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http://dx.doi.org/10.5010/JPB.2018.45.1.017

Gene expression and SNP identification related to leaf angle traits using a genome-wide association study in rice (Oryza sativa L.)  

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)
Publication Information
Journal of Plant Biotechnology / v.45, no.1, 2018 , pp. 17-29 More about this Journal
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.
Keywords
GWAS; core-collection; rice; leaf angle; SNP;
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1 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   DOI
2 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   DOI
3 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
4 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
5 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
6 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   DOI
7 Toledo-Ortiz G, Huq E, Quail PH (2003) The Arabidopsis basic/helix-loop-helix transcription factor family. Plant Cell 15:1749-1770   DOI
8 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   DOI
9 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   DOI
10 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   DOI
11 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   DOI
12 Heang D, Sassa H (2012) Antagonistic actions of HLH/bHLH proteins are involved in grain length and weight in rice. PLoS One 7:e31325   DOI
13 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   DOI
14 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
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 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   DOI
17 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   DOI
18 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   DOI
19 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   DOI
20 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
21 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   DOI
22 Kruglyak L (2008) The road to genome-wide association studies. Nature Reviews Genetics 9(4):314-318   DOI
23 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   DOI
24 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   DOI
25 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   DOI
26 Kump B, Javornik B (1996) Evaluation of genetic variability among common buckwheat (Fagopyrum esculentum Moench) population by RAPD markers. Plant Science 114:149-158   DOI
27 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   DOI
28 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   DOI
29 Massari ME, Murre C (2000) Helix-loop-helix proteins: regulators of transcription in eukaryotic organisms. Molecular and Cellular Biology 20:429-440   DOI
30 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   DOI
31 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   DOI
32 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   DOI
33 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   DOI
34 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   DOI
35 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   DOI
36 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   DOI
37 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
38 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   DOI
39 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   DOI
40 Altshule D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322:881-888.
41 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   DOI
42 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   DOI
43 Shabalina SA, Spiridonov NA (2004) The mammalian transcriptome and the function of non-coding DNA sequences. Genome Biology 5(4):105   DOI
44 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   DOI
45 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   DOI
46 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   DOI
47 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   DOI
48 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
49 Cho DS (1975) Studies on the productivity of individual leaf blade of paddy rice. Korean Journal of Breeding Science 18:1-27
50 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