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作物学报 ›› 2023, Vol. 49 ›› Issue (3): 622-633.doi: 10.3724/SP.J.1006.2023.23024

所属专题： 玉米：遗传育种·种质资源·分子遗传学

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

利用F2:3家系来源单倍体定位玉米雄穗相关性状QTL及全基因组选择

许加波(), 吴鹏昊, 黄博文, 陈占辉, 马月虹, 任姣姣()   

  1. 新疆农业大学农学院, 新疆乌鲁木齐 830052
  • 收稿日期:2022-03-03 接受日期:2022-06-07 出版日期:2023-03-12 网络出版日期:2022-07-07
  • 通讯作者: 任姣姣
  • 作者简介:E-mail: 1142019044@qq.com
  • 基金资助:
    国家自然科学基金项目(U2003304);国家自然科学基金项目(32060484);国家自然科学基金项目(32001561);自治区天山青年优秀青年科技人才培养项目(2018Q019);新疆自然科学基金项目(2019D01A41);博士后面上项目(2018M643774);新疆主要作物生物育种创新工程(一)——玉米生物育种创新工程项目(2021A02001-2);新疆玉米绿色丰产提质增效技术优化集成及应用项目(2021B02002-2)

QTL locating and genomic selection for tassel-related traits using F2:3 lineage haploids

XU Jia-Bo(), WU Peng-Hao, HUANG Bo-Wen, CHEN Zhan-Hui, MA Yue-Hong, REN Jiao-Jiao()   

  1. College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
  • Received:2022-03-03 Accepted:2022-06-07 Published:2023-03-12 Published online:2022-07-07
  • Contact: REN Jiao-Jiao
  • Supported by:
    National Natural Science Foundation of China(U2003304);National Natural Science Foundation of China(32060484);National Natural Science Foundation of China(32001561);Scientific and Technological Talent Training Project for Excellent Youth in Tianshan Mountain of Autonomous Region(2018Q019);Natural Science Foundation of Xinjiang(2019D01A41);Postdoctoral Project(2018M643774);Xinjiang Main Crop Biological Breeding Innovation Project (I)—Maize Biological Breeding Innovation Project(2021A02001-2);Xinjiang Maize Green High Yield, Quality and Efficiency Technology Optimization Integration and Application Funding(2021B02002-2)

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摘要：

雄穗大小影响玉米光合作用合成的养分分配, 进而影响雌穗发育以及由此决定的穗行数、行粒数、结实率、百粒重等产量构成因素。本研究用优良自交系郑58和B73构建的F2:3家系诱导单倍体, 通过48K液相杂交探针捕获技术获得基因型, 结合多环境单倍体表型数据, 对雄穗相关性状采用完备区间作图法(inclusive composite interval mapping, ICIM)进行QTL (quantitative trait locus)定位, 采用(ridge regression best linear unbiased prediction, RRBLUP)模型探索全基因组选择中训练群体大小及SNP标记数目对预测精度的影响。结果表明, 雄穗主轴长、一级分枝数、二级分枝数和总分枝数遗传力分别为0.82、0.88、0.84和0.88。雄穗主轴长检测到2个QTL, 分别位于bin1.03和bin4.09, 表型贡献率为6.02%和11.10%。一级分枝数检测到2个QTL, 分别位于bin1.05和bin4.05, 表型贡献率为9.17%和11.75%。二级分枝检测到2个QTL, 分别位于bin2.03和bin3.06, 表型贡献率为5.51%和5.65%。总分枝数检测到2个QTL, 分别位于bin1.04和bin4.05, 表型贡献率为9.37%和10.83%。其中, 一级分枝数和总分枝数在bin4.05定位到了一个相同位点, 一因多效。全基因组选择五倍交叉验证的预测精度分别为0.36、0.41、0.28、0.38。当训练群体达到总群体60%时, 标记密度达到500个时, 即可以得到较高的预测精度。

关键词: 玉米, 单倍体, 雄穗, QTL, 全基因组选择

Abstract:

Tassel size affects the nutrient allocation synthesized by photosynthesis in maize, which in turn affects ears development and the yield components, such as kernel rows, kernel number per row, seeding rate, and kernels weight per one hundred. In this study, haploids were induced using the F2:3 families, which were constructed from the elite inbred lines Zheng 58 and B73. Genotypes were obtained by 48K liquid phase hybridization capture probes technique, and phenotypes were evaluated in multi-environment trails. QTL mapping was performed using the inclusive composite interval mapping (ICIM) method. The RRBLUP model was used for genomic selection to explore the effects of training population size and the number of SNP markers on prediction accuracy. The results showed that the heritabilities of tassel length, tassel primary branch number, tassel secondary branch number, and tassel branch number were 0.82, 0.88, 0.84, and 0.88, respectively. Two QTL, located in bins 1.03 and 4.09, were detected for tassel length with phenotypic variation explained (PVE) of 6.02% and 11.10%, respectively. Two QTL, located in bins 1.05 and 4.05, were detected for tassel primary branch with PVE of 9.17% and 11.75%, respectively. Two QTL, located in bins 2.03 and 3.06, were detected for the tassel secondary branch with PVE of 5.51% and 5.65%, respectively. Two QTLs, located in bins 1.04 and 4.05, were detected for the tassel branch number with PVE of 9.37% and 10.83%, respectively. Tassel primary branch number and tassel branch number identified a same QTL located in bin 4.05 with multiple effects. The prediction accuracy of five-fold cross-validation for genomic selection was 0.36, 0.41, 0.28, and 0.37, respectively. When the training population reached 60% of the total population and the marker density reached 500, a high prediction accuracy could be obtained.

Key words: maize (Zea mays L.), haploid, tassel, QTLs, genomic selection

表1

玉米单倍体雄穗相关性状测定方法"

性状
Trait		 测定方法
Assessment
雄穗主轴长
Tassel length		 测量雄穗最底部分枝的基部至雄穗顶端的长度[17-18]。
Measure the length from the base of the lowest branch of the tassel to the tip of the tassel[17-18].
雄穗一级分枝数
Tassel primary branch number		 对基部与雄穗主茎直接相连的分枝进行计数[7]。
The branches whose base is directly connected to the main stem of the tassel were counted[7].
雄穗二级分枝数
Tassel secondary branch number		 对基部与一级分枝直接相连的分枝进行计数。
Counting of branches whose base is directly connected to the primary branches.
雄穗总分枝数
Tassel branch number		 计算雄穗一级分枝和雄穗二级分枝的总和[11,17]。
Calculate the sum of the tassel primary branche number and the tassel secondary branche number[11,17].

表2

玉米单倍体雄穗相关性状描述性统计"

性状
Trait		 均值
Mean		 最小值
Min.		 最大值
Max.		 偏度
Skewness		 峰度
Kurtosis		 变异系数
CV
雄穗主轴长Tassel length (cm) 18.52 17.33 19.90 0.12 -0.57 0.03
雄穗一级分枝数Tassel primary branch number 5.85 3.79 8.32 0.16 -0.22 0.14
雄穗二级分枝数Tassel secondary branch number 0.79 0.50 1.23 0.48 -0.14 0.19
雄穗总分枝数Tassel branch number 6.64 4.33 9.51 0.17 -0.22 0.15

表3

玉米单倍体雄穗相关性状的方差及广义遗传力分析"

性状
Trait		 方差来源
Source of variances		 自由度
DF		 均方
Mean of square		 F值
F-value		 P 值
P-value		 遗传力
H2
雄穗主轴长
Tassel length		 基因型Genotype (G) 199 5.27 2.56 <0.01** 0.82
环境Environment (E) 2 8.04 3.90 0.02*
基因×环境型
Genotype × environment (G×E)		 304 2.38 1.16 0.08
残差Error 506 2.06
雄穗一级分枝数
Tassel primary branch number		 基因型G 199 7.48 4.35 <0.01** 0.88
环境E 2 72.74 42.29 <0.01**
基因×环境型G×E 304 2.22 1.29 <0.01**
残差Error 506 1.72
雄穗二级分枝数
Tassel secondary branch number		 基因型G 199 0.35 2.67 <0.01** 0.84
环境E 2 6.71 51.14 <0.01**
基因×环境型G×E 304 0.14 1.06 0.28
残差Error 506 0.13
雄穗总分枝数
Tassel branch number		 基因型G 199 9.72 4.21 <0.01** 0.88
环境E 2 120.89 52.42 <0.01**
基因×环境型G×E 304 2.83 1.23 0.02*
残差Error 506 2.31

图1

玉米单倍体雄穗相关性状的相关性分析 *在0.05水平上差异显著; **在0.01水平上差异显著; ***在0.001水平上差异显著。圆形由小到大代表相关系数由低到高。"

图2

玉米单倍体群体雄穗相关性状的QTL定位"

表4

3个环境条件下单倍体雄穗相关性状的QTL定位"

性状
Trait		 QTL 染色体/bin
Chr./bin		 遗传位置
Genetic position (cM)		 物理距离a
Physical position (Mb)a		 LOD值
LOD-value		 表型贡献率
PVE (%)		 加性效应
Additive		 显性效应
Dominant		 基因效应b
Gene actionb		 参考文献
Reference
雄穗主轴长
Tassel length		 qTL1-1 1/1.03 34 32.50-32.53 3.01 6.02 0.05 −0.25 OD 贾波等[24] Jia B, et al.[24]
qTL4-1 4/4.09 69 223.30-224.37 5.40 11.10 0.27 −0.04 A
雄穗一级分枝数
Tassel primary branch number		 qTPBN1-1 1/1.05 61 102.35-105.19 4.98 9.17 0.37 −0.20 PD 杨钊钊等[6] Yang Z Z, et al.[6]
qTPBN4-1 4/4.05 31 75.57-79.81 6.29 11.75 −0.42 0.20 PD
雄穗二级分枝数
Tassel secondary branch number		 qTSBN2-1 2/2.03 35 26.29-27.50 2.69 5.51 −0.06 −0.01 A
qTSBN3-1 3/3.06 47 185.21-185.33 2.78 5.65 0.06 0.01 A 张先创[8] Zhang X C[8]
雄穗总分枝数
Tassel branch number		 qTBN1-1 1/1.04 46 56.92-57.40 5.11 9.37 0.42 −0.28 PD 张先创[8]、Upadyayula等[25]
Zhang X C[8]; Upadyayula, et al.[25]
qTBN4-1 4/4.05 31 75.57-79.81 5.80 10.83 −0.46 0.22 PD

图3

玉米单倍体群体雄穗相关性状全基因组选择预测精度"

图4

玉米单倍体不同训练群体大小雄穗相关性状全基因组选择预测精度 A: 雄穗主轴长; B: 雄穗一级分枝数; C: 雄穗二级分枝数; D: 雄穗总分枝数。"

图5

玉米单倍体群体不同SNP个数雄穗相关性状全基因组选择预测精度 A: 雄穗主轴长; B: 雄穗一级分枝数; C: 雄穗二级分枝数; D: 雄穗总分枝数。"

[1] Lambert R J, Johnson R R. Leaf angle, tassel morphology, and the performance of maize hybrids 1. Crop Sci, 1978, 18: 499-502.
doi: 10.2135/cropsci1978.0011183X001800030037x
[2] 王赛, 王宇宇, 王石磊, 徐梦真, 邹欢, 侯清桂, 毛棣, 田磊, 陈彦惠, 吴连成. 基于SNP遗传图谱定位玉米雄穗分枝数和主轴长QTLs. 河南农业大学学报, 2019, 53: 671-676.
Wang S, Wang Y Y, Wang S L, Xu M Z, Zou H, Hou Q G, Mao D, Tian L, Chen Y H, Wu L C. QTLs mapping of tassel branch number and tassel total length in maize based on SNP genetic map. J Henan Agric Univ, 2019, 53: 671-676. (in Chinese with English abstract)
[3] Wartha C A, Cargnelutti Filho A, Lúcio A D, Follmann D N, Kleinpaul J A, Simões F M. Sample sizes to estimate mean values for tassel traits in maize genotypes. Genet Mol Res, 2016, 15: gmr15049151.
[4] Qin X, Tian S, Zhang W, Dong X, Ma C, Wang Y, Yan J, Yue B. Q Dtbn1, an F-box gene affecting maize tassel branch number by a dominant model. Plant Biotechnol J, 2021, 19: 1183-1194.
doi: 10.1111/pbi.13540
[5] 申涛, 谭康, 李春红, 杨梅, 胡小兰, 蒋滔, 张志, 邱红波. 玉米株型相关性状的QTL定位. 分子植物育种, 2022, 20: 155-162.
Shen T, Tan K, Li C H, Yang M, Hu X L, Jiang T, Zhang Z, Qiu H B. QTL mapping for plant type related traits in maize. Mol Plant Breed, 2022, 20: 155-162. (in Chinese with English abstract)
[6] 杨钊钊, 李永祥, 刘成, 刘志斋, 李春辉, 李清超, 彭勃, 张岩, 王迪, 谭巍巍, 孙宝成, 石云素, 宋燕春, 王天宇, 黎裕. 基于多个相关群体的玉米雄穗相关性状QTL分析. 作物学报, 2012, 38: 1435-1442.
doi: 10.3724/SP.J.1006.2012.01435
Yang Z Z, Li Y X, Liu C, Liu Z Z, Li C H, Li Q C, Peng B, Zhang Y, Wang D, Tan W W, Sun B C, Shi Y S, Song Y C, Wang T Y, Li Y. QTL analysis of tassel-related traits in maize (Zea mays L.) using multiple connected populations. Acta Agron Sin, 2012, 38: 1435-1442. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2012.01435
[7] 高世斌, 赵茂俊, 兰海, 张志明. 玉米雄穗分枝数与主轴长的QTL鉴定. 遗传, 2007, 29: 1013-1017.
Gao S B, Zhao M J, Lan H, Zhang C M. Identification of QTL associated with tassel branch number and total tassel length in maize. Hereditas, 2007, 29: 1013-1017. (in Chinese with English abstract)
[8] 张先创. 玉米雄穗相关性状的QTL定位. 西南大学硕士学位论文, 重庆, 2020.
Zhang X C. QTL Mapping of Tassel Related Traits in Maize (Zea mays L.). MS Thesis of Southwest University, Chongqing, China, 2020. (in Chinese with English abstract)
[9] Wu X, Guo X Y, Wang A G, Liu P F, Wu W Q, Zhao Q, Zhao M Y, Zhu Y F, Chen Z H. Quantitative trait loci mapping of plant architecture-related traits using the high-throughput genotyping by sequencing method. Euphytica, 2019, 215: 212.
doi: 10.1007/s10681-019-2535-x
[10] Upadyayula N, Wassom J, Bohn M O, Rocheford T R. Quantitative trait loci analysis of phenotypic traits and principal components of maize tassel inflorescence architecture. Theor Appl Genet, 2006, 113: 1395-1407.
pmid: 17061102
[11] Mickelson S M, Stuber C S, Senior L, Kaeppleret S M. Quantitative trait loci controlling leaf and tassel traits in a B73×Mo17 population of maize. Crop Sci, 2002, 42: 1902-1909.
doi: 10.2135/cropsci2002.1902
[12] Liu X, Hao L, Kou S, Su E, Zhou Y, Wang R, Mohamed A, Gao C, Zhang D, Li Y, Li H, Song Y, Shi Y, Wang T, Li Y. High-density quantitative trait locus mapping revealed genetic architecture of leaf angle and tassel size in maize. Mol Breed, 2019, 39: 7.
doi: 10.1007/s11032-018-0914-y
[13] Olatoye M O, Clark L V, Wang J P, Yang X P, Yamada T, Sacks E L, Lipka A E. Evaluation of genomic selection and marker- assisted selection in Miscanthus and energy cane. Mol Breed, 2019, 39: 1-16.
doi: 10.1007/s11032-018-0907-x
[14] 刘策, 孟焕文, 程智慧. 植物全基因组选择育种技术原理与研究进展. 分子植物育种, 2020, 18: 5335-5342.
Liu C, Meng H W, Cheng Z H. Plant genome-wide selection breeding technical principle and research progress. Mol Plant Breed, 2020, 18: 5335-5342. (in Chinese with English abstract)
[15] Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger A E. Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet, 2012, 44: 217-220.
doi: 10.1038/ng.1033 pmid: 22246502
[16] Liu X G, Wang H W, Wang H, Guo Z F, Xu X J, Liu J C, Wang S H, Li W X, Zou C, Prasanna B M, Olsen M S, Huang C L, Xu Y B. Factors affecting genomic selection revealed by empirical evidence in maize. Crop J, 2018, 6: 341-352.
doi: 10.1016/j.cj.2018.03.005
[17] Wu X, Li Y X, Shi Y S, Song Y C, Zhang D F, Li C H, Buckler E S, Li Y, Zhang Z W, Wang T Y. Joint-linkage mapping and GWAS reveal extensive genetic loci that regulate male inflorescence size in maize. Plant Biotechnol J, 2016, 14: 1551-1562.
doi: 10.1111/pbi.12519 pmid: 26801971
[18] Brown P J, Upadyayula N, Mahone G S, Tian F, Bradbury P J, Myles S, Holland J B, Flint-Garcia S, McMullen M D, Buckler E S, Rocheford T R. Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet, 2011, 7: e1002383.
[19] Song W B, Wang B B, Hauck A L, Dong X M, Li J P, Lai J S. Genetic dissection of maize seedling root system architecture traits using an ultra-high density bin-map and a recombinant inbred line population. J Integr Plant Biol, 2016, 58: 266-279.
doi: 10.1111/jipb.12452
[20] Stuber C W, Edwards M D A, Wendel J F. Molecular marker-facilitated investigations of quantitative trait loci in maize. II: Factors influencing yield and its component traits. Crop Sci, 1987, 27: 639-648.
doi: 10.2135/cropsci1987.0011183X002700040006x
[21] Endelman J B, Atlin G N, Beyene Y, Semagn K, Zhang X C, Sorrells M E, Jannink J L. Optimal design of preliminary yield trials with genome-wide markers. Crop Sci, 2014, 54: 48-59.
doi: 10.2135/cropsci2013.03.0154
[22] 李宗泽, 徐晓明, 孙强, 杨彩霞, 许加波, 吴鹏昊. 玉米穗轴长与穗轴粗的QTL定位及全基因组预测. 中国农业大学学报, 2022, 27(4): 44-52.
Li Z Z, Xu X M, Sun Q, Yang C X, Xu J B, Wu P H. QTL mapping and genomic selection of cob length and diameter in maize. J China Agric Univ, 2022, 27(4): 44-52. (in Chinese with English abstract)
[23] Cao S L, Loladze A, Yuan Y B, Wu Y S, Zhang A, Chen J F, Gordon H, Cao J S, Chaikam V, Olsen M, Prasanna B M, San V, Zhang X C. Genome-wide analysis of tar spot complex resistance in maize using genotyping-by-sequencing SNPs and whole-genome prediction. Plant Genome, 2017, 10. doi: 10.3835/plantgenome2016.10.0099.
doi: 10.3835/plantgenome2016.10.0099
[24] 贾波, 崔敏, 谢庆春, 严卫古, 印志同. 基于SNP标记的玉米雄穗主要性状QTL定位分析. 西南农业学报, 2019, 32: 1469-1473.
Jia B, Cui M, Xie Q C, Yan W G, Yin Z T. QTL analysis of tassel traits based on SNP markers in maize. Southwest China J Agric Sci, 2019, 32: 1469-1473. (in Chinese with English abstract)
[25] Upadyayula N, Da Silva H S, Bohn M O, Rocheford T R. Genetic and QTL analysis of maize tassel and ear inflorescence architecture. Theor Appl Genet, 2006, 112: 592-606.
doi: 10.1007/s00122-005-0133-x pmid: 16395569
[26] Wang B B, Liu H, Liu Z P, Dong X M, Guo J J, Li W, Chen J, Gao C, Zhu Y B, Zheng X M, Chen Z L, Chen J, Song W B, Hauck A, Lai J S. Identification of minor effect QTLs for plant architecture related traits using super high density genotyping and large recombinant inbred population in maize (Zea mays). BMC Plant Biol, 2018, 18: 17.
doi: 10.1186/s12870-018-1233-5 pmid: 29347909
[27] Yang W F, Zheng L Z, He Y, Zhu L Y, Chen X Q, Tao Y S. Fine mapping and candidate gene prediction of a major quantitative trait locus for tassel branch number in maize. Gene, 2020, 757: 144928.
doi: 10.1016/j.gene.2020.144928
[28] Xu G H, Wang X F, Huang C, Xu D Y, Li D, Tian J G, Chen Q Y, Wang C L, Liang Y M, Wu Y Y, Yang X H, Tian F. Complex genetic architecture underlies maize tassel domestication. New Phytol, 2017, 214: 852-864.
doi: 10.1111/nph.14400 pmid: 28067953
[29] Chen Z L, Wang B B, Dong X M, Liu H, Ren L H, Chen J, Hauck A, Song W B, Lai J S. An ultra-high density bin-map for rapid QTL mapping for tassel and ear architecture in a large F2 maize population. BMC Genomics, 2014, 15: 433.
doi: 10.1186/1471-2164-15-433
[30] 邵元健. 质量性状和数量性状含义的辨析. 生物学杂志, 2006, (4): 55-57.
Shao Y J. Discrimination of the meaning of qualitative and quantitative traits. J Biol, 2006, (4): 55-57. (in Chinese)
[31] Cerrudo D, Cao S L, Yuan Y B, Martinez C, Suarez E A, Babu R, Zhang X C, Trachsel S. Genomic selection outperforms marker assisted selection for grain yield and physiological traits in a maize doubled haploid population across water treatments. Front Plant Sci, 2018, 9: 366.
doi: 10.3389/fpls.2018.00366 pmid: 29616072
[32] Lorenzana R E, Bernardo R. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet, 2009, 120: 151-161.
doi: 10.1007/s00122-009-1166-3 pmid: 19841887
[33] Amini F, Franco F R, Hu G P, Wang L Z. The look ahead trace back optimizer for genomic selection under transparent and opaque simulators. Sci Rep, 2021, 11: 4124.
doi: 10.1038/s41598-021-83567-5 pmid: 33602979
[34] Guo R, Dhliwayo T, Mageto E K, Rajas N P, Lee M, Yu D S, Ruan Y Y, Zhang A, Vicente F S, Olsen M, Crossa J, Prasanna B M, Zhang L J, Zhang X C. Genomic prediction of kernel zinc concentration in multiple maize populations using genotyping-by-sequencing and repeat amplification sequencing markers. Front Plant Sci, 2020, 11: 534.
doi: 10.3389/fpls.2020.00534 pmid: 32457778
[35] Cao S L, Song J Q, Yuan Y B, Zhang A, Ren J J, Liu Y B, Qu G H, Zhang J G, Wang C P, Cao J S, Olsen M S, Boddupalli P, Vicente F S, Zhang X C. Genomic prediction of resistance to tar spot complex of maize in multiple populations using genotyping-by-sequencing SNPs. Front Plant Sci, 2021, 12: 1438.
[36] Ren J J, Li Z M, Wu P H, Zhang A, Liu Y B, Hu G H, Cao S L, Qu J T, Dhliwayo T, Zheng H J, Olsen M S, Boddupalli P, Vicente F S, Zhang X C. Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined genome-wide association study, linkage mapping, and genomic prediction. Front Plant Sci, 2021, 12: 1338.
[37] Liu X G, Wang H W, Hu X J, Li K, Liu Z F, Wu Y J, Huang C L. Improving genomic selection with quantitative trait loci and nonadditive effects revealed by empirical evidence in maize. Front Plant Sci, 2019, 10: 1129.
doi: 10.3389/fpls.2019.01129 pmid: 31620155
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