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Acta Agron Sin ›› 2018, Vol. 44 ›› Issue (01): 32-42.doi: 10.3724/SP.J.1006.2018.00032

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Genome-wide Association Study of Grain Appearance and Milling Quality in a Worldwide Collection of Indica Rice Germplasm

FANG Ya-Jie1,2,ZHU Ya-Jun2,3,WU Zhi-Chao2,CHEN Kai2,3,SHEN Cong-Cong3,SHI Ying-Yao1,*,XU Jian-Long2,3,4,*   

  1. 1 Anhui Agricultural University, Hefei 230036, China; 2 Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 3 Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen 518210, China; 4 Shenzhen Institute of Breeding & Innovation, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
  • Received:2017-04-05 Revised:2017-09-10 Online:2018-01-12 Published:2017-09-28
  • Supported by:

    This study was supported by National High-Tech Research & Development Plan (863) (2014AA10A601), Introduction of International Advanced Agricultural Science and Technology of Ministry of Agriculture (948) (2016-X16), ShenZhen Peacock Plan (20130415095710361), and Scientific and Technological Innovation Project of Chinese Academy of Agricultural Sciences.

Abstract:

Appearance and milling quality are two crucial properties of rice affecting their market acceptability. Understanding the genetic basis of rice grain quality could improve the efficiency of breeding for high quality. Here we carried out genome-wide association analysis using the 404K SNP genotype and data of grain appearance and milling quality for a diverse panel of 400 indica accessions selected from 3K Rice Genome Project collected in two environments. These were closely correlation between grain shape and quality (appearance and milling) of indica rice. The chalk trait reduced milled rice rate and high milled rice rate. In addition to grain shape, the phenotype value of appearance and milling quality traits was obviously affected by environments. Total of 39 QTL were detected which was significantly associated with grain appearance and milling quality in two environments and 17 of them were new QTL. Six new QTL (qMRR9, qGL2, qGL10, qGW1, qGLWR1, and qGLWR2.1) with minor or small effect were considered as stably expressed QTL because they were simultaneously identified in two environments. In addition, we found 21 QTL probably having multiple-effect in appearance and milling quality of indica rice. Combined with the results of previous studies, we concluded that qSW5, GSE5 and GS3 play important role in affecting appearance and milling quality. This research provides valuable information for cloning QTL controlling appearance and milling quality and breeding rice with high yield and good quality by molecular techniques.

Key words: Indica germplasms, Appearance and milling quality, GWAS, Quantitative trait loci/locus (QTL)

[1]梅德勇, 朱玉君, 樊叶杨. 籼稻稻米碾磨与外观品质性状的QTL定位. 遗传, 2012, 34: 1591–1598 Mei D Y, Zhu Y J, Fan Y Y. Mapping QTL for rice milling and appearance quality traits in indica rice. Hereditas (Beijing), 2012, 34: 1591–1598 (in Chinese with English abstract) [2]王丹英, 章秀福, 朱智伟, 陈能, 闵捷, 姚青, 严建立, 廖西元. 食用稻米品质性状间的相关性分析. 作物学报, 2005, 31: 1086-1091 Wang D Y, Zhang X F, Zhu Z W, Chen N, Min J, Yao Q, Yan J L, Liao X Y. Correlation analysis of rice grain quality characteristics. Acta Agron Sin, 2005, 31: 1086–1091(in Chinese with English abstract) [3]Wang X Q, Pang Y L, Wang C C, Chen K, Zhu Y J, Shen C C, Ali J, Xu J L, Li Z K. New candidate genes affecting rice grain appearance and milling quality detected by genome-wide and gene-based association analyses. Front Plant Sci, 2017, 7: 1998 [4]Xu J L, Yu S B, Luo L J , Zhong D B, Sanchez A, Mei H W, Khush G S , Li Z K. Molecular dissection of the primary sink size and its related traits in rice. Plant Breed, 2004, 123: 43–50 [5]周立军, 江玲, 翟虎渠, 万建民. 水稻垩白的研究现状与改良策略. 遗传, 2009, 31: 563–572 Zhou L J, Jiang L, Zhai H Q, Wan J M. Current status and strategies for improvement of rice grain chalkiness. Hereditas (Beijing), 2009, 31: 563–572 (in Chinese with English abstract) [6]Qiu X J, Chen K, Lv W K, Qu X X, Zhu Y J, Xing D Y, Yang L W, Fan F J, Yang J, Xu J L. Examining two sets of introgression lines reveals background-independent and stably expressed QTL that improve grain appearance quality in rice (Oryza sativa L.). Theor Appl Genet, 2017, doi: 101007/s00122-017-2862-z [7]Song X J, Huang W, Shi M, Zhu M Z, Lin H X. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet, 2007, 39: 623–630 [8]Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M. Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet, 2008, 40: 1023–1028 [9]Yan S, Zou G H, Li S J, Wang H, Liu H Q, Zhai G W, Guo P, Song H M, Yan C J, Tao Y Z. Seed size is determined by the combinations of the genes controlling different seed characteristics in rice. Theor Appl Genet, 2011, 123: 1173–1181 [10]Fan C C, Xing Y Z, Mao H L, Lu T T, Han B, Xu C G, Li X H, Zhang Q F. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet, 2006, 112: 1164–1171 [11]Wang Y X, Xiong G S, Hu J, Jiang L, Yu H, Xu J, Fang Y X, Zeng L J, Xu E B, Xu J, Ye W J, Meng X B, Liu R F, Chen H Q, Jing Y H, Wang Y H, Zhu X D, Li J Y, Qian Q. Copy number variation at the GL7 locus contributes to grain size diversity in rice. Nat Genet, 2015, 47: 944-948 [12]Wang S K, Li S, Liu Q, Wu K, Zhang J Q, Wang S S, Wang Y, Chen X B, Zhang Y, Gao C X, Wang F, Huang H X, Fu X D. The OsSPL16-GW7 regulatory module determines grain shape and simultaneously improves rice yield and grain quality. Nat Genet, 2015, 47: 949–954 [13]Sun L J, Li X J, Fu Y C, Zhu Z G, Tan L B, Liu F X, Sun X Y, Sun X W, Sun C Q. GS6, a member of the GRAS gene family, negatively regulates grain size in rice. J Integr Plant Biol, 2013, 55: 938–949 [14]Guo L B, Ma L L, Jiang H, Zeng D L, Hu J, Wu L W, Gao Z Y, Zhang G Z, Qian Q. Genetic analysis and fine mapping of two genes for grain shape and weight in rice. J Integr Plant Biol, 2009, 51: 45–51 [15]Singh R, Singh A K, Sharma T R, Singh A, Singh N K. Fine mapping of grain length QTLs on chromosomes 1 and 7 in Basmati rice (Oryza sativa L.). J Plant Biochem Biotechnol, 2012, 21: 157–166 [16]Li Y B, Fan C H, Xing Y Z, Yun P, Luo L J, Yan B, Peng B, Xie W B, Wang G W, Li X H, Xiao J H, Xu C G, He Y Q. Chalk5 encodes a vacuolar H(+)-translocating pyrophosphatase influencing grain chalkiness in rice. Nat Genet, 2014, 46: 398–404 [17]Wang E T, Wang J J, Zhu X D, Hao W, Wang L Y, Li Q, Zhang L X, He W, Lu B R, Lin H X, Ma H, Zhang G Q, He Z H. Control of rice grain-filling and yield by a gene with a potential signature of domestication. Nat Genet, 2008, 40: 1370–1374 [18]Zhang Y, Verhoeff N I, Chen Z, Chen S, Wang M, Zhu Z, Ouwerkerk P B F. Functions of OsDof25 in regulation of OsC4PPDK. Plant Mol Biol, 2015, 89: 229–242 [19]Ryoo N, Yu C, Park C S, Baik M Y, Park I M, Cho M H, Bhoo S h, An G, Hahn T R, Jeon J S. Knockout of a starch synthase gene OsSSIIIa/Flo5 causes white-core floury endosperm in rice (Oryza sativa L.). Plant Cell Rep, 2007, 26: 1083–1095 [20]Zhou L J, Chen L M, Jiang L, Zhang W W, Liu L J, Liu X, Zhao Z G, Liu S J , Zhang L J, Wang J K, Wan J M. Fine mapping of the grain chalkiness QTL qPGWC-7 in rice (Oryza sativa L.). Theor Appl Genet, 2009, 118: 581–590 [21]Ren D Y, Rao Y H, Huang L C, Leng Y J, Hu J, Lu M, Zhang G H, Zhu L, Gao Z Y, Dong G J, Guo L B, Qian Q, Zeng D L. Fine mapping identifies a new QTL for brown rice rate in rice (Oryza sativa L.). Rice, 2016, 9: 4–14 [22]Qiu X J, Pang Y L, Yuan Z H, Xing D Y, Xu J L, Dingkuhn M, Li Z K, Ye G Y. Genome-wide association study of grain appearance and milling quality in a worldwide collection of indica rice germplasm. PLoS One, 2015, 10: e0145577 [23]Zhao K Y, Tung C W, Eizenga G C, Wright M H, Ali ML, Price A H, Norton G J, Islam M R, Reynolds A, Mezey J, McClung A M, Bustamante C D, McCouch S R. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun, 2011, 2: 467–478 [24]唐富福, 徐非非, 包劲松. 全基因组关联分析在水稻遗传育种中的应用. 核农学报, 2013, 27: 598–606 Tang F F, Xu F F, Bao J S. Application of genome-wide association analysis in rice. Acta Agric Nucl Sin, 2013, 27: 598-606(in Chinese) [25]侯青青, 司丽珍, 黄学辉, 韩斌. 水稻复杂性状研究的新途径: 水稻重要农艺性状全基因组关联分析. 生命科学, 2016, 28: 1250–1257 Hou Q Q, Si L Z, Huang X H, Han B. Progress on genome-wide association study of important agronomic traits in rice. Chin Bull Life Sci, 2016, 28: 1250–1257 (in Chinese with English abstract) [26]邱先进, 袁志华, 陈凯, 杜斌, 何文静, 杨隆维, 徐建龙, 邢丹英, 吕文恺. 用全因组关联分析解析籼稻垩白的遗传基础. 作物学报, 2015, 41: 1007-1016 Qiu X J, Yuan Z H, Chen K, Du B, He W J, Yang W L, Xu J L, Xing D Y, Lyu W K. Genetic dissection of grain chalkiness in indica mini-core germplasm using genome-wide association method. Acta Agron Sin, 2015, 41: 1007–1016 (in Chinese with English abstract) [27]The 3,000 rice genomes project. The 3,000 rice genomes project. GigaScience, 2014, 3: 7–13 [28]Alexandrov N, Tai S, Wang W S, Mansueto L, Palis K, Fuentes RR, Ulat VJ, Chebotarov D, Zhang G Y, Li Z K. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res, 2015, 43: D1023 [29]Hu J, Wang Y X, Fang Y X, Zeng L J, Xu J, Yu H P, Shi Z T, Pan J J, Zhang D, Kang S J, Zhu L, Dong G J, Guo L B, Zeng D L, Zhang G H, Xie L H, Xiong G S, Li J Y, Qian Q. A rare allele of GS2 enhances grain size and grain yield in rice. Mol Plant, 2015, 8: 1455–1465 [30]Matsushima R, Maekawa M, Kusano M, Tomita K, Kondo H, Nishimura H, Crofts N, Fujita N, Sakamoto W. Amyloplast membrane protein SUBSTANDARD STARCH GRAIN6 controls starch grain size in rice endosperm. Plant Physiol, 2016, 170: 1445–1459 [31]Song X J, Kuroha T, Ayano M, Furuta T, Nagai K, Komeda N, Segami S, Miura K, Ogawa D, Kamura T, Suzuki T, Higashiyama T, Yamasaki M, Mori H, Inukai Y, Wu J Z, Kitano H, Sakakibara H, Jacobsen S E, Ashikari M. Rare allele of a previously unidentified histone H4 acetyltransferase enhances grain weight, yield, and plant biomass in rice. Proc Natl Acad Sci USA, 2015, 112: 76–81 [32]Feng Z M, Wu C Y, Wang C M, Roh J, Zhang L, Chen J, Zhang S Z, Zhang H, Yang C Y, Hu J L. SLG controls grain size and leaf angle by modulating brassinosteroid homeostasis in rice. J Exp Bot, 2016, 67: erw204 [33]Zhang X J, Wang J F, Huang J, Lan H X, Wang C L, Yin C F, Wu Y Y, Tang H J, Qian Q, Li J Y. Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc Natl Acad Sci USA, 2012, 109: 21534–21543 [34]Tan Y F, Xing Y Z, Li J X, Yu S B, Xu C G, Zhang Q F. Genetic bases of appearance quality of rice grains in Shanyou 63, an elite rice hybrid. Theor Appl Genet, 2000, 101: 823–829 [35]Zhao X Q, Zhou L J, Ponce K, Ye G Y. The usefulness of known genes/QTLs for grain quality traits in an indica population of diverse breeding lines tested using association analysis. Rice. 2015, 8: 29–42 [36]张云康, 林榕辉, 闵捷, 吴戍君, 朱智伟, 葛进平. 浙江水稻品种资源的品质研究. 作物品种资源, 1992, (4): 23–25 Zhang Y K, Lin R H, Min J, Wu S J, Zhu Z W, Ge J P. The research of germplasm on rice quality in Zhejiang province. Crop Variety Germplasm Res, 1992, (4): 23–25 (in Chinese) [37]徐正进, 陈温福, 马殿荣, 吕英娜, 周淑清, 刘丽霞. 稻谷粒形与稻米主要品质性状的关系. 作物学报, 2004, 30: 894–900 Xu Z J, Chen W F, Ma D R, Lyu Y N, Zhou S Q, Liu L X. Correlations between rice grain shapes and main qualitative characteristics. Acta Agron Sin, 2004, 30: 894–900 (in Chinese with English abstract) [38]Duan P G, Xu J S, Zeng D L, Zhang B L, Geng M F, Zhang G Z, Huang K, Huang L J, Xu R, Ge S, Qian Q, Li Y H. Natural variation in the promoter of GSE5 contributes to grain size diversity in rice. Mol Plant, 2017, doi: http: //dxdoiorg/101016/jmolp201703009 [39]Xu Y B, Lu Y K, Xie C X, Gao S B, Wan J M, Prasanna B M. Whole-genome strategies for marker-assisted plant breeding. Mol Breed, 2012, 29: 855–855 [40]Feng Y, Lu Q, Zhai R R, Zhang M C, Xu Q, Yang Y L, Wang S, Yuan X P, Yu H Y, Wang Y P, Wei X H. Genome wide association mapping for grain shape traits in indica rice. Planta, 2016, 244: 819–830 [41]Nagata K, Ando T, Nonoue Y, Mizubayashi T, Kitazawa N, Shomura A, Matsubara K, Ono N, Mizobuchi R, Shibaya T, Ogiso-Tanaka E, Hori K, Yano M, Fukuoka S. Advanced backcross QTL analysis reveals complicated genetic control of rice grain shape in a japonica × indica cross. Breed Sci, 2015, 65: 308–318

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