بررسی ردپای انتخاب و هستی‌شناسی ژن در برخی نژاد‌های گوسفند اهلی و وحشی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم دامی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

2 دانشگاه علوم پزشکی هاروارد، دپارتمان پزشکی ، شهر بوستون، ایالت ماساچوست، آمریکا

چکیده

در طی سالیان طولانی، حیوانات در ﻣﻌﺮض ﻋﻮاﻣﻞ ﻣﺨﺘﻠﻔﯽ ﻣﺎﻧﻨﺪ اﻧﺘﺨﺎب ﻃﺒﯿﻌﯽ، راﻧﺶ ژﻧﺘﯿﮑﯽ و ﺟﻬﺶ­های متعدد ﻗﺮار گرفته­اند، بنابراین چنین ﻋﻮاﻣلی ﺳﺒﺐ ﺗﻐﯿﯿﺮات در ﺑﯿﻦ و داﺧﻞ ﮔﻮﻧﻪ­ها شده است. از سویی دیگر امروزه ﻧﯿﺎز ﺑﻪ اﻓﺰاﯾﺶ ﺗﻮﻟﯿﺪ سبب کاهش تنوع ژنتیکی گونه­ها بوده که ﻧﮕﺮاﻧﯽ­ﻫﺎی ﺷﺪﯾﺪی را ﺑﺮای ﺳﯿﺴﺘﻢ ﺗﻮﻟﯿﺪی ﺣﯿﻮاﻧﺎت در ﺳﺮاﺳﺮ ﺟﻬﺎن ﺑﻪ‌وﺟﻮد آورده اﺳﺖ. هدف از انجام این تحقیق، شناسایی مناطق ژنومی تحت انتخاب در گوسفندان اهلی در مقایسه با گوسفندان وحشی ایران و بررسی هستی‌شناسی ژن­های کاندید مرتبط با صفات اقتصادی می­باشد. در این تحقیق، داده­های دو گروه گوسفند اهلی (106 راس) و وحشی (8 راس) بومی ایران بعد از ویرایش و کنترل کیفیت با نرم‌افزارهای R و Plink مورد آنالیز قرار گرفتند و نتایج حاصل با استفاده از سرورهای آنلاین DAVID، GeneCards و UniProtKB تفسیر شدند. نتایج نهایی نشان دادند، 95 منطقه ژنومی روی 23 کروموزوم در گوسفند وحشی و اهلی با هم اختلاف داشتند و بیشترین اختلاف روی کروموزوم­های 13 و 7 بوده که به‌ ترتیب با ژن­های 14 و 9 مرتبط می­باشند. بررسی مناطق ژنومی دارای اختلاف در دو نژاد نشان دادند که این مناطق با صفات کیفیت و کمیت گوشت، شیر، چربی، استخوان (با فراوانی بالاتر در گونه اهلی) و سیستم ایمنی و مقاومت به انگل (با فراوانی بالاتر در گونه اهلی) مرتبط می­باشند. برخی از ژن­های مهم شناسایی شده، شامل GABRB1، GRM3، HERC1، HERC3 و KCND2 بودند. در بررسی هستی‌شناسی ژن­ها، مسیرهای زیستی شناسایی شده مربوط به کانال­های عبور یون‌ها از غشای سلولی، فرآیندهای تحریک عصبی عضلات، رشد مغز و مخچه، انتقال غشایی یون‌های غیر آلی بود. مشخص نمودن صفات مهم اقتصادی و مکان­یابی بخش‌هایی از ژنوم که در اثر انتخاب تغییر پیدا کرده­اند، می­تواند در برنامه­های اصلاح نژادی گوسفند در کشور مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Study of Signatures of Positive Selection and Gene Ontology in some Domestic and Wild Sheep Breeds in Iran

نویسندگان [English]

  • Majid Bigham 1
  • Mohammadreza Nassiry 1
  • Mahyar Heydarpour 2
  • Ali Javadmanesh 1
1 Ferdowsi University of Mashhad
2 Department of Medicine Massachusetts General Brigham Harvard Medical School Boston, MA, 02115, USA
چکیده [English]

Introduction[1]: Over the years, animals have been exposed to various factors such as natural selection, genetic drift, and multiple mutations, so such factors have caused changes between and within species. The genetic mutations that occur in the populations of domestic animals, will be added to the merits of animals who contain these genetic mutations and they will have more breeds. These mutations are also repeated in their breeds. If a new SNP in a population increases the competence of its carriers compared to other members of society, this choice will make the more deserving individual more involved in shaping the next generation. The most important statistical tests based on demographic differentiation are the FST statistics, which identify distinct positions under positive selection, which are of particular importance for economic characteristics. One of the best ways to understand physiological processes is to analyze gene regulation networks. Identification of genes involved in economic traits as molecular markers in breeding is of special importance. Gene regulation networks enable the researcher to study all of the genes together. The aim of this study was to identify selection signature regions and candidate genes related to economic traits.
Materials and Methods: The necessary data for this research were acquired from two sources, namely NEXTGEN and HAPMAP. The dataset encompassed breeds such as Afshari (41 individuals), Ghezel (35 individuals), Moghani (35 individuals), and eight wild sheep. The initial objective was to assess data quality and perform filtration on raw data. For the remaining single nucleotide polymorphisms (SNPs), those not conforming to Hardy-Weinberg equilibrium were considered indicative of genotyping errors. A stringent probability level of 10⁻⁶, determined through Bonferroni correction, was applied. Various stages of quality control were meticulously executed using PLINK v1.9. Additionally, the study involved identifying animals positioned outside their respective groups, contributing to a comprehensive understanding of the population structure within the two groups. Principal component analysis (PCA) were done in R software. The FST index was proposed to study the distinction between subpopulations and identification of selection signature. the population structure of wild and domestic sheep breeds was analyzed. PCA analysis was performed using genotype information of the samples to investigate how the animals were grouped Investigation of identified genes using SNPs in the upper 1% range of FST were identified by Plink v1.9 software. In addition, the DAVID database (http://david.abcc.ncifcrf.gov) was used to determine biological routes. At this stage, it is assumed that genes that belong to a functional class can be considered as a group of genes that have some specific and common characteristics. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes.
Results and Discussion: The results showed that adjacent SNPs are highly dispersed in several genomic regions. From 34556 SNPs after filtration above 1%, SNPs with higher FST stabilization index (340 SNP) with FST range from 0.304 to 0.472 were selected. Selected SNPs consisted of 95 genomic regions on 23 chromosomes between domestic and wild sheep. Most regions were located on chromosomes 13 and 7 had 14 and 9 gene regions, respectively. Examination of the relationship between QTLs and important genes in selected areas showed that 95 genes related to economic traits were identified. QTLs with important economic characteristics including quality and quantity of meat, milk, fat, bone, immune system and parasite resistance were reported. Most QTLs were located on chromosomes 2, 3, 5, 6, and 7, indicating that the most positive mutations occurred on these chromosomes. Most of the identified biological pathways related to ion channels through cell membranes are neuromuscular processes, Brain and cerebellum growth, metencephalon growth, membrane ion membrane transport, and pathways involved in regulating ion transport in cell membranes. Genes identified in different genomic regions can be considered as selective candidates. A number of genes studied as selection signatures reported were consistent with previous studies. Important genes were included: GABRB1, GRM3, HERC1, HERC3 and KCND2.
Conclusion: The study of genomic regions showed that these regions are directly and indirectly related to the quality and quantity of meat, milk, fat, bone, immune system and parasite resistance. Identifying important economic traits and locating parts of the genome that have changed as a result of selection could be used in sheep breeding programs. However, in this research we had limitations such as the incompleteness of information related to functional annotation of genes in sheep species and also the small sample size of this study. Therefore, in subsequent studies with more samples and more breeds of domestic and wild sheep in Iran, a better understanding of candidate genes for important economic traits in domestic and wild species would be achieved.
 

کلیدواژه‌ها [English]

  • Domestic sheep
  • Economic traits
  • Ontology
  • Selection signature
  • Wild sheep
  1. Akey, J. M. (2009). Constructing genomic maps of positive selection in humans: Where do we go from here? Genome Research, 19(5),711-722. http://dx.doi.org/1101/gr.086652.108.
  2. Al Kalaldeh, M., Gibson, J., Lee, S. H., Gondro, C., & Van Der Werf, J. H. (2019). Detection of genomic regions underlying resistance to gastrointestinal parasites in Australian sheep. Genetics Selection Evolution, 51(1),1-18. http://dx.doi.org/1186/s12711-019-0479-1.
  3. Álvarez, I., Fernández, I., Traoré, A., Pérez-Pardal, L., Menéndez-Arias, N. A., & Goyache, F. (2020). Ancient homozygosity segments in West African Djallonké sheep inform on the genomic impact of livestock adaptation to the environment. Animals, 10(7),1178. http://dx.doi.org/3390/ani10071178.
  4. Bakhshalizadeh, S., Zerehdaran, S., & Javadmanesh, A. (2021). Meta-analysis of genome-wide association studies for somatic cells score trait in dairy cows. Journal of Ruminant Research, 9(3),39-58. http://dx.doi.org/22069/ejrr.2021.19036.1787 (In Persian).
  5. Chen, Z.H., Xu, Y.X., Xie, X.L., Wang, D.F., Aguilar-Gómez, D., Liu, G.J., Li, X., Esmailizadeh, A., Rezaei, V., Kantanen, J., Ammosov, I. Nosrati, M., Periasamy, K., Coltman, D.W., Lenstra, L.A., Nielsen, R., & Li M.H. (2021). Whole-genome sequence analysis unveils different origins of European and Asiatic mouflon and domestication-related genes in sheep. Communications Biology, 4(1),1-15. http://dx.doi.org/s42003-021-02817-4.
  6. Deng, X., Wang, D., Wang, S., Wang, H., & Zhou, H. (2018). Identification of key genes and pathways involved in response to pain in goat and sheep by transcriptome sequencing. Biological Research, 51. http://dx.doi.org/1186/s40659-018-0174-7.
  7. Dong, K., Yao, N., Pu, Y., He, X., Zhao, Q., Luan, Y., Guan, W., Rao, S., & Ma, Y. (2014). Genomic scan reveals loci under altitude adaptation in Tibetan and Dahe pigs. PLoS One, 9(10),e110520. http://dx.doi.org/1371/journal.pone.0110520.
  8. Dou, D., Shen, L., Zhou, J., Cao, Z., Luan, P., Li, Y., Xiao, F., Guo, H., Li, H., & Zhang, H. (2022). Genome-wide association studies for growth traits in broilers. BMC Genomic Data, 23(1),1-9. http://dx.doi.org/1186/s12863-021-01017-7.
  9. Duarte, D.A.S., Fortes, M.R.S., de Souza Duarte, M., Guimarães, S.E., Verardo, L.L., Veroneze, R., Ribeiro, A.M.F., Lopes, P.S., de Resende, M.D.V., & e Silva, F.F. (2017). Genome-wide association studies, meta-analyses and derived gene network for meat quality and carcass traits in pigs. Animal Production Science, 58(6),1100-1108. http://dx.doi.org/1071/AN16018.
  10. Durinck, S., Spellman, P. T., Birney, E., & Huber, W. (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature protocols, 4(8),1184-1191. http://dx.doi.org/1038/nprot.2009.97.
  11. Eydivandi, S., Sahana, G., Momen, M., Moradi, M. H., & Schönherz, A. A. (2020). Genetic diversity in Iranian indigenous sheep vis‐à‐vis selected exogenous sheep breeds and wild mouflon. Animal Genetics, 51(5),772-787. http://dx.doi.org/1111/age.12985.
  12. Forough Ameri, N., Asadi Fouzi, M., & Vasmeilizade Keshkoi, A., (2015). Whole genome scanning of eight indigenous breeds of Iranian cattle to identify selection markers. Livestock Production Magazine, 18(2),201-213. (In Persian).
  13. Guan, D., Luo, N., Tan, X., Zhao, Z., Huang, Y., Na, R., Zhang, J., & Zhao, Y. (2016). Scanning of selection signature provides a glimpse into important economic traits in goats (Capra hircus). Scientific Reports, 6(1),1-7. http://dx.doi.org/1038/srep36372.
  14. Gunawan, A., Listyarini, K., Harahap, R.S., Jakaria, Roosita, K., Sumantri, C., Inounu, I., Akter, S.H., Islam, M.A., & Uddin, M.J. (2021). Hepatic transcriptome analysis identifies genes, polymorphisms and pathways involved in the fatty acids metabolism in sheep. PloS One, 16(12),e0260514. http://dx.doi.org/1371/journal.pone.0260514.
  15. Guomundsdottir, O. O. (2015). Genome-wide association study of muscle traits in Icelandic sheep (Doctoral dissertation). (Doctoral dissertation).
  16. Hazard, D., Mace, T., Foulquie, D., Delval, E., Douls, S., Carriere, F., Pradel, J., Moreno, C., & Boissy, A. (2018). Genome wide association studies of maternal behaviours in sheep. In: 11. World Congress on Genetics Applied to Livestock Production (WCGALP), pp. 1130-p. Massey Universtiy.
  17. Jiao, D., Ji, K., Liu, H., Wang, W., Wu, X., Zhou, J., Zhang, Y., Zhou, H., Hickford, J.G., Degen, A.A., & Yang, G. (2021). Transcriptome analysis reveals genes involved in thermogenesis in two cold-exposed sheep breeds. Genes, 12(3),375. http://dx.doi.org/3390/genes12030375.
  18. Krivoruchko, A. Y., Yatsyk, O. A., & Safaryan, E. Y. (2020). Candidate genes for productivity identified by genome-wide association study with indicators of class in the Russian meat merino sheep breed. Vavilov Journal of Genetics and Breeding, 24(8),836. http://dx.doi.org/18699/VJ20.681.
  19. Li, Z., He, X., Zhang, X., Zhang, J., Guo, X., Sun, W., & Chu, M. (2020). Transcriptome profile of key CircRNAs and MiRNAs in oviduct that affect sheep reproduction. http://dx.doi.org/21203/rs.3.rs-67727/v1.
  20. Liu, G., Liu, R., Tang, X., Cao, J., Zhao, S., & Yu, M. (2015). Expression profiling reveals genes involved in the regulation of wool follicle bulb regression and regeneration in sheep. International Journal of Molecular Sciences, 16(5),9152-9166. http://dx.doi.org/ 3390 / ijms16059152.
  21. Mohammadi, F., Tahmoorespur, M., & Javadmanesh, A. (2019). Study of differentially expressed genes, related pathways and gene networks in sheep fetal muscle tissue in thin-and fat-tailed breeds. Animal Sciences Journal, 32(123),301-312. http://dx.doi.org/22092 /asj .2018 .122913.1749.
  22. Mohammadi, H., Rafat, S. A., Moradi Shahrbabak, H., Shodja, J., & Moradi, M. H. (2020). Genome-wide association study and gene ontology for growth and wool characteristics in Zandi sheep. Journal of Livestock Science and Technologies, 8(2),45-55. http://dx.doi.org/22103/jlst.2020.15795.1317. (In Persian).
  23. Moradi, M. H., Nejati-Javaremi, A., Moradi-Shahrbabak, M., Dodds, K. G., & McEwan, J. C. (2012). Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genetics, 13(1),1-15. http://dx.doi.org/1186/1471-2156-13-10.
  24. Mwacharo, J. M., Kim, E. S., Elbeltagy, A. R., Aboul-Naga, A. M., Rischkowsky, B. A., & Rothschild, M. F. (2017). Genomic footprints of dryland stress adaptation in Egyptian fat-tail sheep and their divergence from East African and western Asia cohorts. Scientific reports, 7(1),1-10. http://dx.doi.org/1038/s41598-017-17775-3.
  25. Oldenbroek, K. (Ed.). (2007). Utilisation and Conservation of Farm Animal Genetic Resources. Wageningen Academic Publishers.
  26. Pickering, N. K. (2013). Genetics of flystrike, dagginess and associated traits in New Zealand dual-purpose sheep. A thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, Palmerston North, New Zealand (Doctoral dissertation, Massey University).
  27. Kaveh Pishghadam, N., Malekian, M., & Adavodi, R. (2017). Genetic assessment of funding population of wild sheep (Ovis orientalis) in Chadegan captive breeding site. Journal of Animal Environment, 9(3),41-48. http://dx.doi.org/1001.1.27171388.1396.9.3.6.6.(In Persian).
  28. Qanbari, S., Strom, T.M., Haberer, G., Weigend, S., Gheyas, A.A., Turner, F., Burt, D.W., Preisinger, R., Gianola, D., & Simianer, H. (2012). A high resolution genome-wide scan for significant selective sweeps: An application to pooled sequence data in laying chickens. PloS one, 7(11),e49525. http://dx.doi.org/1371/journal.pone.0049525.
  29. Qanbari, S., Pausch, H., Jansen, S., Somel, M., Strom, T.M., Fries, R., Nielsen, R., & Simianer, H. (2014). Classic selective sweeps revealed by massive sequencing in cattle. PLoS Genetics, 10(2),e1004148. http://dx.doi.org/1371/journal.pgen.1004148.
  30. Sabeti, P.C., Reich, D.E., Higgins, J.M., Levine, H.Z., Richter, D.J., Schaffner, S.F., Gabriel, S.B., Platko, J.V., Patterson, N.J., McDonald, G.J., & Ackerman, H.C., Campbell, S. J., Altsshuler, D., Cooper, R., Kwiatkowski, D., Ward, R., & Lander, E. S. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419(6909),832-837. http://dx.doi.org/1038/nature01140.
  31. Sabeti, P.C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E.H., McCarroll, S.A., Gaudet, R., & Schaffner, S.F. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449(7164),913-918. http://dx.doi.org/1038/nature06250.
  32. Serranito, B., Cavalazzi, M., Vidal, P., Taurisson-Mouret, D., Ciani, E., Bal, M., Rouvellac, E., Servin, B., Moreno-Romieux, C., Tosser-Klopp, G., Hall, S.J. & Da Silva, A. (2021). Local adaptations of Mediterranean sheep and goats through an integrative approach. Scientific Reports, 11(1),1-17. http://dx.doi.org/s41598-021-00682-z.
  33. Suárez-Vega, A., Gutiérrez-Gil, B., & Arranz, J. J. (2016). Transcriptome expression analysis of candidate milk genes affecting cheese-related traits in 2 sheep breeds. Journal of Dairy Science, 99(8),6381-6390. http://dx.doi.org/3168/jds.2016-11048.
  34. Sweet‐Jones, J., Yurchenko, A. A., Igoshin, A. V., Yudin, N. S., Swain, M. T., & Larkin, D. M. (2021). Resequencing and signatures of selection scan in two Siberian native sheep breeds point to candidate genetic variants for adaptation and economically important traits. Animal Genetics, 52(1),126-131. http://dx.doi.org/1111/age.13015.
  35. Tao, L., He, X.Y., Wang, F.Y., Pan, L.X., Wang, X.Y., Gan, S.Q., Di, R., & Chu, M.X. (2021). Identification of genes associated with litter size combining genomic approaches in Luzhong mutton sheep. Animal Genetics, 52(4),545-549. http://dx.doi.org/1111/age.13078.
  36. Taheri,, Zerehdaran, S., & Javadmanesh, A. (2020). Investigating genetic diversity and traces of selection in Iranian domestic and wild sheep and goats. M.Sc.Thesis of Ferdowsi University of Mashhad, Faculty of Agriculture, Mashhhad, Iran. (In Persian).
  37. Tsartsianidou, V., Sánchez-Molano, E., Kapsona, V.V., Basdagianni, Z., Chatziplis, D., Arsenos, G., Triantafyllidis, A., & Banos, G. (2021). A comprehensive genome-wide scan detects genomic regions related to local adaptation and climate resilience in Mediterranean domestic sheep. Genetics Selection Evolution, 53(1),1-17. http://dx.doi.org/1186/s12711-021-00682-7.
  38. Upadhyay, M., Kunz, E., Sandoval‐Castellanos, E., Hauser, A., Krebs, S., Graf, A., Blum, H., Dotsev, A., Okhlopkov, I., Shakhin, A., Bagirov, V., & Medugorac, I. (2021). Whole genome sequencing reveals a complex introgression history and the basis of adaptation to subarctic climate in wild sheep. Molecular Ecology, 30(24),6701-6717. http://dx.doi.org/1111/mec.16184.
  39. Wang, H., Zhang, L., Cao, J., Wu, M., Ma, X., Liu, Z., Liu, R., Zhao, F., Wei, C., & Du, L. (2015). Genome-wide specific selection in three domestic sheep breeds. PloS One, 10(6),e0128688. http://dx.doi.org/1371/journal.pone.0128688.
  40. Weir, B. S., & Cockerham, C. C. (1984). Estimating F-statistics for the analysis of population structure. Evolution, 1358-1370.
  41. Wiener, P., Robert, C., Ahbara, A., Salavati, M., Abebe, A., Kebede, A., Wragg, D., Friedrich, J., Vasoya, D., Hume, D.A., Djikeng, A., & Clark, E. L. (2021). Whole-genome sequence data suggest environmental adaptation of Ethiopian sheep populations. Genome Biology and Evolution, 13(3), evab014. http://dx.doi.org/1093/gbe/evab014.
  42. Yang, J.I., Li, W.R., Lv, F.H., He, S.G., Tian, S.L., Peng, W.F., Sun, Y.W., Zhao, Y.X., Tu, X.L., Zhang, M., Xie, X.L., & Liu, M. J. (2016). Whole-genome sequencing of native sheep provides insights into rapid adaptations to extreme environments. Molecular Biology and Evolution, 33(10),2576-2592. http://dx.doi.org/10.1093/molbev/msw129.
  43. Zeraatpisheh, Y., Zerehdaran, S., & Javadmanesh, A. (2022). Investigation of metabolic pathways of genes related to the QTL of parasite resistance trait in sheep genome using gene network and gene ontology. Veterinary Researches & Biological Products. http://dx.doi.org/22092/vj.2022.357660.1941. (In Persian).
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