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作物学报 ›› 2024, Vol. 50 ›› Issue (2): 373-382.doi: 10.3724/SP.J.1006.2024.33021

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于多维组学数据的玉米农艺和品质性状预测研究

杨静蕾1,**(), 吴冰杰1,**(), 王安洲1, 肖英杰1,2,*()   

  1. 1作物遗传改良全国重点实验室 / 华中农业大学, 湖北武汉 430070
    2湖北洪山实验室, 湖北武汉 430070
  • 收稿日期:2023-04-02 接受日期:2023-09-13 出版日期:2024-02-12 网络出版日期:2023-10-09
  • 通讯作者: *肖英杰, E-mail: yxiao25@mail.hzau.edu.cn
  • 作者简介:杨静蕾, E-mail: yangjl@webmail.hzau.edu.cn;吴冰杰, E-mail: wubingjie@webmail.hzau.edu.cn
    **同等贡献
  • 基金资助:
    国家自然科学基金优秀青年科学基金项目(32122066)

Genomic prediction of maize agronomic and quality traits using multi-omics data

YANG Jing-Lei1,**(), WU Bing-Jie1,**(), WANG An-Zhou1, XIAO Ying-Jie1,2,*()   

  1. 1National Key Laboratory of Crop Genetic Improvement / Huazhong Agricultural University, Wuhan 430070, Hubei, China
    2Hubei Hongshan Laboratory, Wuhan 430070, Hubei, China
  • Received:2023-04-02 Accepted:2023-09-13 Published:2024-02-12 Published online:2023-10-09
  • Contact: *E-mail: yxiao25@mail.hzau.edu.cn
  • About author:**Contributed equally to this work
  • Supported by:
    National Natural Science Foundation of China for Youth-Talent Fund(32122066)

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摘要：

基因组选择是利用覆盖基因组的高密度标记对未知表型进行预测并选择的技术。在植物中, 利用该技术可对不同作物性状进行早期选择, 保留优势个体, 节约田间管理和表型鉴定成本, 大大加快育种进程。本研究使用rrBLUP和LASSO两种统计模型, 基于基因组、转录组和代谢组数据对玉米的农艺性状和品质性状进行了基因组预测。研究发现, 对于不同组学数据而言, 其预测能力高低依次为基因组、转录组、代谢组数据。对于不同性状而言, 品质性状的预测能力高于农艺性状。对于rrBLUP和LASSO两种模型而言, 基于基因组数据预测时所有性状均是rrBLUP为最优预测模型; 基于转录组数据预测时有53种性状是以rrBLUP为最佳预测模型, 2种性状以LASSO为最佳预测模型; 基于代谢组数据, 有43种性状以rrBLUP为最佳预测模型, 12种性状以LASSO为最佳预测模型。此外, 还发现用不同系谱材料进行预测时, 热带玉米预测温带玉米, 其效果略优于温带玉米预测热带玉米。而对于品质性状, 不同系谱间材料的预测精度高于同一系谱内。本研究系统评估了各种组学数据和不同统计模型对玉米农艺及品质性状预测能力的差异, 为未来玉米重要性状的基因组育种提供了理论依据。

关键词: 玉米, 农艺和品质性状, 基因组预测, 多维组学数据

Abstract:

Genomic selection predicts unknown phenotypes by using high-density genetic markers covering the genome. In the plant, this method allows early selection for traits, retaining dominant individuals and saving costs for field management and phenotype identification, which greatly accelerating the breeding process. In this study, genomic, transcriptomic, and metabolomic data were used for genomic prediction of agronomic and quality traits of maize by using two statistical models, rrBLUP, and LASSO. We found that the order of predictive power was genomic data, transcriptomic data, and metabolomic data. For different traits, genomic prediction was more powerful than agronomic traits for quality traits. For both rrBLUP and LASSO models, rrBLUP was the best model for all traits when using genomic data, 53 traits were the best predicted by rrBLUP and 2 traits were the best predicted by LASSO when using transcriptomic data, 43 traits were the best predicted by rrBLUP and 12 traits were the best predicted by LASSO, and 12 traits were the best predicted by LASSO based on metabolomic data. In addition, when performing genomic prediction using different lineages, the accuracy of predicting the temperate maize from the tropic maize was slightly better than that of predicting the tropic maize from the temperate. For quality traits, we found the cross-lineage prediction was higher than the within-lineage prediction. This study systematically evaluated the differences in the predictive ability of maize agronomic and quality traits based on various multi-omics data and statistical models, which providing a theoretical basis for future genomic breeding of important agricultural traits in maize.

Key words: maize, agronomic and quality trait, genomic prediction, multi-omics data

图1

基于基因组数据分析对农艺性状和品质性状的预测差异 A: 基于基因组数据预测2种表型性状的预测精度密度分布图; B: 基于基因组数据预测20个农艺性状和35个品质性状的预测精度。"

图2

不同组学数据对性状预测的差异 A: 对比不同组学数据预测不同类型性状的预测差异; B: 不同组学数据预测55个性状的预测结果分类; C: 应用不同染色体标记预测2种性状的预测差异; D: 利用3种组织数据预测2种性状的预测差异; E: 整合不同环境的代谢物数据预测2种性状的预测差异。"

图3

不同模型和数据组合对性状预测的整合评估 A: 基于不同组学数据55个性状的最佳预测模型; B: 基于不同组学数据55个性状在2个预测模型间预测精度变化; C: 55个性状的最佳预测数据和模型组合。"

图4

材料系谱差异对不同组学预测的影响 A, B: 基于不同组学数据对比材料系谱差异对预测表型结果的影响; C~E: 基于不同组学数据对比材料异质性对2种类型性状预测的影响。"

[1] He T H, Li C D. Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. Crop J, 2020, 8: 688-700.
doi: 10.1016/j.cj.2020.04.005
[2] Steinwand M A, Ronald P C. Crop biotechnology and the future of food. Nat Food, 2020, 1: 273-283.
doi: 10.1038/s43016-020-0072-3
[3] Hickey L T, Hafeez A N, Robinson H, Jackson S A, Leal-Bertioli S C M, Tester M, Gao C X, Godwin I D, Hayes Ben J, Wulff B B H. Breeding crops to feed 10 billion. Nat Biotechnol, 2019, 37: 744-754.
doi: 10.1038/s41587-019-0152-9 pmid: 31209375
[4] Borlaug N E. Contributions of conventional plant breeding to food production. Science, 1983, 219: 689-693.
doi: 10.1126/science.219.4585.689 pmid: 17814030
[5] Watson A, Ghosh S, Williams M J, Cubby W S, Simmonds J, Rey M D, Asyraf Md Hatta M, Hinchliffe A, Steed A, Reynolds D, Adamski N M, Breakspear A, Korolev A, Rayner T, Dixon L E, Riaz A, Martin W, Ryan M, Edwards D, Batley J, Raman H, Carter J, Rogers C, Domoney C, Moore G, Harwood W, Nicholson P, Dieters M J, DeLacy I H, Zhou J, Uauy C, Boden S A, Park R F, Wulff B B H, Hickey L T. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants, 2018, 4: 23-29.
doi: 10.1038/s41477-017-0083-8 pmid: 29292376
[6] Meuwissen T H E, Hayes B J, Goddard M E. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157: 1819-1829.
doi: 10.1093/genetics/157.4.1819 pmid: 11290733
[7] Nakaya A, Isobe S N. Will genomic selection be a practical method for plant breeding? Ann Bot, 2012, 110: 1303-1316.
doi: 10.1093/aob/mcs109
[8] Farah M M, Swan A A, Fortes M R S, Fonseca R, Moore S S, Kelly M J. Accuracy of genomic selection for age at puberty in a multi-breed population of tropically adapted beef cattle. Anim Genet, 2016, 47: 3-11.
doi: 10.1111/age.12362 pmid: 26490440
[9] Kariuki C M, Brascamp E W, Komen H, Van Arendonk J A M. Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries. J Dairy Sci, 2017, 100: 2258-2268.
doi: S0022-0302(17)30052-8 pmid: 28109609
[10] Schaeffer L R. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet, 2006, 123: 218-223.
doi: 10.1111/j.1439-0388.2006.00595.x pmid: 16882088
[11] 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
[12] Zhang X C, Perez-Rodriguez P, Burgueno J, Olsen M, Buckler E, Atlin G, Prasanna B M, Vargas M, San Vicente F, Crossa J. Rapid cycling genomic selection in a multi-parental tropical maize population. G3: Gene Genome Genet, 2017, 7: 2315-2326.
doi: 10.1534/g3.117.043141
[13] Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z. Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity, 2017, 118: 302-310.
doi: 10.1038/hdy.2016.87 pmid: 27649618
[14] Ma Y S, Liu Z X, Wen Z X, Wei S H, Yang C M, Wang H C, Yang C Y, Lu W G, Xu R, Zhang W H, Wu J A, Hu G H, Luan X Y, Fu Y S, Wang S M, Han T F, Zhang M C, Zhang L, Yuan B, Guo Y, Reif J C, Jiang Y, Li W B, Wang D C, Qiu L J. Effect of population structure on prediction accuracy of soybean 100-seed weight by genomic selection. Crop J, 2018, 44: 43-52 (in Chinese with English abstract).
[15] Charmet G, Storlie E, Oury F X, Laurent V, Beghin D, Chevarin L, Lapierre A, Perretant M R, Rolland B, Heumez E, Duchalais L, Goudemand E, Bordes J, Robert O. Genome-wide prediction of three important traits in bread wheat. Mol Breed, 2014, 34: 1843-1852.
doi: 10.1007/s11032-014-0143-y
[16] Beyene Y, Semagn K, Mugo S, Tarekegne A, Babu R, Meisel B, Sehabiague P, Makumbi D, Magorokosho C, Oikeh S, Gakunga J, Vargas M, Olsen M, Prasanna B M, Banziger M, Crossa J. Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci, 2015, 55: 154-163.
doi: 10.2135/cropsci2014.07.0460
[17] Zhang X, Perez-Rodriguez P, Semagn K, Beyene Y, Babu R, Lopez-Cruz M A, Vicente F S, Olsen M, Buckler E, Jannink J L, Prasanna B M, Crossa J. Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity, 2015, 114: 291-299.
doi: 10.1038/hdy.2014.99 pmid: 25407079
[18] Cao S, Loladze A, Yuan Y B, Wu Y S, Zhang A, Chen J F, Huestis G, Cao J S, Chaikam V, Olsen M, Prasanna B M, San Vicente F, 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: 1-14.
[19] Zhang A, Wang H W, Beyene Y, Semagn K, Liu Y, B Cao S L, Cui Z H, Ruan Y Y, Burgueno J, San Vicente F, Olsen M, Prasanna B M, Crossa J, Yu H Q, Zhang X C. Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Front Plant Sci, 2017, 8: 1916.
doi: 10.3389/fpls.2017.01916 pmid: 29167677
[20] Guo Z G, Tucker D M, Lu J W, Kishore V, Gay G. Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet, 2012, 124: 261-275.
doi: 10.1007/s00122-011-1702-9 pmid: 21938474
[21] 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
[22] Shikha M, Kanika A, Rao A R, Mallikarjuna M G, Gupta H S, Nepolean T. Genomic selection for drought tolerance using genome-wide SNPs in maize. Front Plant Sci, 2017, 8: 550.
doi: 10.3389/fpls.2017.00550 pmid: 28484471
[23] Crossa J, Perez-Rodriguez P, Cuevas J, Montesinos-Lopez O, Jarquín D, de los Campos G, Burgueno J, Camacho-Gonzalez J M, Perez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X C, Gowda M, Roorkiwal M, Rutkoski J, Varshney R K. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci, 2017, 22: 961-975.
doi: S1360-1385(17)30184-X pmid: 28965742
[24] Azodi C B, Pardo J, VanBuren R, De Los Campos G, Shiu S H. Transcriptome-based prediction of complex traits in maize. Plant Cell, 2020, 32: 139-151.
doi: 10.1105/tpc.19.00332
[25] Bhering L L, Junqueira V S, Peixoto L A, Cruz C D, Laviola B G. Comparison of methods used to identify superior individuals in genomic selection in plant breeding. Genet Mol Res, 2015, 14: 10888-10896.
doi: 10.4238/2015.September.9.26 pmid: 26400316
[26] Yan H L, Guo H Y, Xu W X, Dai C H, Kimani W, Xie J Y, Zhang H F, Li T, Wang F, Yu Y J, Ma M, Hao Z F, He Z Y. GWAS-assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods. J Hazard Mater, 2023, 441: 129929.
doi: 10.1016/j.jhazmat.2022.129929
[27] Tibshirani R. Regression shrinkage and selection via the lasso. J Roy Stat Soc B, 1996, 58: 267-288.
[28] Ahmad I, Singh A, Fahad M, Waqas M M. Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Comput Electron Agric, 2020, 178: 105732-105732.
doi: 10.1016/j.compag.2020.105732
[29] Islam M S, Fang D D, Jenkins J N, Guo J, McCarty J C, Jones D C. Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton. Mol Genet Genom, 2020, 295: 67-79.
doi: 10.1007/s00438-019-01599-z
[30] Tsai H Y, Janss L L, Andersen J R, Orabi J, Jensen J D, Jahoor A, Jensen J. Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat. Sci Rep, 2020, 10: 1-15.
doi: 10.1038/s41598-019-56847-4
[31] Zhang F, Wu J F, Sade N, Wu S, Egbaria A, Fernie A R, Yan J B, Qin F, Wei C, Brotman Y, Dai M Q. Genomic basis underlying the metabolome-mediated drought adaptation of maize. Genome Biol, 2021, 22: 260-260.
doi: 10.1186/s13059-021-02481-1 pmid: 34488839
[32] Qin S, Xu Y, Nie Z, Liu H, Gao W, Li C, Zhao P. Metabolomic and antioxidant enzyme activity changes in response to cadmium stress under boron application of wheat (Triticum aestivum). Environ Sci Pollut Res Int, 2022, 29: 34701-34713.
doi: 10.1007/s11356-021-17123-z
[33] Hu X H, Xie W B, Wu C C, Xu S Z. A directed learning strategy integrating multiple omic data improves genomic prediction. Plant Biotechnol J, 2019, 17: 2011-2020.
doi: 10.1111/pbi.13117 pmid: 30950198
[34] Li H, Peng Z Y, Yang X H, Wang W D, Fu J J, Wang J H, Han Y J, Chai Y C, Guo T T, Yang N, Liu J, Warburton M L, Cheng Y B, Hao X M, Zhang P, Zhao J Y, Liu Y J, Wang G Y, Li J S, Yan J B. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet, 2013, 45: 43-50.
doi: 10.1038/ng.2484 pmid: 23242369
[35] Yang N, Lu Y L, Yang X H, Huang J, Zhou Y, Ali F, Wen W W, Liu J, Li J S, Yan J B. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet, 2014, 10: e1004573.
doi: 10.1371/journal.pgen.1004573
[36] Wang H, Xu S T, Fan Y M, Liu N N, Zhan W, Liu H J, Xiao Y J, Li K, Pan Q C, Li W Q, Deng M, Liu J, Jin M, Yang X H, Li J S, Li Q, Yan J B. Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses. Plant Biotechnol J, 2018, 16: 1464-1475.
doi: 10.1111/pbi.12889 pmid: 29356296
[37] Deng M, Li D Q, Luo J Y, Xiao Y J, Liu H J, Pan Q C, Zhang X H, Jin M L, Zhao M C, Yan J B. The genetic architecture of amino acids dissection by association and linkage analysis in maize. Plant Biotechnol J, 2017, 15: 1250-1263.
doi: 10.1111/pbi.12712 pmid: 28218981
[38] Wen W W, Li D, Li X, Gao Y Q, Li W Q, Li H H, Liu J, Liu H J, Chen W, Luo J, Yan J B. Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun, 2014, 5: 1-10.
[39] Endelman J B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome, 2011, 4: 250-255.
doi: 10.3835/plantgenome2011.08.0024
[40] Zheng L W, Ma S J, Shen D D, Fu H, Wang Y, Liu Y, Shah K, Yue C P, Huang J Y. Genome-wide identification of Gramineae histone modification genes and their potential roles in regulating wheat and maize growth and stress responses. BMC Plant Biol, 2021, 21: 543.
doi: 10.1186/s12870-021-03332-8 pmid: 34800975
[41] De Los Campos G, Hickey J M, Pong-Wong R, Daetwyler H D, Calus M P. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics, 2013, 193: 327-345.
doi: 10.1534/genetics.112.143313 pmid: 22745228
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