Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
권호 표지
DOI QR코드

DOI QR Code

Pathway enrichment and protein interaction network analysis for milk yield, fat yield and age at first calving in a Thai multibreed dairy population

  • Received : 2018.05.12
  • Accepted : 2018.07.15
  • Published : 2019.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population. Methods: Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network. Results: Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC. Conclusion: These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time.

Keywords

References

  1. Koonawootrittriron S, Elzo MA, Thongprapi T. Genetic trends in a Holstein $\times$ other breeds multibreed dairy population in central Thailand. Livest Sci 2009;122:186-92. https://doi.org/10.1016/j.livsci.2008.08.013
  2. Laodim T, Elzo MA, Koonawootrittriron S, et al. Identification of SNP markers associated with milk and fat yields in multibreed dairy cattle using two genetic group structures. Livest Sci 2017;206:95-104. https://doi.org/10.1016/j.livsci.2017.10.015
  3. Yodklaew P, Koonawootrittriron S, Elzo MA, et al. Genomewide association study for lactation characteristics, milk yield and age at first calving in a Thai multibreed dairy cattle population. Agric Nat Res 2017;51:223-30.
  4. Jattawa D, Elzo MA, Koonawootrittriron S, et al. Imputation accuracy from low to moderate density single nucleotide polymorphism chips in a Thai multibreed dairy cattle population. Asian-Australas J Anim Sci 2016;29:464-70. https://doi.org/10.5713/ajas.15.0291
  5. Aguilar I, Misztal I, Johnson DL, et al. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci 2010;93:743-752. https://doi.org/10.3168/jds.2009-2730
  6. Raven LA, Cocks BG, Goddard ME, et al. Genetic variants in mammary development, prolactin signalling and involution pathways explain considerable variation in bovine milk production and milk composition. Genet Sel Evol 2014;46:29. https://doi.org/10.1186/1297-9686-46-29
  7. Edwards SM, Thomsen B, Madsen P, et al. Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle. Genet Sel Evol 2015;47:60. https://doi.org/10.1186/s12711-015-0132-6
  8. Purfield DC, Bradley DG, Evans RD, et al. Genome-wide association study for calving performance using high-density genotypes in dairy and beef cattle. Genet Sel Evol 2015;47:47. https://doi.org/10.1186/s12711-015-0126-4
  9. Sargent FD, Lytton VH, Wall JROG. Test interval method of calculating dairy herd improvement association records. J Dairy Sci 1968;51:170-9. https://doi.org/10.3168/jds.S0022-0302(68)86943-7
  10. Koonawootrittriron S, Elzo MA, Tumwasorn S. Multibreed genetic parameters and predicted genetic values for first lactation 305-d milk yield, fat yield, and fat percentage in a Bos taurus$\times$Bos indicus multibreed dairy population in Thailand. Thai J Agric Sci 2002;35:339-60.
  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. Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. J Dairy Sci 2009;92:4656-63. https://doi.org/10.3168/jds.2009-2061
  13. Misztal I, Tsuruta S, Lourenco D, et al. Manual for BLUPF90 family of programs [Internet]. Athens, GA, USA: University of Georgia; 2015 [cited 2017 Aug 10]. Available from: http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=blupf90_all2.pdf.
  14. Tsuruta S. Average Information REML with several options including EM-REML and heterogeneous residual variances [Internet]. Athens, GA, USA: University of Georgia, USA; 2016 [cited 2017 Aug 25], Available from: http://nce.ads.uga.edu/ wiki/lib/exe/fetch.php?media=blupf90_all4.pdf.
  15. Wang H, Misztal I, Aguilar I, et al. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front Genet 2014;5:134. https://doi.org/10.3389/fgene.2014.00134
  16. Hanna LLH, Riley DG. Mapping genomic markers to closest feature using the R package Map2NCBI. Livest Sci 2014;162:59-65. https://doi.org/10.1016/j.livsci.2014.01.019
  17. Bindea G, Mlecnik B, Hackl H, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009;25:1091-3. https://doi.org/10.1093/bioinformatics/btp101
  18. Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015;43:D447-52. https://doi.org/10.1093/nar/gku1003
  19. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504. https://doi.org/10.1101/gr.1239303
  20. Tang Y, Li M, Wang J, et al. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 2015;127:67-72. https://doi.org/10.1016/j.biosystems.2014.11.005
  21. Zielak-Steciwko AE, Browne JA, McGettigan PA, et al. Expression of microRNAs and their target genes and pathways associated with ovarian follicle development in cattle. Physiol Genomics 2014;46:735-45. https://doi.org/10.1152/physiolgenomics.00036.2014
  22. Haisenleder D, Farris HA, Shapnik MA. The calcium component of gonadotropin-releasing hormone-stimulated luteinizing hormone subunit gene transcription is mediated by calcium/calmodulin-dependent protein kinase type II. Endocrinology 2003;144:2409-16. https://doi.org/10.1210/en.2002-0013
  23. Munshi A, Ramesh R. Mitogen-activated protein kinases and their role in radiation response. Genes Cancer 2013;4:401-8. https://doi.org/10.1177/1947601913485414
  24. Wang W, Pan YW, Wietecha T, et al. Extracellular signal-regulated kinase 5 (ERK5) mediates prolactin-stimulated adult neurogenesis in the subventricular zone and olfactory bulb. J Biol Chem 2013;288:2623-31. https://doi.org/10.1074/jbc.M112.401091
  25. Bionaz M, Loor JJ. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics 2008;9:366. https://doi.org/10.1186/1471-2164-9-366
  26. Clarkson J, Herbison AE. Development of GABA and glutamate signaling at the GnRH neuron in relation to puberty. Mol Cell Endocrinol 2006;254-255:32-8. https://doi.org/10.1016/j.mce.2006.04.036
  27. Liu X, Herbison AE. Dopamine regulation of gonadotropinreleasing hormone neuron excitability in male and female mice. Endocrinology 2013;154:340-50. https://doi.org/10.1210/en.2012-1602
  28. Fortes MR, Reverter A, Zhang Y, et al. Association weight matrix for the genetic dissection of puberty in beef cattle. Proc Natl Acad Sci USA 2010;107:13642-7. https://doi.org/10.1073/pnas.1002044107
  29. Golombek DA, Rosenstein RE. Physiology of circadian entrainment. Physiol Rev 2010;90:1063-102. https://doi.org/10.1152/physrev.00009.2009
  30. Plaut K, Casey T. Does the circadian system regulate lactation? Animal 2012;6:394-402. https://doi.org/10.1017/S1751731111002187
  31. Fu M, Zhang L, Ahmed A, et al. Does circadian disruption play a role in the metabolic-hormonal link to delayed lactogenesis II? Front Nutr 2015;2:4. https://doi.org/10.3389/fnut.2015.00004
  32. Howard JT, Kachman SD, Snelling WM, et al. Beef cattle body temperature during climatic stress: a genome-wide association study. Int J Biometeorol 2014;58:1665-72. https://doi.org/10.1007/s00484-013-0773-5
  33. West JW. Effects of heat-stress on production in dairy cattle. J Dairy Sci 2003;86:2131-44. https://doi.org/10.3168/jds.S0022-0302(03)73803-X
  34. Johnstone ED, Sibley CP, Lowen B, et al. Epidermal growth factor stimulation of trophoblast differentiation requires MAPK11/14 (p38 MAP kinase) activation. Biol Reprod 2005;73:1282-8. https://doi.org/10.1095/biolreprod.105.044206

Cited by

  1. Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle vol.11, pp.5, 2019, https://doi.org/10.3390/ani11051392
  2. Genome-Wide Association Study Using Whole-Genome Sequence Data for Fertility, Health Indicator, and Endoparasite Infection Traits in German Black Pied Cattle vol.12, pp.8, 2021, https://doi.org/10.3390/genes12081163