Molecules and Cells

Korean Society for Molecular and Cellular Biology (한국분자세포생물학회)
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Detecting Positive Selection of Korean Native Goat Populations Using Next-Generation Sequencing

  • Lee, Wonseok (Department of Agricultural Biotechnology, Animal Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Ahn, Sojin (Department of Natural Science, Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Taye, Mengistie (Department of Agricultural Biotechnology, Animal Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Sung, Samsun (C&K genomics, Seoul National University Research Park) ;
  • Lee, Hyun-Jeong (Division of Animal Genomics and Bioinformatics, National Institute of Animal science, Rural Development Administration) ;
  • Cho, Seoae (C&K genomics, Seoul National University Research Park) ;
  • Kim, Heebal (Department of Agricultural Biotechnology, Animal Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University)
  • Received : 2016.09.08
  • Accepted : 2016.11.14
  • Published : 2016.12.31
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Abstract

Goats (Capra hircus) are one of the oldest species of domesticated animals. Native Korean goats are a particularly interesting group, as they are indigenous to the area and were raised in the Korean peninsula almost 2,000 years ago. Although they have a small body size and produce low volumes of milk and meat, they are quite resistant to lumbar paralysis. Our study aimed to reveal the distinct genetic features and patterns of selection in native Korean goats by comparing the genomes of native Korean goat and crossbred goat populations. We sequenced the whole genome of 15 native Korean goats and 11 crossbred goats using next-generation sequencing (Illumina platform) to compare the genomes of the two populations. We found decreased nucleotide diversity in the native Korean goats compared to the crossbred goats. Genetic structural analysis demonstrated that the native Korean goat and cross-bred goat populations shared a common ancestry, but were clearly distinct. Finally, to reveal the native Korean goat's selective sweep region, selective sweep signals were identified in the native Korean goat genome using cross-population extended haplotype homozygosity (XP-EHH) and a cross-population composite likelihood ratio test (XP-CLR). As a result, we were able to identify candidate genes for recent selection, such as the CCR3 gene, which is related to lumbar paralysis resistance. Combined with future studies and recent goat genome information, this study will contribute to a thorough understanding of the native Korean goat genome.

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References

  1. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Statistical Society. Series B (Methodological), 289-300.
  2. Browning, S.R., and Browning, B.L. (2007). Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084-1097. https://doi.org/10.1086/521987
  3. Chen, H., Patterson, N., and Reich, D. (2010). Population differentiation as a test for selective sweeps. Genome Res. 20, 393-402. https://doi.org/10.1101/gr.100545.109
  4. Choi, S., Choy, Y., Kim, Y., and Hur, S. (2006). Effects of feeding browses on growth and meat quality of Korean Black Goats. Small Ruminant Res. 65, 193-199. https://doi.org/10.1016/j.smallrumres.2005.04.031
  5. Cingolani, P., Platts, A., Coon, M., Nguyen, T., Wang, L., Land, S.J., Lu, X., and Ruden, D.M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80-92. https://doi.org/10.4161/fly.19695
  6. Crigler, L., Robey, R.C., Asawachaicharn, A., Gaupp, D., and Phinney, D.G. (2006). Human mesenchymal stem cell subpopulations express a variety of neuro-regulatory molecules and promote neuronal cell survival and neuritogenesis. Exp. Neurol. 198, 54-64. https://doi.org/10.1016/j.expneurol.2005.10.029
  7. Dalai, S.K., Das, D., and Kar, S.K. (1998). Setaria digitataAdult 14-to 20-kDa antigens induce differential Thl/Th2 cytokine responses in the lymphocytes of endemic normals and asymptomatic microfilariae carriers in Bancroftian Filariasis. J. Clin. Immunol. 18, 114-123. https://doi.org/10.1023/A:1023294716282
  8. Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., and Sherry, S.T. (2011). The variant call format and VCFtools. Bioinformatics 27, 2156-2158. https://doi.org/10.1093/bioinformatics/btr330
  9. Dennis Jr, G., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., and Lempicki, R.A. (2003). DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, P3. https://doi.org/10.1186/gb-2003-4-5-p3
  10. Dong, Y., Xie, M., Jiang, Y., Xiao, N., Du, X., Zhang, W., Tosser-Klopp, G., Wang, J., Yang, S., and Liang, J. (2013). Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus). Nat. Biotechnol. 31, 135-141. https://doi.org/10.1038/nbt.2478
  11. Dybkaer, K., Iqbal, J., Zhou, G., Geng, H., Xiao, L., Schmitz, A., d'Amore, F., and Chan, W.C. (2007). Genome wide transcriptional analysis of resting and IL2 activated human natural killer cells: gene expression signatures indicative of novel molecular signaling pathways. BMC Genomics 8, 1. https://doi.org/10.1186/1471-2164-8-1
  12. Emmons, D., and Lister, E. (1976). Quality of protein in milk replacers for young calves. I. Factors affecting in vitro curd formation by rennet (chymosin, rennin) from reconstituted skim milk powder. Canadian J. Animal Sci. 56, 317-325. https://doi.org/10.4141/cjas76-037
  13. Evanno, G., Regnaut, S., and Goudet, J. (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611-2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x
  14. Hivert, B., Liu, Z., Chuang, C.-Y., Doherty, P., and Sundaresan, V. (2002). Robo1 and Robo2 are homophilic binding molecules that promote axonal growth. Mol. Cell. Neurosci. 21, 534-545. https://doi.org/10.1006/mcne.2002.1193
  15. Huang, D.W., Sherman, B.T., and Lempicki, R.A. (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1-13. https://doi.org/10.1093/nar/gkn923
  16. Hubbard, T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., Cox, T., Cuff, J., Curwen, V., and Down, T. (2002). The Ensembl genome database project. Nucleic Acids Res. 30, 38-41. https://doi.org/10.1093/nar/30.1.38
  17. Jackson, J.E. (2005). A user's guide to principal components (Wiley.com).
  18. Kim, S.W., Park, S.B., Kim, M.J., Kim, D.H. and Yim, D.-G. (2014). Effects of different levels of concentrate in the diet on physicochemical traits of Korean native black goat meats. Korean J. Food Sci. Animal Res. 34, 457. https://doi.org/10.5851/kosfa.2014.34.4.457
  19. Lee, H.-J., Kim, J., Lee, T., Son, J.K., Yoon, H.-B., Baek, K.-S., Jeong, J.Y., Cho, Y.-M., Lee, K.-T., and Yang, B.-C. (2014). Deciphering the genetic blueprint behind Holstein milk proteins and production. Genome Biol. Evol. 6, 1366-1374. https://doi.org/10.1093/gbe/evu102
  20. Leo, A. and Schraven, B. (2001). Adapters in lymphocyte signalling. Curr. Opin. Immunol. 13, 307-316. https://doi.org/10.1016/S0952-7915(00)00220-X
  21. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., and Durbin, R. (2009). The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078-2079. https://doi.org/10.1093/bioinformatics/btp352
  22. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., and Daly, M. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303. https://doi.org/10.1101/gr.107524.110
  23. Mombaerts, P. (2004). Genes and ligands for odorant, vomeronasal and taste receptors. Nat. Rev. Neurosci. 5, 263-278. https://doi.org/10.1038/nrn1365
  24. Nachman, M.W. (2001). Single nucleotide polymorphisms and recombination rate in humans. Trend Genet. 17, 481-485. https://doi.org/10.1016/S0168-9525(01)02409-X
  25. Niimura, Y. (2009). Evolutionary dynamics of olfactory receptor genes in chordates: interaction between environments and genomic contents. Hum. Genomics 4, 107-118. https://doi.org/10.1186/1479-7364-4-2-107
  26. Odahara, S., Chung, H., Choi, S., Yu, S., Sasazaki, S., Mannen, H., Park, C., and Lee, J. (2006). Mitochondrial DNA diversity of Korean native goats. Asian Australasian J. Animal Sci. 19, 482.
  27. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904-909. https://doi.org/10.1038/ng1847
  28. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., De Bakker, P.I., and Daly, M.J. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575. https://doi.org/10.1086/519795
  29. Quinlan, A.R. and Hall, I.M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842. https://doi.org/10.1093/bioinformatics/btq033
  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., and McDonald, G.J. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature 419, 832-837. https://doi.org/10.1038/nature01140
  31. Sabeti, P.C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E.H., McCarroll, S.A., and Gaudet, R. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913-918. https://doi.org/10.1038/nature06250
  32. Sallusto, F., Mackay, C.R., and Lanzavecchia, A. (1997). Selective expression of the eotaxin receptor CCR3 by human T helper 2 cells. Science 277, 2005-2007. https://doi.org/10.1126/science.277.5334.2005
  33. Severino, P., Silva, E., Baggio-Zappia, G.L., Brunialti, M.K.C., Nucci, L.A., Rigato Jr, O., da Silva, I.D.C.G., Machado, F.R., and Salomao, R. (2014). Patterns of gene expression in peripheral blood mononuclear cells and outcomes from patients with sepsis secondary to community acquired pneumonia. PloS One 9, e91886. https://doi.org/10.1371/journal.pone.0091886
  34. Sirko-Osadsa, D.A., Murray, M.A., Scott, J.A., Lavery, M.A., Warman, M.L., and Robin, N.H. (1998). Stickler syndrome without eye involvement is caused by mutations in COL11A2, the gene encoding the ${\alpha}2$(XI) chain of type XI collagen. J. Pediatr. 132, 368-371. https://doi.org/10.1016/S0022-3476(98)70466-4
  35. Son, Y. (1999). Production and uses of Korean native Black goat. Small Ruminant Res. 34, 303-308. https://doi.org/10.1016/S0921-4488(99)00081-4
  36. Uccelli, A., Benvenuto, F., Laroni, A., and Giunti, D. (2011). Neuroprotective features of mesenchymal stem cells. Best practice & research Clin. Haematol. 24, 59-64. https://doi.org/10.1016/j.beha.2011.01.004
  37. Vikkula, M., Madman, E., Lui, V.C., Zhidkova, N.I., Tiller, G.E., Goldring, M.B., van Beersum, S.E., de Waal Malefijt, M.C., van den Hoogen, F.H. and Ropers, H.-H. (1995). Autosomal dominant and recessive osteochondrodysplasias associated with the COL11A2 locus. Cell 80, 431-437. https://doi.org/10.1016/0092-8674(95)90493-X
  38. Wright, K.T., Masri, W.E., Osman, A., Chowdhury, J., and Johnson, W.E. (2011). Concise review: bone marrow for the treatment of spinal cord injury: mechanisms and clinical applications. Stem Cells 29, 169-178. https://doi.org/10.1002/stem.570
  39. Yang, J., Lee, S.H., Goddard, M.E., and Visscher, P.M. (2011). GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76-82. https://doi.org/10.1016/j.ajhg.2010.11.011
  40. Yu, B., Qian, T., Wang, Y., Zhou, S., Ding, G., Ding, F., and Gu, X. (2012). miR-182 inhibits Schwann cell proliferation and migration by targeting FGF9 and NTM, respectively at an early stage following sciatic nerve injury. Nucleic Acids Res. 40, 10356-10365. https://doi.org/10.1093/nar/gks750

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