作物学报 ›› 2022, Vol. 48 ›› Issue (8): 1894-1904.doi: 10.3724/SP.J.1006.2022.14114
张胜忠1(), 胡晓辉1, 慈敦伟1, 杨伟强1, 王菲菲1, 邱俊兰2, 张天雨3, 钟文3, 于豪諒4, 孙冬平4, 邵战功5, 苗华荣1,*(), 陈静1,*()
ZHANG Sheng-Zhong1(), HU Xiao-Hui1, CI Dun-Wei1, YANG Wei-Qiang1, WANG Fei-Fei1, QIU Jun-Lan2, ZHANG Tian-Yu3, ZHONG Wen3, YU Hao-Liang4, SUN Dong-Ping4, SHAO Zhan-Gong5, MIAO Hua-Rong1,*(), CHEN Jing1,*()
摘要:
荚果网纹厚度, 不仅是花生重要的品种特性, 也与花生适宜机械化收获特性密切相关。为探索花生荚果网纹厚度遗传基础, 本研究开发了一种基于三维模型重构测定网纹厚度的方法, 并且以品种花育36号和品系6-13配组衍生的181个重组自交系(recombinant inbred line, RIL)群体为材料, 考察了该RIL群体2019—2020年在山东青岛、东营和威海3个环境下表型数据。结果表明, 横向和纵向网纹厚度在RIL群体中均表现为连续分布和超亲遗传, 广义遗传率分别为0.92和0.91。利用前期构建的高密度遗传图谱, 共定位到11个与网纹厚度相关加性QTL, 其中6个与横纹厚度相关, 5个与纵纹厚度相关, 表型贡献率范围为5.21%~11.06%。定位到2个主效位点qLA2和qLO9, 可在不同环境下表达, 其增效等位基因分别来自花育36号和6-13。共定位到22对上位性QTL, 共涉及34个位点, 表型贡献率范围为0.55%~4.37%, 其中10对与横纹厚度相关, 12对与纵纹厚度相关。本研究结果将为花生相关性状基因定位和分子育种提供重要的参考。
[1] | FAO FAO Statistical Database. Rome, Italy. Available: http://faostat.fao.org. |
[2] | 禹山林. 中国花生品种及其系谱. 上海: 上海科学技术出版社, 2008. pp 55-58. |
Yu S L. Chinese Peanut Cultivars and Their Pedigrees. Shanghai: Shanghai Scientific and Technical Publishers, 2008. pp 55-58. (in Chinese) | |
[3] |
Mondal S, Badigannavar A M. Identification of major consensus QTLs for seed size and minor QTLs for pod traits in cultivated groundnut (Arachis hypogaea L.). 3 Biotech, 2019, 9: 347.
doi: 10.1007/s13205-019-1881-7 |
[4] | Patil V H. Genetic Studies in Groundnut (Arachis hypogaea L.). MS Thesis of Poona University, Poona, India, 1965. |
[5] | Jadhav G D, Shinde N N. Studies in groundnut (Arachis hypogaea). India J Agric Res, 1979, 13: 93-96. |
[6] |
Murthy T G K, Tiwari S P, Reddy P S. A linkage group for genes governing pod characters in peanut.v Euphytica, 1988, 39: 43-46.
doi: 10.1007/BF00025109 |
[7] | Manoharan V, Ramalingam R S. Inheritance of testa colour and pod reticulation in groundnut. Madras Agric J, 1992, 79: 646-648. |
[8] | 周金超, 杨鑫雷, 崔顺立, 侯名语, 陈焕英, 穆国俊, 刘立峰. 花生SSR标记与农艺性状的相关性. 作物学报, 2014, 40: 1197-1204. |
Zhou J C, Yang X L, Cui S L, Hou M Y, Chen H Y, Mu G J, Liu L F. Correlation between SSR markers and agronomic traits in peanut (Arachis hypogaea L.). Acta Agron Sin, 2014, 40: 1197-1204. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2014.01197 |
|
[9] | 郭慧敏. 栽培种花生染色体片段置换系群体的构建及部分农艺性状QTL定位. 河北农业大学硕士学位论文,河北保定, 2014. |
Guo H M. Construction of Chromosome Segement Substitution Lines and QTLs Mapping for Agronomic Traits in Cultivated Peanut. MS Thesis of Agricultural University of Hebei, Baoding, Hebei, China, 2014. (in Chinese with English abstract) | |
[10] | Hu W J, Zhang C, Jiang Y Q, Huang C L, Liu Q, Xiong L Z, Yang W N, Chen F. Nondestructive 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography. Plant Phenomics, 2020, 2020: 1-12. |
[11] |
Su Y, Xiao L T. 3D visualization and volume-based quantification of rice chalkiness in vivo by using high resolution micro-CT. Rice, 2020, 13: 69.
doi: 10.1186/s12284-020-00429-w |
[12] | Dornbusch T, Wernecke P, Diepenbrock W. A method to extract morphological traits of plant organs from 3D point clouds as a database for an architectural plant model. Ecol Model, 2007, 200: 119-129. |
[13] |
Ivanov N, Boissard P, Chapron M, Andrieu B. Computer stereo plotting for 3-D reconstruction of a maize canopy. Agric For Meteorol, 1995, 75: 85-102.
doi: 10.1016/0168-1923(94)02204-W |
[14] |
Biskup B, Scharr H, Schurr U, Rascher U. A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ, 2007, 30: 1299-1308.
doi: 10.1111/j.1365-3040.2007.01702.x |
[15] | McCarthy C L, Hancock N H, Raine S R. Applied machine vision of plants: a review with implications for field deployment in automated farming operations. Intel Serv Robot, 2010, 3: 209-217. |
[16] |
Pound M P, French A P, Murchie E H, Pridmore P. Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol, 2014, 166: 1688-1698.
doi: 10.1104/pp.114.248971 |
[17] | 胡鹏程, 郭焱, 李保国, 朱晋宇, 马韫韬. 基于多视角立体视觉的植株三维重建与精度评估. 农业工程学报, 2015, 31(11): 209-214. |
Hu P C, Guo Y, Li B G, Zhu J Y, Ma Y T. Three-dimensional reconstruction and its precision evaluation of plant architecture based on multiple view stereo method. TCSA Engin, 2015, 31(11): 209-214. (in Chinese with English abstract) | |
[18] |
Lowe D G. Distinctive image features from scale-invariant keypoints. Int J Comput Vision, 2004, 60: 91-110.
doi: 10.1023/B:VISI.0000029664.99615.94 |
[19] | 艾海舟, 兴军亮. 计算机视觉--算法与应用. 北京: 清华大学出版社, 2012. pp 237-290. |
Ai H Z, Xing J L. Computer Vision:Algorithms and Applications. Beijing: Tsinghua University Press, 2012. pp 237-290. (in Chinese) | |
[20] |
Furukawa Y, Ponce J. Accurate, dense, and robust multiview stereopsis. IEEE Trans Pattern Anal Mach Intell, 2010, 32: 1362-1376.
doi: 10.1109/TPAMI.2009.161 |
[21] | Kazhdan M, Hoppe H. Screened poisson surface reconstruction. ACM Trans Graph, 2013, 32: 29. |
[22] |
Meng L, Li H H, Zhang L Y, Wang J K. QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J, 2015, 3: 269-283.
doi: 10.1016/j.cj.2015.01.001 |
[23] |
Liu N, Guo J B, Zhou X J, Wu B, Huang L, Luo H Y, Chen Y N, Chen W G, Lei Y, Huang Y, Liao B S, Jiang H F. High-resolution mapping of a major and consensus quantitative trait locus for oil content to a -0.8-Mb region on chromosome A08 in peanut (Arachis hypogaea L.). Theor Appl Genet, 2020, 133: 37-49.
doi: 10.1007/s00122-019-03438-6 |
[24] |
Zhang S Z, Hu X H, Miao H R, Chu Y, Cui F G, Yang W Q, Wang C M, Shen Y, Xu T T, Zhao L B, Zhang J C, Chen J. QTL identification for seed weight and size based on a high-density SLAF-seq genetic map in peanut (Arachis hypogaea L.). BMC Plant Biol, 2019, 19: 537.
doi: 10.1186/s12870-019-2164-5 |
[25] | Silva L C, Wang S, Zeng Z B. Composite interval mapping and multiple interval mapping: procedures and guidelines for using Windows QTL Cartographer. Methods Mol Biol, 2012, 871: 75-119. |
[26] |
Voorrips R E. Mapchart: software for the graphical presentation of linkage map and QTL. J Hered, 2002, 93: 77-78.
pmid: 12011185 |
[27] | McCouch S R, Cho Y G, Yano M, Paul E, Blinstrub M, Morishima H, Kinosita T. Report on QTL nomenclature. Rice Genet Newl, 1997, 14: 11-13. |
[28] | Chen Y N, Ren X P, Zheng Y L, Zhou X J, Huang L, Yan L Y, Jiao Y Q, Chen W G, Huang S M, Wan L Y, Lei Y, Liao B S, Huai D X, Wei W H, Jiang H F. Genetic mapping of yield traits using RIL population derived from Fuchuan Dahuasheng and ICG6375 of peanut (Arachis hypogaea L.). Mol Plant, 2017, 37: 17. |
[29] |
Wang Z J, Huai D X, Zhang Z H, Cheng K, Kang Y P, Wan L Y, Yan L Y, Jiang H F, Lei Y, Liao B S. Development of a high-density genetic map based on specific length amplified fragment sequencing and its application in quantitative trait loci analysis for yield-related traits in cultivated peanut. Front Plant Sci, 2018, 9: 827.
doi: 10.3389/fpls.2018.00827 |
[30] |
Hagiwara W E, Onishi K, Takamure O I, Sano Y. Transgressive segregation due to linked QTLs for grain characteristics of rice. Euphytica, 2006, 150: 27-35.
doi: 10.1007/s10681-006-9085-8 |
[31] |
Balakrishnan D, Surapaneni M, Yadavalli V R, Addanki K R, Mesapogu S, Beerelli K, Neelamraju S. Detecting CSSLs and yield QTLs with additive, epistatic and QTL × environment interaction effects from Oryza sativa × O. nivara IRGC81832 cross. Sci Rep, 2020, 10: 7766.
doi: 10.1038/s41598-020-64300-0 pmid: 32385410 |
[32] |
Li Z K, Luo L J, Mei H W, Wang Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice: I. Biomass and grain yield. Genetics, 2001, 158: 1737-1753.
doi: 10.1093/genetics/158.4.1737 pmid: 11514459 |
[33] |
Carlbog O, Haley C S. Epistasis: too often neglected in complex trait studies? Nat Rev Genet, 2004, 5: 618-625.
doi: 10.1038/nrg1407 |
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