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作物学报 ›› 2018, Vol. 44 ›› Issue (01): 43-52.doi: 10.3724/SP.J.1006.2018.00043

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

群体构成方式对大豆百粒重全基因组选择预测准确度的影响

马岩松1,2,13,刘章雄1,文自翔3,魏淑红4,杨春明5,王会才6,杨春燕7,卢为国8,徐冉9,张万海10,吴纪安11,胡国华12,栾晓燕13,付亚书14,郭泰15,王曙明5,韩天富1,张孟臣7,张磊16,苑保军17,郭勇1,Jochen C. REIF18,江勇18,李文滨2,王德春3,邱丽娟1,*   

  1. 1 中国农业科学院作物科学研究所 / 国家农作物基因资源与遗传改良重大科学工程 / 农业部作物种质资源与生物技术重点开放实验室，北京100081；2 东北农业大学农学院，黑龙江哈尔滨150030；3 Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing MI 48824, USA；4 黑龙江省农业科学院育种研究所，黑龙江哈尔滨150081；5 吉林省农业科学院大豆研究所，吉林长春130033；6 内蒙古赤峰市农科所，内蒙古赤峰024031；7 河北省农业科学院粮油作物研究所，河北石家庄050031；8 河南省农业科学院经作所，河南郑州450002；9 山东省农业科学院作物研究所，山东济南250010；10 内蒙古呼伦贝尔市农科所，内蒙古呼伦贝尔021000；11 黑龙江省农业科学院黑河分院，黑龙江黑河164300；12 黑龙江省农垦科研育种中心，黑龙江哈尔滨150090；13 黑龙江省农业科学院大豆研究所，黑龙江哈尔滨150086；14 黑龙江省农业科学院绥化分院，黑龙江绥化152052；15 黑龙江省农业科学院佳木斯分院，黑龙江佳木斯154007；16 安徽省农业科学院作物研究所，安徽合肥230031；17 河南省周口市农业科学院，河南周口466001；18 Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben 06466, Germany
  • 收稿日期:2017-02-10 修回日期:2017-09-10 出版日期:2018-01-12 网络出版日期:2017-10-30
  • 基金资助:

    本研究由国家转基因生物新品种培育重大专项(2014ZX08004001)和中国农业科学院农业科技创新项目资助。

Effect of Population Structure on Prediction Accuracy of Soybean 100-Seed Weight by Genomic Selection MA Yan-Song1,2,13, LIU Zhang-Xiong1, WEN Zi-Xiang3, WEI Shu-Hong4, YANG Chun-Ming5, WANG Hui-Cai6, YANG

MA Yan-Song1,2,13, LIU Zhang-Xiong1, WEN Zi-Xiang3, WEI Shu-Hong4, YANG Chun-Ming5, WANG Hui-Cai6,YANG Chun-Yan7, LU Wei-Guo8, XU Ran9, ZHANG Wan-Hai10, WU Ji-An11, HU Guo-Hua12, LUAN Xiao-Yan13, FU Ya-Shu14, GUO Tai15, WANG Shu-Ming5, HAN Tian-Fu1, ZHANG Meng-Chen7, ZHANG Lei16, YUAN Bao-Jun17, GUO Yong1, Jochen C. REIF18, JIANG Yong18, LI Wen-Bin2, WANG De-Chun3,QIU Li-Juan1,*   

  1. 1 National Key Facility for Crop Gene Resources and Genetic Improvement / Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 2 College of Agriculture, Northeast Agricultural University, Harbin 150030, China; 3 Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing MI 48824, United States of America; 4 Institute of Crop Breeding, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, Heilongjiang, China; 5 Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun 130033, Jilin, China; 6 Chifeng Institute of Agricultural Sciences, Chifeng 024031, Inner Mongolia, China; 7 Institution of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050031, Hebei, China; 8 Economic Crops Institute, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China; 9 Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250010, Shandong, China; 10 Hulunbeier Institute of Agricultural Sciences, Hulunbeier 021000, Inner Mongolia, China; 11 Heihe Branch Institute, Heilongjiang Academy of Agricultural Sciences, Heihe 164300, Heilongjiang, China; 12 The Crop Research and Breeding Center of Land-Reclamation, Harbin 150090, Heilongjiang, China; 13 Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, Heilongjiang, China; 14 Suihua Branch Institute, Heilongjiang Academy of Agricultural Sciences, Suihua 152052, Heilongjiang, China; 15 Jiamusi Branch Institute, Heilongjiang Academy of Agricultural Sciences, Jiamusi 154007, Heilongjiang, China; 16 Crop Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, Anhui, China; 17 Zhoukou Institute of Agricultural Sciences, Zhoukou 466001, Henan, China; 18 Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben 06466, Germany
  • Received:2017-02-10 Revised:2017-09-10 Published:2018-01-12 Published online:2017-10-30
  • Supported by:

    This study was supported by the National Major Project for Developing New GM Crops (2014ZX08004001) and the Agricultural Science and Technology Innovation Program (ASTIP) of Chinese Academy of Agricultural Sciences.

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摘要：

百粒重是大豆产量的重要构成因子，在一定条件下与产量呈显著正相关。百粒重是一个复杂的数量性状，用传统的育种方法其遗传增益不明显。本研究对280份大豆品种多年多点田间鉴定，通过混合线性模型预测获得品种百粒重的最佳线性无偏预测值。同时利用分布在大豆全基因组的5361个SNP标记鉴定参试品种基因型，结合随机回归最佳线性无偏预测模型和交互验证方法，探讨了群体构成方式对大豆百粒重的全基因组选择预测准确度的影响。结果表明，大豆百粒重的全基因组选择预测准确度变化范围为–0.15~ +0.75；群体构成方式对百粒重的预测准确度影响明显；亚群内的预测准确度(0.24~0.75)高于亚群间(?0.15~ +0.29)；当群体间遗传距离由0.1566增加到0.2201时，预测准确度下降27.87%；相比随机构建的训练群体，基于群体遗传结构构建的训练群体能将百粒重的预测准确度提高2.34%。本研究明确了大豆百粒重的全基因组选择预测准确度，阐明了群体结构对大豆百粒重的全基因组选择预测准确度的影响，为大豆分子育种提供了新的思路和方法。

关键词: 大豆, 百粒重, 全基因组选择, 预测准确度, 遗传结构

Abstract:

Hundred-seed weight is an important yield component and has positive relationship with soybean yield under certain conditions. The genetic gain of 100-seed weight based on traditional breeding or markers assisted-selection is limited because it is controlled by plenty of small effect genes. Genomic selection offers an approach to accelerate the soybean 100-seed weight breeding. However, the effect of population structure on soybean 100-seed weight prediction accuracy has not been elaborated. In our study 280 soybean varieties with phenotypic data evaluated in multi-location in 2008–2012 and 5361 SNPs genotype were used to explore the effect of population structure on 100-seed weight prediction accuracy. The best linear unbiased prediction of 100-seed weight of each variety was calculated according to mixed linear model. Ridge regression best linear unbiased prediction and five-fold cross validation were used to estimate the 100-seed weight prediction accuracy. Our research showed that the range of 100-seed weight, which was from –0.15 to 0.75. Hundred-seed weight prediction accuracy was affected by population structure significantly. The prediction accuracy within subset (0.24 to 0.75) was higher than that between subsets (?0.15 to +0.29). When the genetic distance between subsets increased from 0.1566 to 0.2201, the 100-seed weight prediction accuracy was decreased by 27.87%. Compared with random sampling training population, the training population composed based on genetic structure improved 100-seed weight prediction accuracy by 2.34%. In summary we are clear about the soybean 100-seed weight genomic selection accuracy and the effect of population structure on genomic selection accuracy. The genomic selection is an efficient method to improve the soybean breeding.

Key words: Glycine max, 100-seed weight, Genomic selection, Prediction accuracy, Genetic structure

[1]盖钧镒, 熊冬金, 赵团结. 中国大豆育成品种系谱与种质基础(1923–2005). 北京: 中国农业出版社, 2015. pp 11–12 Gai J Y, Xiong D J, Zhao T J. The Pedigrees and Germplasm Bases of Soybean Cultivars Released in China (1923–2005). Beijing: China Agriculture Press, 2015. pp 11–12 (in Chinese) [2]徐东河, 李东艳, 程舜华. 大豆百粒重与抗旱性及产量的关系. 中国油料, 1991, (3): 64–66 Xu D H, Li D Y, Cheng S H. Relationship between 100-seed weight and anti-draught and yield of soybean. Oil Crops China, 1991, (3): 64–66 (in Chinese) [3]王占廷, 栾素荣, 程舜华. 大豆百粒重与产量的相关分析. 大豆通报, 1997, (2): 9 Wang Z T, Luan S R, Cheng S H. Relationship analysis between 100-seed weight and yield in soybean. Soybean Bull, 1997, (2): 9 (in Chinese) [4]汪霞, 徐宇, 李广军, 李河南, 艮文全, 章元明. 大豆百粒重QTL定位. 作物学报, 2010, 36: 1674–1682 Wang X, Xu Y, Li G J, Li H N, Gen W Q, Zhang Y M. Mapping quantitative trait loci for 100-seed weight in soybean(Glycine max L. Merr.). Acta Agron Sin, 2010, 36: 1674–1682 (in Chinese with English abstract) [5]陈庆山, 蒋洪蔚, 孙殿君, 刘春燕, 辛大伟, 曾庆力, 马占洲, 胡国华. 利用野生大豆染色体片段代换系定位百粒重QTL. 大豆科学, 2014, 33: 154–160 Chen Q S, Jiang H W, Sun D J, Liu C Y, Xin D W, Zeng Q L, Ma Z Z, Hu G H. QTL Mapping for 100-seed weight using wild soybean chromosome segment substitution lines. Soybean Sci, 2014, 33: 154–160 (in Chinese with English abstract) [6]张英虎, 孟珊, 贺剑波, 王宇峰, 邢光南, 赵团结, 盖钧镒. 大豆重组自交系群体NJRSXG百粒重超亲分离的遗传解析. 中国农业科学, 2015, 48: 4408–4416 Zhang Y H, Meng S, He J B, Wang Y F, Xing G N, Zhao T J, Gai J Y. The genetic constitution of transgressive segregation of the 100-seed weight in a recombinant inbred line population NJRSXG of soybean. Sci Agric Sin, 2015, 48: 4408–4416 (in Chinese with English abstract) [7]齐照明, 孙亚男, 陈立君, 郭强, 刘春燕, 胡国华, 陈庆山. 基于Meta分析的大豆百粒重的QTLs定位. 中国农业科学, 2009, 42: 3795–3803 Qi Z M, Sun Y N, Chen L J, Guo Q, Liu C Y, Hu G H, Chen Q S. Meta-analysis of 100-seed weight QTL in soybean. Sci Agric Sin, 2009, 42: 3795–3803 (in Chinese with English abstract) [8]Goddard M E, Hayes B J. Genomic selection. J Anim Breed Genet, 2007, 124: 323–330 [9]Jannink J L, Lorenz A J, Iwata H. Genomic selection in plant breeding: from theory to practice. Brief Funct Genom, 2010, 9: 166–177 [10]Nakaya A, Isobe S N. Will genomic selection be a practical method for plant breeding? Annals of Botany Bot, 2012, 110: 1303–1316 [11]Meuwissen T H E, Hayes B J, Goddar M E. Prediction of total genetic value using genome-wide dense marker maps. Geneics, 2001,157: 1819–1829 [12]Zhao Y, Gowda M, Liu W, Wurschum T, Maurer H P, Longin F H, Ranc N, Reif J C. Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet, 2012, 124: 769–776 [13]Zhao Y, Gowda M, Longin F H, Wurschum T, Ranc N, Reif J C. Impact of selective genotyping in the training population on accuracy and bias of genomic selection. Theor Appl Genet, 2012, 125: 707–713 [14]Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Rojas J C, Zhang X, Dreisigacker S, Babu R, Li Y, Mathews K. Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity, 2014,112: 48–60 [15]Sprdel J, Begum H, Akdemir D, Virk P, Collard B, Redona E, Atlin G, Jannink J L, McCouch S R. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet, 2015, 11: e1004982–e1004982 [16]Shu Y J, Yu D S, Wang D, Bai X, Zhu Y M, Guo C H. Genomic selection of seed weight based on low-density SCAR markers in soybean. Genet Mol Res, 2013, 12: 2178–2188 [17]Bao Y, Vuong T, Meinhardt C, Tiffin P, Denny R, Chen S Y, Nguyen H T, Orf J H, Young N D. Potential of association mapping and genomic selection to explore PI88788 derived soybean cyst nematode resistance. Plant Genome, 2014, 7: 1–13 [18]Dawson J C, Endelman J B, Heslot N, Crossa J, Poland J, Dreisigacker S, Manes Y, Sorrells M E, Jannink J L. The use of unbalanced historical data for genomic selection in an international wheat breeding program. Field Crops Res, 2013,154: 12–22 [19]Zhong S Q, Dekkers J C, Fernando R L, Jannink J L. Factors affecting accuracy from genomic selection in population derived from multiple inbred lines: a barley case study. Genetics, 2009, 182: 355–364 [20]Wang Y, Mette M F, Miedaner T, Gottwald M, Wilde P, Rif J C, Zhao Y S. The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years. BMC Genom, 2014, 15: 556–567 [21]Reif J C, Zhao Y S, Wurschum T, Gowda M, Hahn V. Genomic selection of sunflower hybrid performance. Plant Breed, 2013, 132: 107–114 [22]Denis M, Bouvet J M. Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding. Tree Genet & Genom, 2013, 9: 37–51 [23]Desta Z A, Ortiz R. Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci, 2014, 19: 592–601 [24]Heslot N, Jannink J L, Sorrells M E. Perspective for genomic selection applications and research in plants. Crop Sci, 2015, 55: 1–12 [25]Schmutz J, Cannon S B, Schlueter J, Ma J X, Mitros T, Nelson W, Hyten D L, Song Q J, Thelen J J, Cheng J L, Xu D, Hellsten U, May G D, Yu Y S, Sakurai T, Umezawa T S, Bhattacharyya M K, Sandhu D, Valliyodan B, Lindquist E, Peto M, Grant D, Shu S Q, Goodstein D, Barry K, Griggs M F, Abernathy B, Du J C, Tian Z X, Zhu L C, Gill N, Joshi T, Libault M, Sethuraman A, Zhang X C, Shinozaki K, Nguyen H T, Wing R A, Cregan P, Specht J, Grimwood J, Rokhsar D, Stacey G, Shoemaker R C, Jachson S A. Genome sequence of the palaeoployploid soybean. Nature, 2010, 463: 178–183 [26]Lam H M, Xu X, Liu X, Chen W B, Yang G H, Wong F L, Li M W, He W M, Qin N, Wang B, Li J, Jian M, Wang J, Shao G H, Wang J, Sun S S, Zhang G Y. Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet, 2010,42: 1053–1059 [27]Li Y H, Zhou G Y, Ma J X, Jiang W K, Jin L G, Zhang Z H, Guo Y, Zhong J B, Sui Y, Zheng L T, Zhang S S, Zou Q Y, Shi X H, Li Y F, Zhang W K, Hu Y Y, Kong G Y, Hong H L, Tan B, Song J, Liu Z X, Wang Y S, Ruan H, Yeung C K, Liu J, Wang H L, Zhang L J, Guan R X, Wang K J, Li W B, Chen S Y, Chang R Z, Jiang Z, Jackson S A, Li R Q, Qiu L J. De novo assembly of soybean wild relatives for pan-genome analysis of diversity and agronomic traits. Nat Biotechnol, 2014, 32: 1045–1052 [28]Zhou Z K, Jiang Y, Wang Z, Gou Z H, Lyu J, Li W Y, Yu Y J, Shu L Q, Zhao Y J, Ma Y M, Fang C, Shen Y T, Liu T F, Li C C, Li Q, Wu M, Wang M, Wu Y S, Dong Y, Wan W T, Wang X, Ding Z L, Gao Y D, Xiang H, Zhu B G, Lee S H, Wang W, Tian Z X. Re-sequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol, 2015, 33: 408–414 [29]Song Q J, Hyten D L, Jia G F, Quigley C V, Fickus E W, Nelson R L, Cregan P B. Development and evaluation of SoySNP50K, a high-density genotyping array for soybean. PLoS One, 2013, 8: e54985 [30]邱丽娟, 常汝镇, 刘章雄, 关荣霞, 李英慧. 大豆种质资源描述规范和数据标准. 北京: 中国农业出版社, 2006. pp 18–24 Qiu L J, Chang R Z, Liu Z X, Guan R X, Li Y H. Descriptors and Data Standard for Soybean (Glycine spp.). Beijing: China Agriculture Press, 2015. pp 18–24 (in Chinese) [31]Fehr W R. Genetic contributions to yield gains of five major crop plants; proceedings of a symposium sponsored by Division C-1 of the Crop Science Society of America, in Atlanta, Georgia - ResearchGate, 1984. [32]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 [33]Bradbury P J, Zhang Z W, Kroon D E, Casstevens T M, Ramdoss Y, Buckler E S. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 2007, 23: 2633–2635 [34]文自翔, 赵团结, 郑永战, 刘顺湖, 王春娥, 王芳, 盖钧镒. 中国栽培和野生大豆农艺品质性状与SSR标记的关联分析: I. 群体结构及关联标记. 作物学报, 2008, 34: 1169–1178 Wen Z X, Zhao T J, Zheng Y Z, Liu S H, Wang C E, Wang F, Gai J Y. Association analysis of agronomic and quality traits with SSR markers in Glycine max and Glycine soja in china: I. Population structure and associated markers. Acta Agron Sin, 2008, 34: 1169–1178 (in Chinese with English abstract) [35]张军, 赵团结, 盖钧镒. 中国东北大豆育成品种遗传多样性和群体遗传结构分析. 作物学报, 2008, 34: 1529–1536 Zhang J, Zhao T J, Gai J Y. Genetic diversity and genetic structure of soybean cultivar population released in Northeast China. Acta Agron Sin, 2008, 34: 1529–1536 (in Chinese with English abstract) [36]范虎, 赵团结, 丁艳来, 邢光南, 盖钧镒. 中国野生大豆群体特征和地理分化的遗传分析. 中国农业科学, 2012, 45: 414–425 Fan H, Zhao T J, Ding Y L, Xing G N, Gai J Y. Genetic analysis of the characteristics and geographic differentiation of Chinese wild soybean population. Sci Agric Sin, 2012, 45: 414–425 (in Chinese with English abstract) [37]宋喜娥, 李英慧, 常汝镇, 郭平毅, 邱丽娟. 中国栽培大豆(Glycine max(L.) Merr.) 微核心种质的群体结构与遗传多样性. 中国农业科学, 2010, 43: 2209–2219 Song X E, Li Y H, Chang R Z, Guo P Y, Qiu L J. Population sturcture and genetic diversity of mini core collection of cultivated soybean (Glycine max(L.) Merr.) in China. Sci Agric Sin, 2010, 43: 2209–2219 (in Chinese with English abstract) [38]张军, 赵团结, 盖钧镒. 中国大豆育成品种群体遗传结构分化和亚群特异性分析. 中国农业科学, 2009, 42: 1901–1910 Zhang J, Zhao T J, Gai J Y. Analysis of genetic structure differentiation of released soybean cultivar population and specificity of subpopulations in China. Sci Agric Sin, 2009, 42: 1901–1910 (in Chinese with English abstract) [39]魏世平, 刘晓芬, 杨胜先, 吕海燕, 牛远, 章元明. 中国栽培大豆群体结构不同分类方法的比较. 南京农业大学学报, 2011, 34(2): 13–17 Wei S P, Liu X F, Yang S X, Lu H Y, Niu Y, Zhang Y M. Comparison of various clustering methods for population structure in Chinese cultivated soybean (Glycine max (L.) Merr.). J Nanjing Agric Univ, 2011, 34(2): 13–17 (in Chinese with English abstract) [40]黎裕, 李英慧, 杨庆文, 张锦鹏, 张金梅, 邱丽娟, 王天宇. 基于基因组学的作物种质资源研究: 现状与展望. 中国农业科学, 2015, 48: 3333–3353 Li Y, Li Y H, Yang Q W, Zhang J P, Zhang J M, Qiu L J, Wang T Y. Genomics-based crop germplasm research: advances and perspectives. Sci Agric Sin, 2015, 48: 3333–3353 (in Chinese with English abstract) [41]郭娟娟, 常汝镇, 章建新, 张巨松, 关荣霞, 邱丽娟. 日本大豆种质十胜长叶对我国大豆育成品种的遗传贡献分析. 大豆科学, 2007, 26: 807–819 Guo J J, Chang R Z, Zhang J X, Zhang J S, Guan R X, Qiu L J. Contribution of Japanese soybean germplasm TOKACHI-NAGAHA to Chinese soybean cultivars. Soybean Sci, 2007, 26: 807–819 (in Chinese with English abstract) [42]Toosi A, Fernando R L, Dekkers J C M. Genomic selection in admixed and crossbred populations. J Anim Sci, 2010, 88: 32–46 [43]Asoro F G, Newell M A, Beavis W D, Scott M P, Jannink J L. Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome,2011, 4: 132–144 [44]Guo Z G, Tucker D M, Basten C J, Gandhi H, Ersoz E, Guo B H, Xu Z Y, Wang D L, Gay G. The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet, 2014,127: 749–762 [45]Habier D, Fernando R L, Dekkers J C M. Impact of genetic relationship information on genome-assisted breeding values. Genetics, 2007, 177: 2389–2397 [46]Daetwyler H D, Wong R P, Villanueva B, Woolliams J A. The impact of genetic architecture on genome-wide evaluation methods. Genetics, 2010, 185: 1021–103
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