Journal of Plant Biotechnology

The Korean Society of Plant Biotechnology (한국식물생명공학회)
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Gene expression and SNP identification related to leaf angle traits using a genome-wide association study in rice (Oryza sativa L.)

GWAS 분석을 이용한 벼 지엽각 관련 SNP 동정 및 발현 분석

  • Kim, Me-Sun (Department of Crop Science, Chungbuk National University) ;
  • Yu, Yeisoo (DNACARE) ;
  • Kang, Kwon-Kyoo (Department of Horticultural Science, Hankyong National University) ;
  • Cho, Yong-Gu (Department of Crop Science, Chungbuk National University)
  • Received : 2018.03.07
  • Accepted : 2018.03.15
  • Published : 2018.03.31
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Abstract

This study was conducted to investigate a morphological trait in 294 rice accessions including Korean breeding lines. We also carried out a genome-wide association study (GWAS) to detect significant single nucleotide polymorphism markers and candidate genes affecting major agronomic traits. A Manhattan plot analysis of GWAS using morphological traits showed that phenotypic and statistical significance was associated with a chromosome in each group. The significance of SNPs that were detected in this study was investigated by comparing them with those found previously studied QTL regions related to agronomic traits. As a result, SNP (S8-19815442), which is significant with regard to leaf angle, was located in the known QTL regions. To observe gene mutations related to leaf angle in a candidate gene, Os08g31950, its sequences were compared with sequences in previously selected rice varieties. In Os08g31950, a single nucleotide mutation occurred in one region. To compare relative RNA expression levels of candidate gene Os08g31950, obtained from GWAS analysis of 294 rice accessions and related to lateral leaf angle, we investigated relative levels by selecting 10 erect leaf angle varieties and 10 horizontal leaf angle varieties and examining real-time PCR. In Os08g31950, a high level of expression and various expression patterns were observed in all tissues. Also, Os08g31950 showed higher expression levels in the erect leaf angle variety group and higher expression rates in the leaf than in the root. The candidate gene detected through GWAS would be useful in developing new rice varieties with improved yield potential through future molecular breeding.

본 연구에서는 국내외에서 수집한 벼 294개 유전자원 핵심집단을 대상으로 벼의 지엽각 특성에 대한 조사를 수행하였고, GWAS를 이용하여 지엽각 연관 유전자를 추출 및 분석하였다. 표현형 데이터를 이용한 GWAS의 Manhattan plot 결과 분석을 통해, 각 집단에서 염색체를 대상으로 표현형과 통계적 유의성을 나타내 연관성을 보이는 SNP를 발굴하였다. 지엽각 관련 특성에 대하여 선행 연구된 QTL region과의 비교를 통하여 본 연구에서 발굴된 SNP간의 유의성을 조사한 결과, 지엽각과 유의성이 있는 SNP (S8-19815442)가 이미 확인된 QTL region에 위치하는 것으로 나타났으며, 후보유전자 Os08g31950 대해 연관 유전자 변이를 관찰하기 위해서 형질 특이적 품종군 간의 염기서열을 비교한 결과 1개의 지역에서 단일염기변이가 검출되었다. Os08g31950의 조직별 RNA의 상대적 발현량 수준을 비교한 결과, Os08g31950 유전자는 모든 조직에서 높은 발현량을 확인할 수 있었으며 조직별로 다양한 발현 양상을 관찰할 수 있었다. 또한, 모두 직립형 품종군에서 상대적으로 발현량이 높게 나타났으며 뿌리보다 잎에서의 발현율이 높게 나타났다. 본 연구를 통해 동정된 지엽각 연관 후보유전자 Os08g31950는 벼 생육 및 수량 증대에 이용할 수 있는 마커제작 및 육종의 기초자료가 될 것으로 기대된다.

Keywords

References

  1. Abdula SE et al (2013) Development and identification of transgenic rice lines with abiotic stress tolerance by using a full-length overexpressor gene hunting system. Plant Breed Biotechnol 1:33-48 https://doi.org/10.9787/PBB.2013.1.1.033
  2. Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nature Biotechnology 30:174-178 https://doi.org/10.1038/nbt.2095
  3. Alexandrov N, Tai S, Wang W, Mansueto L, Palis K, Fuentes RR, Ulat VJ, Chebotarov D, Zhang G, Li Z, Mauleon R, Sackville Hamilton R, McNally KL (2015) SNP-seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Research 43(D1):1023-1027 https://doi.org/10.1093/nar/gku1039
  4. Altshule D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322:881-888.
  5. Asano K, Takashi T, Miura K, Qian Q, Kitano H, Matsuoka M, Ashikari M (2007) Genetic and molecular analysis of utility of sd1 alleles in rice breeding. Korean Journal of Breeding Science 57:53-58 https://doi.org/10.1270/jsbbs.57.53
  6. Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M (2005) Cytokinin oxidase regulates rice grain production. Science 309:741-745 https://doi.org/10.1126/science.1113373
  7. Blawid R, Silva JMF, Nagata T (2017) Discovering and sequencing new plant viral genomes by next-generation sequencing: description of a practical pipeline. Annals of Applied Biology 170:301-314 https://doi.org/10.1111/aab.12345
  8. Cai J, Zhang M, Guo LB, Li XM, Bao JS, Ma LY (2015) QTLs for rice flag leaf traits in doubled haploid populations in different environments. Genetics and Molecular Research 14(2): 6786-6795 https://doi.org/10.4238/2015.June.18.21
  9. Castillo-Davis CI, Mekhedov SL, Hartl DL, Koonin EV, Kondrashov FA (2002) Selection for short introns in highly expressed genes. Nature Genetics 31(4):415-418 https://doi.org/10.1038/ng940
  10. Cheong HS, Yoon DH, Kim LH, Park BL, Lee HW, Park BL, Choi YH, Chung ER, Cho YM, Park EW, Cheong IC, Oh SJ, Yi SG, Park TS, Shin HD (2006) Growth hormone-releasing hormone (GHRH) polymorphisms associated with carcass traits of meat in Korean cattle. BMC Genetics 7:35-40
  11. Cho DS (1975) Studies on the productivity of individual leaf blade of paddy rice. Korean Journal of Breeding Science 18:1-27
  12. Cho YG, McCouch SR, Kuiper M, Kang MR, Pot J, Groenen JTM, Eun MY (1998) Integrated map of AFLP, SSLP, and RFLP markers using a recombinant inbred population of rice (Oryza sativa L.). Theoretical and Applied Genetics 97:370-380
  13. Dong Y, Kamiunten H, Ogawa T, Tsuzuki E, Terao H, Lin D, Matsuo M (2004) Mapping of QTLs for leaf developmental behavior in rice (Oryza sativa L.). Euphytica 138:169-175 https://doi.org/10.1023/B:EUPH.0000046799.21410.13
  14. Feng Z, Wu C, Wang C, Roh J, Zhang L, Chen J, Zhang S, Zhang H, Yang C, Hu J, You X, Liu X, Yang X, Guo X, Zhang X, Wu F, Terzaghi W, Kim SK, Jiang L, Wan J (2016) SLG controls grain size and leaf angle by modulating brassinosteroid homeostasis in rice. Journal of Experimental Botany 16(14):4341-4253
  15. Fujino K, Obara M, Sato K (2014) Diversification of the plantspecific hybrid glycine-rich protein (HyGRP) genes in cereals. Plant Science 24:489
  16. Garg AK, Kim JK, Owens TG, Ranwala AP, Choi YD, Kochian LV, Wu RJ (2002) Trehalose accumulation in rice plants confers high tolerance levels to different abiotic stresses. Proceedings of the National Academy of Sciences of the United States of America 99(25):15898-15903 https://doi.org/10.1073/pnas.252637799
  17. Grandori G, Cowley SM, James LP, Eisenman RM (2000) The Myc/Max/Mad network and the transcriptional control of cell behavior. Annual Review of Cell and Developmental Biology 16:653-699 https://doi.org/10.1146/annurev.cellbio.16.1.653
  18. Heang D, Sassa H (2012) Antagonistic actions of HLH/bHLH proteins are involved in grain length and weight in rice. PLoS One 7:e31325 https://doi.org/10.1371/journal.pone.0031325
  19. Hu WD, Zhang H, Jiang JH, Wang YY, Sun DY, Wang XS, Liang K, Hong DL (2012) Genetic analysis and QTL mapping of large flag leaf angle trait in Japonica rice. Rice Scienc 19(4):277-285 https://doi.org/10.1016/S1672-6308(12)60052-3
  20. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, Guan J, Fan D, Weng Q, Huang T, Dong G, Sang T, Han B (2009) High-throughput genotyping by whole-genome resequencing. Genome Research 19:1068-1076
  21. Huang X, Qian Q, Liu Z, Sun H, He S, Luo D, Xia G, Chu C, Li J, Fu X (2009) Natural variation at the DEP1 locus enhances grain yield in rice. Nature Genetics 41:494-497 https://doi.org/10.1038/ng.352
  22. Hunter KW, Crawford NPS (2008) The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics 42:131-141 https://doi.org/10.1146/annurev.genet.42.110807.091659
  23. Jang S, An G, Li HY (2017) Rice leaf angle and grain size are affected by the OsBUL1 transcriptional activator complex. Plant Physiology 173(1):688-702 https://doi.org/10.1104/pp.16.01653
  24. Jung KH, Dardick C, Bartley LE, Cao P, Phetsom J, Canlas P, Seo YS, Shultz M, Ouyang S, Yuan Q, Frank BC, Ly E, Zheng L, Jia Y, Hsia AP, An K, Chou HH, Rocke D, Lee GC, Schnable PS, An G, Buell CR, Ronald PC (2008) Refinement of light-responsive transcript lists using rice oligonucleotide arrays: evaluation of gene-redundancy. PLoS ONE 3:e3337 https://doi.org/10.1371/journal.pone.0003337
  25. Kim TS, He Q, Kim KW, Yoon MY, Ra WH, Li FP, Tong W, et al. (2016) Genome-wide resequencing of KRICE_CORE reveals their potential for future breeding, as well as functional and evolutionary studies in the post-genomic era. BMC Genomics 17:408 https://doi.org/10.1186/s12864-016-2734-y
  26. Kobayashi S, Fukuta Y, Morita S, Sato T (2003) Quantitative trait loci affecting flag leaf development in rice (Oryza sativa L.). Breeding Science 53:255-262 https://doi.org/10.1270/jsbbs.53.255
  27. Kruglyak L (2008) The road to genome-wide association studies. Nature Reviews Genetics 9(4):314-318 https://doi.org/10.1038/nrg2316
  28. Kump B, Javornik B (1996) Evaluation of genetic variability among common buckwheat (Fagopyrum esculentum Moench) population by RAPD markers. Plant Science 114:149-158 https://doi.org/10.1016/0168-9452(95)04321-7
  29. Li J (2008) A novel strategy for detecting multiple loci in Genome-Wide Association Studies of complex diseases. International Journal of Bioinformatics Research and Applications 4(2):150-163 https://doi.org/10.1504/IJBRA.2008.018342
  30. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397-2399 https://doi.org/10.1093/bioinformatics/bts444
  31. Ma X, Fu Y, Zhao X, Jiang L, Zhu Z, Gu P, Xu W, Su Z, Sun C, Tan L (2016) Genomic structure analysis of a set of Oryza nivara introgression lines and identification of yield-associated QTLs using whole-genome resequencing. Scientific Reports 6:27425 https://doi.org/10.1038/srep27425
  32. Massari ME, Murre C (2000) Helix-loop-helix proteins: regulators of transcription in eukaryotic organisms. Molecular and Cellular Biology 20:429-440 https://doi.org/10.1128/MCB.20.2.429-440.2000
  33. Nicolae DL, Gamazon E, Zhang W, Duan S, Eileen Dolan M, Cox NJ (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genetics 6(4):e1000888 https://doi.org/10.1371/journal.pgen.1000888
  34. Park HS, Ha KY, Kim KY, Kim WJ, Nam JK, Baek MK, Kim JJ, Jeong JM, Cho YC, Lee JH, Kim BK, Ahn SN (2015) Development of high-yielding rice lines and analysis of panicle and yield-related traits using doubled haploid lines derived from the cross between Deuraechan and Boramchan, high-yielding japonica rice cultivars in Korea. Korean Journal of Breeding Science 47:384-402 https://doi.org/10.9787/KJBS.2015.47.4.384
  35. Peng B, Kong HL, Li YB, Wang LQ, Zhong M, Sun L, Gao GJ, Zhang QL, Luo LJ, Wang GW, Xie WB, Chen JX, Yao W, Peng Y, Lei L, Lian XM, Xiao JH, Xu CG, Li XH, He YQ (2014) OsAAP6 functions as an important regulator of grain protein content and nutritional quality in rice. Nature Communications 5:4847 https://doi.org/10.1038/ncomms5847
  36. Perez-de-Castro AM, Vilanova S, Canizares J, Pascual L, Blanca JM, Diez MJ, Prohens J, Pico B (2012) Application of genomic tools in plant breeding. Current Genomics 13:179-195
  37. Sakai H, Kanamori H, Arai-Kichise Y, Shibata-Hatta M, Ebana K, Oono Y, Kurita K, et al. (2014) Construction of pseudomolecule sequences of the Aus rice cultivar kasalath for comparative genomics of asian cultivated rice. DNA research 21(4):397-405 https://doi.org/10.1093/dnares/dsu006
  38. Schatz MC, Maron LG, Stein JC, Wences AH, Gurtowski J, Biggers E, Lee H, et al. (2014) New whole genome de nomo assemblies of three divergent strains of rice (O. sativa) documents novel gene space of aus and indica. bioRxiv doi:http://dx.doi.org/10.1101/003764
  39. Shabalina SA, Spiridonov NA (2004) The mammalian transcriptome and the function of non-coding DNA sequences. Genome Biology 5(4):105 https://doi.org/10.1186/gb-2004-5-4-105
  40. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445(7130):881-885 https://doi.org/10.1038/nature05616
  41. Su JJ, Pang CY, Wei HL, Li LB, Liang B, Wang CX, Song MZ, Wang HT, Zhao SQ, Jia XY, Mao GZ, Huang L, Geng DD, Wang CS, Fan SL, Yu SX (2016) Identification of favorable SNP alleles and candidate genes fortraits related to early maturity via GWAS in upland cotton. BMC Genomics 17:687 https://doi.org/10.1186/s12864-016-2875-z
  42. Sugimoto K, Takeuchi Y, Ebana K, Miyao A, Hirochika H, Hara N, Ishiyama K, Kobayashi M, Ban Y, Hattori T, Yano M (2010) Molecular coloning of Sdr4, a regulator incolced in seed dormancy and domestication of rice. Preceedings of the National Academy of Sciences of the United States of America 107:5792-5797
  43. Takagi H, Uemura A, Yaegashi H, Tamiru M, Abe A, Mitsuoka C, Utsushi H, et al. (2013) MutMap-Gap: whole-genome resequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytoloist 200:276-283
  44. Tanaka A, Nakagawa H, Tomita C, Shimatani Z, Ohtake M, Nomura T, Jiang CJ, Dubouzet JG, Kikuchi S, Sekimoto H (2009) BRASSINOSTEROID UPREGULATED1, encoding a helix-loop-helix protein, is a novel gene involved in brassinosteroid signaling and controls bending of the lamina joint in rice. Plant Physiology 151:669-680 https://doi.org/10.1104/pp.109.140806
  45. Toledo-Ortiz G, Huq E, Quail PH (2003) The Arabidopsis basic/helix-loop-helix transcription factor family. Plant Cell 15:1749-1770 https://doi.org/10.1105/tpc.013839
  46. Wallace C, Newhouse SJ, Braund P, Zhang F, Tobin M, Falchi M, Ahmadi K, et al. (2008) Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. American Journal of Human Genetics 82(1):139-149 https://doi.org/10.1016/j.ajhg.2007.11.001
  47. Weedon MN, Lettre G, Freathy RM, Lindgren GM, Voight BF, Perry JR, Elliott KS, et al. (2007) A common variant of HMGA2 is associated with adult and childhood height in the general population. Nature Genetics 39(10):1245-1250 https://doi.org/10.1038/ng2121
  48. Y H, Feng J, Zhang L, Zhang J, Mispan MS, Cao Z, Yang J, Beighley DH, Gu X (2015) Map-based cloning of qSD1-2 identified a gibberellin synthesis gene regulating the development of endosperm-imposed dormancy in rice. Plant Physiology 169:152-165
  49. Zhang LY, Bai MY, Wu J, Zhu JY, Wang H, Zhang Z, Wang W, Sun Y, Zhao J, Sun X (2009) Antagonistic HLH/bHLH transcription factors mediate brassinosteroid regulation of cell elongation and plant development in rice and Arabidopsis. Plant Cell 21:3767-3780 https://doi.org/10.1105/tpc.109.070441
  50. Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nature Genetics 42:355-360 https://doi.org/10.1038/ng.546