作物学报 ›› 2010, Vol. 36 ›› Issue (06): 895-904.doi: 10.3724/SP.J.1006.2010.00895
魏添梅1,2,昌小平2,闵东红1,景蕊莲2, *
WEI Tian-Mei1,2,CHANG Xiao-Ping2,MIN Dong-Hong1,JING Juan-Lian2*
摘要:
为了解北方冬麦区小麦抗旱品种的遗传多样性,筛选株高相关标记的等位变异,选用117个均匀分布于小麦各条染色体的SSR标记,对136份小麦抗旱品种进行分析。共检测到1 484个等位变异,平均每个标记12.6个等位变异,变化范围为2~42个,供试材料的遗传信息含量(PIC)变化范围为0.016~0.941,平均为0.640。聚类分析把同一地区或育种单位育成的品种、具有共同亲本的姊妹品种聚为一类,部分相近年代选育的品种也分别聚在一类,国外材料的基因导入对育成品种的遗传基础产生了影响。关联分析表明,在旱地条件下与株高显著相关的标记有19个(P<0.01),其中6个极显著相关(P<0.001);在水地条件下与株高显著相关的标记也有19个,其中7个极显著相关。水、旱两种条件下共检测出与株高极显著相关的标记9个,分别是Xbarc125(7D)、 Xbarc168(2D)、 Xgwm126(5A)、 Xgwm130(2B)、 Xgwm212(5D)、 Xgwm285(3B)、 Xgwm495(4B)、 Xgwm95(2A)、 Xwmc396(7B),其中Xgwm285的220 bp、Xgwm495的181 bp、Xgwm212的99 bp和Xbarc125的167 bp等位变异是与矮秆关联的优异等位基因。
[1] Zhang X-Y(张学勇), Tong Y-P(童依平), You G-X(游光霞), Hao C-Y(郝晨阳), Ge H-M(盖红梅), Wang L-F(王兰芬), Li B(李 滨), Dong Y-C(董玉琛), Li Z-S(李振声). Hitchhiking effect mapping: A new approach for discovering agronomic important genes. Sci Agric Sin (中国农业科学), 2006, 39(8): 1526-1535 (in Chinese with English abstract)[2] Tanksley S D, McCouch S R. Seed bank and molecular maps: Unlocking genetic potential from the wild. Science, 1997, 277: 1063-1066 [3] Börner A, Schumann E, Fürste A, Cöster H, Leithold B, Röder M S, Weber W E. Mapping of quantitative trait loci determine agronomic important characters in hexaploid wheat (Triticum aestivum L.). Theor Appl Genet, 2002, 105: 921-936[4] Farnir F, Coppieters W, Arranz J J, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P, Vanmanshoven P, Wagenaar D, Georges M. Extensive genome-wide linkage disequilibrium in cattle. Genome Res, 2000, 10: 220-227[5] Kruglyak L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat Genet, 1999, 22: 139-144[6] Jorde L B. Linkage disequilibrium and the search for complex disease genes. Genome Res, 2000, 10: 1435-1444[7] Eizonga G C, Agrama H A, Lee F N, Yan W, Jia Y. Identifying novel resistance genes in newly introduced blast resistant rice germplasm. Crop Sci, 2006, 46: 1870-1878[8] Flint-Garcia S A, Thuillet A C, Yu J M, Pressoir G, Romero S M, Sharon E. Mitchell S E, Doebley J, Kresovich S, Goodman M M, Buckler IV E S. Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J, 2005, 44: 1054–1064[9] Maccaferri M, Sanguineti M C, Enrico N, Roberto T. Population structure and long-range linkage disequilibrium in a durum wheat elite collection. Mol Breed, 2005, 15: 271-289[10] Zhuang Q-S(庄巧生). Chinese Wheat Improvement and Pedigree Analysis (中国小麦品种改良及系谱分析). Beijing:China Agriculture Press, 2003[11] Liu K J, Muse S V. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics, 2005, 21: 2128-2129[12] Pritchard J K, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics, 2000, 155: 945-959[13] Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol, 2005, 14: 2611-2620[14]Gupta P K, Rustgi S, Kulwal P L. Linkage disequilibrium and association studies in higher plants: Present status and future prospects. Plant Mol Biol, 2005, 57: 461-485[15] Jing R-L(景蕊莲), Chang X-P(昌小平). Applications of SSR markers in wheat germplasm.Crop Variety Resour (作物品种资源), 1999, (2): 17-20(in Chinese)[16] Beló A, Zheng P Z, Luck S, Shen B, Meyer D J, Li B, Tingey S, Rafalski A. Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Mol Genet Genomics, 2008, 279: 1-10[17] Andersen J R, Schrag T, Melchinger A E, Zein I, Lübberstedt T. Validation of Dwarf8 polymorphisms associated with flowering time in elite European inbred lines of maize (Zea mays L.). Theor Appl Genet, 2005, 111: 206-217[18]Ducrocq S, Madur D, Veyrieras J B, Camus-Kulandaivelu L, Kloiber-Maitz M, Presterl T, Ouzunova M, Manicacci D, Charcosset A. Key impact of Vgt1 on flowering time adaptation in maize: Evidence from association mapping and ecogeographical information. Genetics, 2008, 178: 2433-2437[19]Agrama H A, Eizenga G C, Yan W. Association mapping of yield and its components in rice cultivars. Mol Breed, 2007, 19: 341-356[20] Breseghello F, Sorrells M E. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics, 2006, 172: 1165-1177[21] Li X-J(李小军), Xu X(徐鑫), Liu W-H(刘伟华), Li X-Q(李秀全), Li L-H(李立会). Genetic diversity of the founder parent Orofen and its progenies revealed by SSR markers. Sci Agric Sin (中国农业科学), 2009. 42(10): 3397-3404 (in Chinese with English abstract)[22] Liu Z H, Anderson J A, Hu J, Friesen T L, Rasmussen J B & Faris J D. Wheat genetic linkage map based on microsatellite and target region amplified polymorphism markers and its utility for detecting quantitative trait loci. Theor Appl Genet, 2005, 111: 782-794[23] Pritchard J K, Stephens M, Rosenberg N A, Donnelly P. Association mapping in structured populations. Am J Hum Genet, 2000, 67: 170-181[24] Beer S C, Siripoonwiwat W, O’donoughue L S, Souza E, Matthews D, Sorrels M E. Associations between molecular markers and quantitative traits in an oat germplasm pool: can we infer linkages? J Agric Genomics, 1997, 3: 197[25] Virk P S, Fordlloyd B V, Jackson M T, Pooni H S, Clemeno T P, Newbury H J. Predicting quantitative variation within rice germplasm using molecular markers. Heredity, 1996, 76: 96-304[26] Thornsberry J M, Goodman M M, Doebley J, Kresovich S, Nielsen D, Buckler IV E S. Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet, 2001, 28: 286-289[27] Wilson L M, Whitt S R, Ibanez A M, Rochefor T R, Goodman M M, Buckler IV E S. Dissection of maize kernel composition and starch production by candidate gene association. Plant Cell, 2004, 16: 2719-2733[28] Szalma S J, Buckler IV E S, Snook M E, McMullen M D. Association analysis of candidate genes for maysin and chlorogenic acid accumulation in maize silk. Theor Appl Genet, 2005, 110: 1324-1333[29] Yu J M, Holland J B, McMullen M D, Buckler E S. Genetic design and statistical power of nested association mapping in maize. Genetics, 2008, 178: 539-551 |
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