作物学报 ›› 2023, Vol. 49 ›› Issue (1): 86-96.doi: 10.3724/SP.J.1006.2023.12079
徐凯1(), 郑兴飞2, 张红燕1, 胡中立3, 宁子岚1, 李兰芝1,*()
XU Kai1(), ZHENG Xing-Fei2, ZHANG Hong-Yan1, HU Zhong-Li3, NING Zi-Lan1, LI Lan-Zhi1,*()
摘要:
抽穗期受光温资源和基因网络的共同调控, 影响作物产量和品种的地域适应性, 通过关联分析鉴定与抽穗期性状相关的显著位点和基因, 对解析抽穗期的遗传基础具有重要意义。本研究按照双因素交叉式遗传设计(North Carolina design II, NCII)将115份籼稻品种作为父本, 5份不育系作为母本, 进行测交, 得到了575个F1代的测交群体。对亲本和F1代抽穗期的表型值、亲本品种群体的一般配合力、F1代群体的特殊配合力和超亲优势值进行全基因组关联分析, (1) 共定位到104个关联位点, 分布在12条染色体上。其中, 第4条染色体上检测到的显著位点最多, 为16个。在亲本的抽穗期表型、一般配合力、F1代的抽穗期表型、特殊配合力、超亲优势值5个数据集中分别检测到6、5、15、57和21个。对这5个数据集中的显著关联位点进行表型变异分析, 发现在亲本抽穗期表型、一般配合力、F1代抽穗期表型、特殊配合力和超亲优势值这5个数据集中的显著关联位点对表型变异的总贡献率(phenotypic variation explained, PVE)分别为79.57%、10.51%、33.35%、56.42%和54.86%。其中, 25个位点在多个数据集中被检测到, 可能为抽穗期相关的热点区域。(2) 通过关联分析得到的显著位点与日本晴参考基因组注释信息比对, 共检测到5个已克隆抽穗期基因, 其中3个基因与显著关联位点的基因组距离小于200 kb, 对这3个基因中的单倍型组合与单个基因的优异单倍型进行比较发现, 亲本品种群体中单倍型组合SDG724 (Hap.A)_Hd17 (Hap. E)_Ghd7 (Hap. A)的对应的水稻单株籽粒产量较高, 抽穗期较长, 该组合中各基因的单倍型对应于单个克隆基因的优异单倍型, 表明优异单倍型的聚合的常规稻, 具有更高的产量和更长的抽穗期。测交子代未见此情形, 测交子代群体中SDG724 (Hap. I)_Hd17 (Hap. K)_Ghd7 (Hap. I)的单倍型组合形式的水稻有适中的抽穗期和较高的产量, 说明测交子代的抽穗期遗传机制较父本(常规水稻品种)复杂。全基因组关联分析和单倍型分析相结合, 能利用到多个SNP提供的连锁不平衡信息, 提高了基因检测效率, 对培育高产的水稻品种具有重要的指导意义。
[1] |
Cai M H, Zhu S S, Wu M M, Zheng X M, Wang J C, Zhou L, Zheng T H, Cui S, Zhou S R, Li C N, Zhang H, Chai J T, Zhang X Y, Jin X, Cheng Z J, Zhang X, Lei C L, Ren Y L, Lin Q B, Guo X P, Zhao L, Wang J, Zhao Z C, Jiang L, Wang H Y, Wan J M. DHD4, a CONSTANS-like family transcription factor, delays heading date by affecting the formation of the FAC complex in rice. Mol Plant, 2021, 14: 330-343.
doi: 10.1016/j.molp.2020.11.013 pmid: 33246053 |
[2] |
Nemoto Y, Nonoue Y, Yano M, Izawa T. Hd1, a CONSTANS ortholog in rice, functions as an Ehd1 repressor through interaction with monocot-specific CCT-domain protein Ghd7. Plant J, 2016, 86: 221-233.
doi: 10.1111/tpj.13168 |
[3] |
Sakamoto T, Kimura S. Plant temperature sensors. Sensors, 2018, 18: 4365.
doi: 10.3390/s18124365 |
[4] |
Zhao Q, Feng Q, Lu H Y, Li Y, Wang A H, Tian Q L, Zhan Q L, Lu Y Q, Zhang L, Huang T, Wang Y C, Fan D L, Zhao Y, Wang Z Q, Zhou C C, Chen J Y, Zhu C R, Li W J, Weng Q J, Xu Q, Wang Z X, Wei X H, Han B, Huang X H. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat Genet, 2018, 50: 278-284.
doi: 10.1038/s41588-018-0041-z pmid: 29335547 |
[5] |
Wei X, Qiu J, Yong K C, Fan J J, Zhang Q, Hua H, Liu J, Wang Q, Olsen K M, Han B, Huang X H. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat Genet, 2021, 53: 243-253.
doi: 10.1038/s41588-020-00769-9 pmid: 33526925 |
[6] |
Wang W, Hu B, Yuan D Y, Liu Y Q, Che R H, Hu Y C, Ou S J, Liu Y X, Zhang Z H, Wang H R, Li H, Jiang Z M, Zhang Z L, Gao X K, Qiu Y H, Meng X B, Liu Y X, Bai Y, Liang Y, Wang Y Q, Zhang L H, Li L G, Sodmergen, Jing H C, Li J Y, Chu C C. Expression of the nitrate transporter gene OsNRT1.1A/OsNPF6.3 confers high yield and early maturation in rice. Plant Cell, 2018, 30: 638-651.
doi: 10.1105/tpc.17.00809 |
[7] |
Purugganan M D. Evolutionary insights into the nature of plant domestication. Curr Biol, 2019, 29: R705-R714.
doi: 10.1016/j.cub.2019.05.053 |
[8] |
Lai X J, Bendix C, Yan L, Zhang Y, Schnable J C, Harmon F G. Interspecific analysis of diurnal gene regulation in panicoid grasses identifies known and novel regulatory motifs. BMC Genomics, 2020, 21: 428.
doi: 10.1186/s12864-020-06824-3 pmid: 32586356 |
[9] |
Lu S J, Zhao X H, Hu Y L, Liu S L, Nan H Y, Li X M, Fang C, Cao D, Shi X Y, Kong L P, Su T, Zhang F G, Li S C, Wang Z, Yuan X H, Cober E R, Weller J L, Liu B H, Hou X L, Tian Z X, Kong F J. Natural variation at the soybean J locus improves adaptation to the tropics and enhances yield. Nat Genet, 2017, 49: 773-779.
doi: 10.1038/ng.3819 pmid: 28319089 |
[10] |
Zhang B, Liu H Y, Qi F X, Zhang Z Y, Li Q P, Han Z M, Xing Y Z. Genetic interactions among Ghd7, Ghd8, OsPRR37 and Hd1 contribute to large variation in heading date in rice. Rice, 2019, 12: 48.
doi: 10.1186/s12284-019-0314-x pmid: 31309345 |
[11] |
Lu Q, Zhang M C, Niu X J, Wang S, Xu Q, Feng Y, Wang C H, Deng H Z, Yuan X P, Yu H Y, Wang Y P, Wei X H. Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genomics, 2015, 16: 1067.
doi: 10.1186/s12864-015-2245-2 |
[12] |
Wang Q X, Xie W B, Xing H K, Yan J, Meng X Z, Li X L, Fu X K, Xu J Y, Lian X M, Yu S B, Xing Y Z, Wang G W. Genetic architecture of natural variation in rice chlorophyll content revealed by a genome-wide association study. Mol Plant, 2015, 8: 946-957.
doi: 10.1016/j.molp.2015.02.014 pmid: 25747843 |
[13] |
Yano K, Yamamoto E, Aya K, Takeuchi H, Lo P C, Hu L, Matsuoka M. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet, 2016, 48: 927-934.
doi: 10.1038/ng.3596 pmid: 27322545 |
[14] | Han Z M, Zhang B, Zhao H, Ayaad M, Xing Y Z. Genome-wide association studies reveal that diverse heading date genes respond to short- and long-day lengths between indica and japonica rice. Front Plant Sci, 2016, 7: 1270. |
[15] |
Jing L, Rui X, Wang C C, Lan Q, Zheng X M, Wang W S, Ding Y B, Zhang L Z, Wang Y Y, Cheng Y L, Zhang L F, Qiao W H, Yang Q W. A heading date QTL, qHD7.2, from wild rice (Oryza rufipogon) delays flowering and shortens panicle length under long-day conditions. Sci Rep, 2018, 8: 2928.
doi: 10.1038/s41598-018-21330-z pmid: 29440759 |
[16] |
Wang X, Xu Y, Hu Z L, Xu C W. Genomic selection methods for crop improvement: current status and prospects. Crop J, 2018, 6: 330-340.
doi: 10.1016/j.cj.2018.03.001 |
[17] |
Wang Y S, Cai Q H, Xie H G, Wu F X, Lian L, He W, Chen L P, xie H A, Zhang J F. Determination of heterotic groups and heterosis analysis of yield performance in indica rice. Rice Sci, 2018, 25: 261-269.
doi: 10.1016/j.rsci.2018.08.002 |
[18] |
Belser C, Istace B, Denis E, Dubarry M, Baurens F C, Falentin C, Aury J M. Chromosome-scale assemblies of plant genomes using nanopore long reads and optical maps. Nat Plants, 2018, 4: 879-887.
doi: 10.1038/s41477-018-0289-4 pmid: 30390080 |
[19] |
Pushalkar S, Hundeyin M, Daley D, Zambirinis C P, Kurz E, Mishra A, Miller G. The pancreatic cancer microbiome promotes oncogenesis by induction of innate and adaptive immune suppression. Cancer Discover, 2018, 8: 403-416.
doi: 10.1158/2159-8290.CD-17-1134 |
[20] |
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 2018, 34: 3094-3100.
doi: 10.1093/bioinformatics/bty191 pmid: 29750242 |
[21] |
Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet, 2019, 20: 467-484.
doi: 10.1038/s41576-019-0127-1 pmid: 31068683 |
[22] |
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A, Bender D, Maller J, Sklar P, Bakker P I, Daly M J, Sham P C. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Human Genet, 2007, 81: 559-575.
doi: 10.1086/519795 |
[23] |
Li B, Dewey C N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinfor, 2011, 12: 323.
doi: 10.1186/1471-2105-12-323 |
[24] |
Lipka A E, Tian F, Wang Q S, Peiffer J, Li M, Bradbury P J, Gore M A, Buckler E S, Zhang Z W. GAPIT: genome association and prediction integrated tool. Bioinformatics, 2012, 28: 2397-2399.
pmid: 22796960 |
[25] |
Kang H M, Sul J H, Service S K, Zaitlen N A, Kong S Y, Freimer N B, Sabatti C, Eskin E. Variance component model to account for sample structure in genome-wide association studies. Nat Genet, 2010, 42: 348-354.
doi: 10.1038/ng.548 pmid: 20208533 |
[26] | 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) | |
[27] |
Fang C, Ma Y M, Wu S W, Liu Z, Wang Z, Yang R, Hu G H, Zhou Z K, Yu H, Zhang M, Pan Y, Zhou G A, Ren H X, Du W G, Yan H R, Wang Y P, Han D Z, Shen Y T, Liu S L, Liu T F, Zhang J X, Qin H, Yuan J, Yuan X H, Kong F J, Li J Y, Zhang Z W, Wang G D, Zhu B G, Tian Z. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol, 2017, 18: 161.
doi: 10.1186/s13059-017-1289-9 pmid: 28838319 |
[28] | Sakai H, Lee S S, Tanaka T, Numa H, Kim J, Kawahara Y, Itoh T. Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics. Plant Cell Phys, 2013, 54: e6. |
[29] | 董骥驰, 杨靖, 郭涛, 陈立凯, 陈志强, 王慧. 基于高密度Bin图谱的水稻抽穗期QTL定位. 作物学报, 2018, 44: 938-946. |
Dong J C, Yang J, Guo T, Chen L K, Chen Z Q, Wang H. QTL mapping for heading date in rice using high-density Bin map. Acta Agron Sin, 2018, 44: 938-946. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2018.00938 |
|
[30] |
Sun C H, Fang J, Zhao T L, Xu B, Zhang F T, Liu L C, Tang J Y, Zhang G F, Deng X J, Chen F, Qian Q, Cao X F, Chu C C. The histone methyltransferase SDG724 mediates H3K36me2/3 deposition at MADS50 and RFT1 and promotes flowering in rice. Plant Cell, 2012, 24: 3235-3247.
doi: 10.1105/tpc.112.101436 |
[31] |
Zhao S Q, Xiang J J, Xue H W. Studies on the rice LEAF INCLINATION1 (LC1), an IAA-amido synthetase, reveal the effects of auxin in leaf inclination control. Mol Plant, 2013, 6: 174-187.
doi: 10.1093/mp/sss064 |
[32] | Kanneganti V, Gupta A K. Isolation and Expression analysis of OsPME1, encoding for a putative Pectin Methyl Esterase from Oryza sativa (subsp. indica). Phys Mol Biol Plants, 2009, 15: 123-131. |
[33] |
Zhao Y, Cheng S F, Song Y L, Huang Y L, Zhou S L, Liu X Y, Zhou D X. The interaction between rice ERF3 and WOX11 promotes crown root development by regulating gene expression involved in cytokinin signaling. Plant Cell, 2015, 27: 2469-2483.
doi: 10.1105/tpc.15.00227 |
[34] |
Marigorta U M, Rodríguez J A, Gibson G, Navarro A. Replicability and prediction: lessons and challenges from GWAS. Trends Genet, 2018, 34: 504-517.
doi: S0168-9525(18)30060-X pmid: 29716745 |
[35] | De R, Bush W S, Moore J H. Bioinformatics challenges in genome-wide association studies (GWAS). Clinic Bioinfor, 2014, 1168: 63-81. |
[36] |
Qian L W, Hickey L T, Stahl A, Werner C R, Hayes B, Snowdon R J, Voss-Fels K P. Exploring and harnessing haplotype diversity to improve yield stability in crops. Front Plant Sci, 2017, 8: 1534.
doi: 10.3389/fpls.2017.01534 pmid: 28928764 |
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