sci 2015;28:25-36. https://doi.org/10.5713/ajas.14.0251 
  9. Wright S. Coefficients of inbreeding and relationship. Am Nat 1922;56:330-8. https://doi.org/10.1086/279872 
  10. Villanueva B, Fernandez A, Saura M, et al. The value of genomic relationship matrices to estimate levels of inbreeding. Genet Sel Evol 2021;53:42. https://doi.org/10.1186/s12711-021-00635-0 
  11. Keller MC, Visscher PM, Goddard ME. Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics 2011;189:237-49. https://doi.org/10.1534/genetics.111.130922 
  12. Zhang Q, Calus MP, Guldbrandtsen B, Lund MS, Sahana G. Estimation of inbreeding using pedigree, 50k SNP chip genotypes and full sequence data in three cattle breeds. BMC Genetics 2015;16:88. https://doi.org/10.1186/s12863-015-0227-7 
  13. Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF. Runs of homozygosity: windows into population history and trait architecture. Nat Rev Genet 2018;19:220-34. https://doi.org/10.1038/nrg.2017.109 
  14. Dadousis C, Ablondi M, Cipolat-Gotet C, et al. Genomic inbreeding coefficients using imputed genotypes: assessing different estimators in Holstein-Friesian dairy cows. J Dairy Sci 2022;105:5926-45. https://doi.org/10.3168/jds.2021-21125 
  15. Bjelland DW, Weigel KA, Vukasinovic N, Nkrumah JD. Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J Dairy Sci 2013;96:4697-706. https://doi.org/10.3168/jds.2012-6435 
  16. Huang X, Otecko NO, Peng M, et al. Genome-wide genetic structure, and selection signatures for color in 10 traditional Chinese, yellow-feathered chicken breeds. BMC Genomics 2020;21:316. https://doi.org/10.1186/s12864-020-6736-4 
  17. Fedorova ES, Dementieva NV, Shcherbakov YS, Stanishevskaya OI. Identification of key candidate genes in runs of homozygosity of the genome of two chicken breeds, associated with cold adaptation. Biology 2022;11:547. https://doi.org/10.3390/biology11040547 
  18. 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 2012;130:286-93. https://doi.org/10.1111/jbg.12012 
  19. Tian S, Tang W, Zhong Z, et al. Identification of runs of homozygosity islands and functional variants in Wenchang Chicken. Animals (Basel) 2023;13:1645. https://doi.org/10.3390/ani13101645 
  20. Seo D, Lee DH, Choi N, Sudrajad P, Lee SH, Lee JH. Estimation of linkage disequilibrium and analysis of genetic diversity in Korean chicken Lines. PLoS ONE 2018;13:e0192063. https://doi.org/10.1371/journal.pone.0192063 
  21. Salojarvi J. Computational tools for population genomics. In: Rajora OP, editors. Population Genomics. Cham, Switzerland: Springer; 2018. pp. 127-60. https://doi.org/10.1007/13836_2018_57 
  22. Groenen MA, Megens HJ, Zare Y, et al. The development and characterization of a 60k SNP chip for chicken. BMC Genomics 2011;12:274. https://doi.org/10.1186/1471-2164-12-274 
  23. 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 
  24. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 2015;4:7. https://doi.org/10.1186/s13742-015-0047-8 
  25. 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 
  26. The R project for statistical computing [Internet]. Vienna, Austria: The R Foundation; c2023 [cited 2024 Feb 5]. Available from: https://www.r-project.org/ 
  27. 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 
  28. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76-82. https://doi.org/10.1016/j.ajhg.2010.11.011 
  29. VanRaden PM, Olson KM, Wiggans GR, Cole JB, Tooker ME. Genomic inbreeding and relationships among Holsteins, jerseys, and Brown Swiss. J Dairy Sci 2011;94:5673-82. https://doi.org/10.3168/jds.2011-4500 
  30. 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 
  31. 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 
  32. Hu Z. Animal QTL database [Internet]. Ames, IA, USA: Animal QTL database; c2024 [cited 2024 Feb 5]. Available from: https://www.animalgenome.org/QTLdb 
  33. Wu X, Zhou R, Zhang W, et al. Genome-wide scan for runs of homozygosity identifies candidate genes in Wannan Black pigs. Anim Biosci 2021;34:1895-902. https://doi.org/10.5713/ab.20.0679 
  34. Xue Q, Li G, Cao Y, et al. Identification of genes involved in inbreeding depression of reproduction in Langshan chickens. Anim Biosci 2021;34:975-84. https://doi.org/10.5713/ajas.20.0248 
  35. Wang Q, Zhang J, Wang H, et al. Estimates of genomic inbreeding and identification of candidate regions in Beijing-You chicken populations. Anim Genet 2023;54:155-65. https://doi.org/10.1111/age.13286 
  36. 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 
  37. 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 2012;130:286-93. https://doi.org/10.1111/jbg.12012 
  38. Hewett AM, Stoffel MA, Peters L, Johnston SE, Pemberton JM. Selection, recombination, and population history effects on runs of homozygosity (ROH) in wild red deer (cervus elaphus). Heredity 2023;130:242-50. https://doi.org/10.1038/s41437-023-00602-z 
  39. Xue J, Peng J, Yuan M, et al. NELL1 promotes high-quality bone regeneration in rat femoral distraction osteogenesis model. Bone 2011;48:485-95. https://doi.org/10.1016/j.bone.2010.10.166 
  40. Lee J, Karnuah AB, Rekaya R, Anthony NB, Aggrey SE. Transcriptomic analysis to elucidate the molecular mechanisms that underlie feed efficiency in meat-type chickens. Mol Genet Genom 2015;290:1673-82. https://doi.org/10.1007/s00438-015-1025-7 
  41. Gao C, Du W, Tian K, et al. Analysis of conservation priorities and runs of homozygosity patterns for Chinese indigenous chicken breeds. Animals (Basel) 2023;13:599. https://doi.org/10.3390/ani13040599 
  42. Li Z, Zhang W, Mulholland MW. LGR4 and its role in intestinal protection and energy metabolism. Front Endocrinol 2015;6:131. https://doi.org/10.3389/fendo.2015.00131 
  43. Yang Y, Cong W, Liu J, et al. Constant light in early life induces fear-related behavior in chickens with suppressed melatonin secretion and disrupted hippocampal expression of clock-and BDNF-associated genes. J Anim Sci Biotechnol 2022; 13:67. https://doi.org/10.1186/s40104-022-00720-4 
  44. Zhou S, Ma Y, Zhao D, Mi Y, Zhang C. Transcriptome profiling analysis of underlying regulation of growing follicle development in the chicken. Poult Sci 2020;99:2861-72. https://doi.org/10.1016/j.psj.2019.12.067 
  45. Zhang M, Li D, Zhai Y, et al. The landscape of DNA methylation associated with the transcriptomic network of intramuscular adipocytes generates insight into intramuscular fat deposition in chicken. Front Cell Dev Biol 2020;8:206. https://doi.org/10.3389/fcell.2020.00206 
  46. Zhang Z, Zhong H, Lin S, et al. Polymorphisms of amy1a gene and their association with growth, carcass traits and feed intake efficiency in chickens. Genomics 2021;113:583-94. https://doi.org/10.1016/j.ygeno.2020.10.041 
  47. Bernini F, Bagnato A, Marelli SP, Zaniboni L, Cerolini S, Strillacci MG. Genetic diversity, and identification of homozygosity-rich genomic regions in seven Italian heritage turkey (Meleagris gallopavo) breeds. Genes 2021;12:1342. https://doi.org/10.3390/GENES12091342