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Identification of genomic diversity and selection signatures in Luxi cattle using whole-genome sequencing data

  • Mingyue Hu (Department of Animal Science, Jilin University) ;
  • Lulu Shi (Department of Animal Science, Jilin University) ;
  • Wenfeng Yi (Department of Animal Science, Jilin University) ;
  • Feng Li (Shandong Binzhou Animal Science & Veterinary Medicine Academy) ;
  • Shouqing Yan (Department of Animal Science, Jilin University)
  • Received : 2023.08.15
  • Accepted : 2023.11.28
  • Published : 2024.03.01

Abstract

Objective: The objective of this study was to investigate the genetic diversity, population structure and whole-genome selection signatures of Luxi cattle to reveal its genomic characteristics in terms of meat and carcass traits, skeletal muscle development, body size, and other traits. Methods: To further analyze the genomic characteristics of Luxi cattle, this study sequenced the whole-genome of 16 individuals from the core conservation farm in Shandong region, and collected 174 published genomes of cattle for conjoint analysis. Furthermore, three different statistics (pi, Fst, and XP-EHH) were used to detect potential positive selection signatures related to selection in Luxi cattle. Moreover, gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed to reveal the potential biological function of candidate genes harbored in selected regions. Results: The results showed that Luxi cattle had high genomic diversity and low inbreeding levels. Using three complementary methods (pi, Fst, and XP-EHH) to detect the signatures of selection in the Luxi cattle genome, there were 2,941, 2,221 and 1,304 potentially selected genes identified, respectively. Furthermore, there were 45 genes annotated in common overlapping genomic regions covered 0.723 Mb, including PLAG1 zinc finger (PLAG1), dedicator of cytokinesis 3 (DOCK3), ephrin A2 (EFNA2), DAZ associated protein 1 (DAZAP1), Ral GTPase activating protein catalytic subunit alpha 1 (RALGAPA1), mediator complex subunit 13 (MED13), and decaprenyl diphosphate synthase subunit 2 (PDSS2), most of which were enriched in pathways related to muscle growth and differentiation and immunity. Conclusion: In this study, we provided a series of genes associated with important economic traits were found in positive selection regions, and a scientific basis for the scientific conservation and genetic improvement of Luxi cattle.

Keywords

Acknowledgement

The authors thank Jishan Liu for providing constructive suggestions for this paper.

References

  1. Qanbari S, Pausch H, Jansen S, et al. Classic selective sweeps revealed by massive sequencing in cattle. PLoS Genet 2014;10:e1004148. https://doi.org/10.1371/journal.pgen.1004148
  2. Iqbal N, Liu X, Yang T, et al. Genomic variants identified from whole-genome resequencing of indicine cattle breeds from Pakistan. PLoS One 2019;14:e0215065. https://doi.org/10.1371/journal.pone.0215065
  3. Zhang W, Gao X, Zhang Y, et al. Genome-wide assessment of genetic diversity and population structure insights into admixture and introgression in Chinese indigenous cattle. BMC Genet 2018;19:114. https://doi.org/10.1186/s12863-018-0705-9
  4. The Bovine Hapmap Consortium, Gibbs RA, Taylor JF, 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
  5. Cho IC, Park HB, Ahn JS, et al. A functional regulatory variant of MYH3 influences muscle fiber-type composition and intramuscular fat content in pigs. PLoS Genet 2019;15: e1008279. https://doi.org/10.1371/journal.pgen.1008279
  6. Matukumalli LK, Lawley CT, Schnabel RD, et al. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 2009;4:e5350. https://doi.org/10.1371/journal.pone.0005350
  7. 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
  8. Li Z, Wei S, Li H, et al. Genome-wide genetic structure and differentially selected regions among Landrace, Erhualian, and Meishan pigs using specific-locus amplified fragment sequencing. Sci Rep 2017;7:10063. https://doi.org/10.1038/s41598-017-09969-6
  9. Liu Z, Sun H, Lai W, et al. Genome-wide re-sequencing reveals population structure and genetic diversity of Bohai Black cattle. Anim Genet 2022;53:133-6. https://doi.org/10.1111/age.13155
  10. Liu Z, Bai C, Shi L, et al. Detection of selection signatures in South African Mutton Merino sheep using whole-genome sequencing data. Anim Genet 2022;53:224-9. https://doi.org/10.1111/age.13173
  11. Mao Y, Chang H, Yang Z, et al. Genetic structure and differentiation of three Chinese indigenous cattle populations. Biochem Genet 2007;45:195-209. https://doi.org/10.1007/s10528-006-9061-y
  12. Ge F LH, Li J, et al. Analysis of growth and slaughter performances and meat quality of Luxi cattle. Shandong Agric Sci 2022;54:112-20.
  13. Groeneveld LF, Lenstra JA, Eding H, et al. Genetic diversity in farm animals--a review. Anim Genet 2010;41(Suppl 1):6-31. https://doi.org/10.1111/j.1365-2052.2010.02038.x
  14. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60. https://doi.org/10.1093/bioinformatics/btp324
  15. McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303. https://doi.org/10.1101/gr.107524.110
  16. 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
  17. Addo S, Klingel S, Hinrichs D, Thaller G. Runs of Homozygosity and NetView analyses provide new insight into the genome-wide diversity and admixture of three German cattle breeds. PLoS One 2019;14:e0225847. https://doi.org/10.1371/journal.pone.0225847
  18. 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
  19. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 2009;19:1655-64. https://doi.org/10.1101/gr.094052.109
  20. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 2016;33:1870-4. https://doi.org/10.1093/molbev/msw054
  21. Yan CL, Lin J, Huang YY, et al. Population genomics reveals that natural variation in PRDM16 contributes to cold tolerance in domestic cattle. Zool Res 2022;43:275-84. https://doi.org/10.24272/j.issn.2095-8137.2021.360
  22. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 2007;81:1084-97. https://doi.org/10.1086/521987
  23. Bu D, Luo H, Huo P, et al. KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res 2021;49:W317-25. https://doi.org/10.1093/nar/gkab447
  24. Hu ZL, Park CA, Reecy JM. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB. Nucleic Acids Res 2019;47:D701-10. https://doi.org/10.1093/nar/gky1084
  25. Chen N, Cai Y, Chen Q, et al. Whole-genome resequencing reveals world-wide ancestry and adaptive introgression events of domesticated cattle in East Asia. Nat Commun 2018;9:2337. https://doi.org/10.1038/s41467-018-04737-0
  26. Samani A, Karuppasamy M, English KG, et al. DOCK3 regulates normal skeletal muscle regeneration and glucose metabolism. bioRxiv 2023;02.22.529576. https://doi.org/10.1101/2023.02.22.529576
  27. Sun T, Huang GY, Wang ZH, et al. Selection signatures of Fuzhong Buffalo based on whole-genome sequences. BMC Genomics 2020;21:674. https://doi.org/10.1186/s12864-020-07095-8
  28. Smith RWP, Anderson RC, Smith JWS, Brook M, Richardson WA, Gray NK. DAZAP1, an RNA-binding protein required for development and spermatogenesis, can regulate mRNA translation. RNA 2011;17:1282-95. https://doi.org/10.1261/rna.2717711
  29. Song Y, Xu L, Chen Y, et al. Genome-wide association study reveals the PLAG1 gene for knuckle, biceps and shank weight in simmental beef cattle. PLoS One 2016;11:e0168316. https://doi.org/10.1371/journal.pone.0168316
  30. Li Y, Wang M, Li Q, et al. Transcriptome profiling of longissimus lumborum in Holstein bulls and steers with different beef qualities. PLoS One 2020;15:e0235218. https://doi.org/10.1371/journal.pone.0235218
  31. Zhang T, Mu Y, Zhang D, et al. Determination of microbiological characteristics in the digestive tract of different ruminant species. Microbiologyopen 2019;8:e00769. https://doi.org/10.1002/mbo3.769
  32. Saito R, Kondo NI, Nemoto Y, et al. Genetic population structure of wild boars (Sus scrofa) in fukushima prefecture. Animals (Basel) 2022;12:491. https://doi.org/10.3390/ani12040491
  33. Han Y, Tan T, Li Z, et al. Identification of selection signatures and loci associated with important economic traits in Yunan Black and Huainan pigs. Genes (Basel) 2023;14:655. https://doi.org/10.3390/genes14030655
  34. Ma X, Cheng H, Liu Y, et al. Assessing Genomic diversity and selective pressures in bohai black cattle using wholegenome sequencing data. Animals (Basel) 2022;12:665. https://doi.org/10.3390/ani12050665
  35. Lu X, Yang Y, Zhang Y, et al. The relationship between myofiber characteristics and meat quality of Chinese Qinchuan and Luxi cattle. Anim Biosci 2021;34:743-50. https://doi.org/10.5713/ajas.20.0066
  36. Xia X, Zhang S, Zhang H, et al. Assessing genomic diversity and signatures of selection in Jiaxian Red cattle using wholegenome sequencing data. BMC Genomics 2021;22:43. https://doi.org/10.1186/s12864-020-07340-0
  37. Zheng T, Li P, Li L, Zhang Q. Research advances in and prospects of ornamental plant genomics. Hortic Res 2021;8:65. https://doi.org/10.1038/s41438-021-00499-x
  38. Zwane AA, Schnabel RD, Hoff J, et al. Genome-Wide SNP Discovery in Indigenous Cattle Breeds of South Africa. Front Genet 2019;10:273. https://doi.org/10.3389/fgene.2019.00273
  39. Qiao R, Zhang M, Zhang B, et al. Population genetic structure analysis and identification of backfat thickness loci of Chinese synthetic Yunan pigs. Front Genet 2022;13:1039838. https://doi.org/10.3389/fgene.2022.1039838
  40. Amoasii L, Holland W, Sanchez-Ortiz E, et al. A MED13-dependent skeletal muscle gene program controls systemic glucose homeostasis and hepatic metabolism. Genes Dev 2016;30:434-46. https://doi.org/10.1101/gad.273128.115
  41. Rehman MS, Hassan FU, Rehman ZU, et al. Comparative genomic characterization of relaxin peptide family in cattle and buffalo. Biomed Res Int 2022;2022:1581714. https://doi.org/10.1155/2022/1581714
  42. 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
  43. Bongiorni S, Mancini G, Chillemi G, Pariset L, Valentini A. Identification of a short region on chromosome 6 affecting direct calving ease in Piedmontese cattle breed. PLoS One 2012;7:e50137. https://doi.org/10.1371/journal.pone.0050137
  44. Boyko AR, Brooks SA, Behan-Braman A, et al. Genomic analysis establishes correlation between growth and laryngeal neuropathy in Thoroughbreds. BMC Genomics 2014;15:259. https://doi.org/10.1186/1471-2164-15-259
  45. Nagaraj M, Horing M, Ahonen MA, et al. GOLM1 depletion modifies cellular sphingolipid metabolism and adversely affects cell growth. J Lipid Res 2022;63:100259. https://doi.org/10.1016/j.jlr.2022.100259