Biotechnology Bulletin ›› 2024, Vol. 40 ›› Issue (3): 1-13.doi: 10.13560/j.cnki.biotech.bull.1985.2023-1079
WANG Xin1,2(), XU Yi-yi1, XU Yang1, XU Chen-wu1()
Received:
2023-11-17
Online:
2024-03-26
Published:
2024-04-08
Contact:
XU Chen-wu
E-mail:seuwangxin@163.com;cwxu@yzu.edu.cn
WANG Xin, XU Yi-yi, XU Yang, XU Chen-wu. Research Progress in Genomic Selection Breeding Technology for Crops[J]. Biotechnology Bulletin, 2024, 40(3): 1-13.
影响因素 Affecting factors | 优化策略 Optimizating strategies | |
---|---|---|
遗传因素 | 目标性状遗传力 | 增加训练样本田间试验的重复次数;增加优选群体的数目;多性状或多组学预测 |
训练群体和育种群体间的关系 | 科学开展遗传交配设计;优化训练样本的选择 | |
标记密度 | 开发GS专用芯片;选用全基因组上适当密度的代表性标记 | |
标记和QTL间连锁不平衡的程度 | 候选基因遴选;单倍型划分 | |
非遗传因素 | 训练样本数量 | 增加训练样本数量;多环境联合预测 |
模型和算法的选择 | 参考模型和算法的比较研究成果;考虑性状的遗传结构;训练集内的交叉验证 | |
参数的选择 | 基于多组数据集,进行网格搜索、随机搜索或人工调参,优化参数组合 | |
数据的清洗方案 | 数据的标准化或归一化等预处理;单倍型划分;主成分分析;因子分析;聚类分析 |
Table 1 Factors affecting GS efficacy and corresponding optimizating strategies
影响因素 Affecting factors | 优化策略 Optimizating strategies | |
---|---|---|
遗传因素 | 目标性状遗传力 | 增加训练样本田间试验的重复次数;增加优选群体的数目;多性状或多组学预测 |
训练群体和育种群体间的关系 | 科学开展遗传交配设计;优化训练样本的选择 | |
标记密度 | 开发GS专用芯片;选用全基因组上适当密度的代表性标记 | |
标记和QTL间连锁不平衡的程度 | 候选基因遴选;单倍型划分 | |
非遗传因素 | 训练样本数量 | 增加训练样本数量;多环境联合预测 |
模型和算法的选择 | 参考模型和算法的比较研究成果;考虑性状的遗传结构;训练集内的交叉验证 | |
参数的选择 | 基于多组数据集,进行网格搜索、随机搜索或人工调参,优化参数组合 | |
数据的清洗方案 | 数据的标准化或归一化等预处理;单倍型划分;主成分分析;因子分析;聚类分析 |
[1] |
Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps[J]. Genetics, 2001, 157(4): 1819-1829.
doi: 10.1093/genetics/157.4.1819 pmid: 11290733 |
[2] |
Xu SZ, Zhu D, Zhang QF. Predicting hybrid performance in rice using genomic best linear unbiased prediction[J]. Proc Natl Acad Sci USA, 2014, 111(34): 12456-12461.
doi: 10.1073/pnas.1413750111 pmid: 25114224 |
[3] |
Juliana P, Singh RP, Braun HJ, et al. Genomic selection for grain yield in the CIMMYT wheat breeding program-status and perspectives[J]. Front Plant Sci, 2020, 11: 564183.
doi: 10.3389/fpls.2020.564183 URL |
[4] |
Zhang XC, Pérez-Rodríguez P, Burgueño J, et al. Rapid cycling genomic selection in a multiparental tropical maize population[J]. G3, 2017, 7(7): 2315-2326.
doi: 10.1534/g3.117.043141 URL |
[5] |
Guo TT, Yu XQ, Li XR, et al. Optimal designs for genomic selection in hybrid crops[J]. Mol Plant, 2019, 12(3): 390-401.
doi: S1674-2052(19)30002-4 pmid: 30625380 |
[6] |
Wang X, Zhang ZL, Xu Y, et al. Using genomic data to improve the estimation of general combining ability based on sparse partial diallel cross designs in maize[J]. Crop J, 2020, 8(5): 819-829.
doi: 10.1016/j.cj.2020.04.012 |
[7] |
Xu Y, Zhao Y, Wang X, et al. Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice[J]. Plant Biotechnol J, 2021, 19(2): 261-272.
doi: 10.1111/pbi.v19.2 URL |
[8] |
Yin LL, Zhang HH, Zhou X, et al. KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters[J]. Genome Biol, 2020, 21(1): 146.
doi: 10.1186/s13059-020-02052-w pmid: 32552725 |
[9] |
Wang KL, Abid MA, Rasheed A, et al. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants[J]. Mol Plant, 2023, 16(1): 279-293.
doi: 10.1016/j.molp.2022.11.004 URL |
[10] | 王欣, 孙辉, 胡中立, 等. 基因组选择方法研究进展[J]. 扬州大学学报: 农业与生命科学版, 2018, 39(1): 61-67. |
Wang X, Sun H, Hu ZL, et al. The research progress of genomic selection methods[J]. J Yangzhou Univ Agric Life Sci Ed, 2018, 39(1): 61-67. | |
[11] |
VanRaden PM. Efficient methods to compute genomic predictions[J]. J Dairy Sci, 2008, 91(11): 4414-4423.
doi: 10.3168/jds.2007-0980 pmid: 18946147 |
[12] |
Wang X, Yang ZF, Xu CW. A comparison of genomic selection methods for breeding value prediction[J]. Sci Bull, 2015, 60(10): 925-935.
doi: 10.1007/s11434-015-0791-2 URL |
[13] |
Zhang Z, Erbe M, He JL, et al. Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix[J]. G3, 2015, 5(4): 615-627.
doi: 10.1534/g3.114.016261 URL |
[14] | Tibshirani R. Regression shrinkage and selection via the lasso[J]. J R Stat Soc Ser B Methodol, 1996, 58(1): 267-288. |
[15] |
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent[J]. J Stat Softw, 2010, 33(1): 1-22.
pmid: 20808728 |
[16] |
Piepho HP. Ridge regression and extensions for genomewide selection in maize[J]. Crop Sci, 2009, 49(4): 1165-1176.
doi: 10.2135/cropsci2008.10.0595 URL |
[17] |
Zou H, Hastie T. Regularization and variable selection via the elastic net[J]. J R Stat Soc Ser B Stat Methodol, 2005, 67(2): 301-320.
doi: 10.1111/j.1467-9868.2005.00503.x URL |
[18] |
Habier D, Fernando RL, Kizilkaya K, et al. Extension of the Bayesian alphabet for genomic selection[J]. BMC Bioinformatics, 2011, 12: 186.
doi: 10.1186/1471-2105-12-186 pmid: 21605355 |
[19] |
Pérez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package[J]. Genetics, 2014, 198(2): 483-495.
doi: 10.1534/genetics.114.164442 pmid: 25009151 |
[20] | Kasnavi SA, Afshar MA, Shariati MM, et al. Performance evaluation of support vector machine(SVM)-based predictors in genomic selection[J]. Indian J Anim Sci, 2017, 87(10): 1226-1231. |
[21] |
De los Campos G, Gianola D, Rosa GJM, et al. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods[J]. Genet Res, 2010, 92(4): 295-308.
doi: 10.1017/S0016672310000285 URL |
[22] |
Exterkate P, Groenen PJF, Heij C, et al. Nonlinear forecasting with many predictors using kernel ridge regression[J]. Int J Forecast, 2016, 32(3): 736-753.
doi: 10.1016/j.ijforecast.2015.11.017 URL |
[23] |
Montesinos-López OA, Montesinos-López A, Hernandez-Suarez CM, et al. Deep-learning power and perspectives for genomic selection[J]. Plant Genome, 2021, 14(3): e20122.
doi: 10.1002/tpg2.v14.3 URL |
[24] |
Pérez-Enciso M, Zingaretti LM. A guide for using deep learning for complex trait genomic prediction[J]. Genes, 2019, 10(7): 553.
doi: 10.3390/genes10070553 URL |
[25] |
Montesinos-López OA, Martín-Vallejo J, Crossa J, et al. A benchmarking between deep learning, support vector machine and Bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding[J]. G3, 2019, 9(2): 601-618.
doi: 10.1534/g3.118.200998 URL |
[26] |
Montesinos-López OA, Montesinos-López A, Crossa J, et al. Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits[J]. G3, 2018, 8(12): 3829-3840.
doi: 10.1534/g3.118.200728 URL |
[27] |
Montesinos-López OA, Martín-Vallejo J, Crossa J, et al. New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes[J]. G3, 2019, 9(5): 1545-1556.
doi: 10.1534/g3.119.300585 URL |
[28] |
Ma WL, Qiu ZX, Song J, et al. A deep convolutional neural network approach for predicting phenotypes from genotypes[J]. Planta, 2018, 248(5): 1307-1318.
doi: 10.1007/s00425-018-2976-9 pmid: 30101399 |
[29] |
Banerjee R, Marathi B, Singh M. Efficient genomic selection using ensemble learning and ensemble feature reduction[J]. J Crop Sci Biotechnol, 2020, 23(4): 311-323.
doi: 10.1007/s12892-020-00039-4 |
[30] |
Holliday JA, Wang TL, Aitken S. Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce(Picea sitchensis)using random forest[J]. G3, 2012, 2(9): 1085-1093.
doi: 10.1534/g3.112.002733 URL |
[31] |
Friedman JH. Greedy function approximation: a gradient boosting machine[J]. Ann Statist, 2001, 29(5): 1189-1232.
doi: 10.1214/aos/1013203450 URL |
[32] |
Westhues CC, Mahone GS, da Silva S, et al. Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks[J]. Front Plant Sci, 2021, 12: 699589.
doi: 10.3389/fpls.2021.699589 URL |
[33] |
Yan J, Xu YT, Cheng Q, et al. LightGBM: accelerated genomically designed crop breeding through ensemble learning[J]. Genome Biol, 2021, 22(1): 271.
doi: 10.1186/s13059-021-02492-y pmid: 34544450 |
[34] |
Wang X, Li L, Yang Z, et al. Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II[J]. Heredity, 2017, 118(3): 302-310.
doi: 10.1038/hdy.2016.87 pmid: 27649618 |
[35] |
Riedelsheimer C, Endelman JB, Stange M, et al. Genomic predictability of interconnected biparental maize populations[J]. Genetics, 2013, 194(2): 493-503.
doi: 10.1534/genetics.113.150227 pmid: 23535384 |
[36] |
Xu YB, Liu XG, Fu JJ, et al. Enhancing genetic gain through genomic selection: from livestock to plants[J]. Plant Commun, 2019, 1(1): 100005.
doi: 10.1016/j.xplc.2019.100005 URL |
[37] |
Su G, Brøndum RF, Ma P, et al. Comparison of genomic predictions using medium-density(-54, 000)and high-density(-777, 000)single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations[J]. J Dairy Sci, 2012, 95(8): 4657-4665.
doi: 10.3168/jds.2012-5379 pmid: 22818480 |
[38] |
Xu Y, Wang X, Ding XW, et al. Genomic selection of agronomic traits in hybrid rice using an NCII population[J]. Rice, 2018, 11(1): 32.
doi: 10.1186/s12284-018-0223-4 pmid: 29748895 |
[39] |
Voss-Fels KP, Cooper M, Hayes BJ. Accelerating crop genetic gains with genomic selection[J]. Theor Appl Genet, 2019, 132(3): 669-686.
doi: 10.1007/s00122-018-3270-8 pmid: 30569365 |
[40] |
Xu Y, Xu C, Xu S. Prediction and association mapping of agronomic traits in maize using multiple omic data[J]. Heredity, 2017, 119(3): 174-184.
doi: 10.1038/hdy.2017.27 pmid: 28590463 |
[41] |
Hochholdinger F, Hoecker N. Towards the molecular basis of heterosis[J]. Trends Plant Sci, 2007, 12(9): 427-432.
doi: 10.1016/j.tplants.2007.08.005 pmid: 17720610 |
[42] |
Guo M, Rupe MA, Yang XF, et al. Genome-wide transcript analysis of maize hybrids: allelic additive gene expression and yield heterosis[J]. Theor Appl Genet, 2006, 113(5): 831-845.
doi: 10.1007/s00122-006-0335-x pmid: 16868764 |
[43] |
Nishio M, Satoh M. Including dominance effects in the genomic BLUP method for genomic evaluation[J]. PLoS One, 2014, 9(1): e85792.
doi: 10.1371/journal.pone.0085792 URL |
[44] |
Hu ZQ, Li YG, Song XH, et al. Genomic value prediction for quantitative traits under the epistatic model[J]. BMC Genet, 2011, 12: 15.
doi: 10.1186/1471-2156-12-15 pmid: 21269439 |
[45] |
Varona L, Legarra A, Toro MA, et al. Non-additive effects in genomic selection[J]. Front Genet, 2018, 9: 78.
doi: 10.3389/fgene.2018.00078 pmid: 29559995 |
[46] |
Xu SZ. Mapping quantitative trait loci by controlling polygenic background effects[J]. Genetics, 2013, 195(4): 1209-1222.
doi: 10.1534/genetics.113.157032 pmid: 24077303 |
[47] | Miranda TLR, Azevedo CF, et al. Evaluation of a new additive-dominance genomic model and implications for quantitative genetics and genomic selection[J]. Sci Agric(Piracicaba, Braz), 2022, 79(6): e20210074. |
[48] |
Huang W, MacKay TFC. The genetic architecture of quantitative traits cannot be inferred from variance component analysis[J]. PLoS Genet, 2016, 12(11): e1006421.
doi: 10.1371/journal.pgen.1006421 URL |
[49] |
Li LZ, Zheng XF, Wang JB, et al. Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids[J]. Nat Commun, 2023, 14(1): 3930.
doi: 10.1038/s41467-023-39534-x pmid: 37402793 |
[50] | Budhlakoti N, Rai A, Mishra DC, et al. Comparative study of different non-parametric genomic selection methods under diverse genetic architecture[J]. Indian J Genet Plant Breed, 2020, 80(4): 395-401. |
[51] | 王向峰, 才卓. 中国种业科技创新的智能时代——“玉米育种4.0”[J]. 玉米科学, 2019, 27(1): 1-9. |
Wang XF, Cai Z. Era of maize breeding 4.0[J]. J Maize Sci, 2019, 27(1): 1-9. | |
[52] |
Wang JK, Crossa J, Gai JY. Quantitative genetic studies with applications in plant breeding in the omics era[J]. Crop J, 2020, 8(5): 683-687.
doi: 10.1016/j.cj.2020.09.001 |
[53] |
Chung PY, Liao CT. Identification of superior parental lines for biparental crossing via genomic prediction[J]. PLoS One, 2020, 15(12): e0243159.
doi: 10.1371/journal.pone.0243159 URL |
[54] |
王欣, 马莹, 胡中立, 等. 基于不完全双列杂交设计的水稻农艺性状配合力基因组预测[J]. 中国水稻科学, 2019, 33(4): 331-337.
doi: 10.16819/j.1001-7216.2019.9025 |
Wang X, Ma Y, Hu ZL, et al. Genomic prediction of combining ability for agronomic traits in rice based on NCII design[J]. Chin J Rice Sci, 2019, 33(4): 331-337.
doi: 10.16819/j.1001-7216.2019.9025 |
|
[55] |
Wang X, Xu Y, Hu ZL, et al. Genomic selection methods for crop improvement: current status and prospects[J]. Crop J, 2018, 6(4): 330-340.
doi: 10.1016/j.cj.2018.03.001 |
[56] |
Schulthess AW, Wang Y, Miedaner T, et al. Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes[J]. Theor Appl Genet, 2016, 129(2): 273-287.
doi: 10.1007/s00122-015-2626-6 pmid: 26561306 |
[57] | Leite WS, Unêda-Trevisoli SH, da Silva FM, et al. Identification of superior genotypes and soybean traits by multivariate analysis and selection index[J]. Revista Ciência Agron, 2018, 49(3): 491-500. |
[58] |
Lyra DH, de Freitas Mendonça L, Galli G, et al. Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids[J]. Mol Breed, 2017, 37(6): 80.
doi: 10.1007/s11032-017-0681-1 URL |
[59] |
Xiao N, Pan CH, Li YH, et al. Genomic insight into balancing high yield, good quality, and blast resistance of japonica rice[J]. Genome Biol, 2021, 22(1): 283.
doi: 10.1186/s13059-021-02488-8 pmid: 34615543 |
[60] |
Wang X, Xu Y, Li PC, et al. Efficiency of linear selection index in predicting rice hybrid performance[J]. Mol Breed, 2019, 39(6): 77.
doi: 10.1007/s11032-019-0986-3 |
[61] |
Liang M, Cao S, Deng TY, et al. MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits[J]. Brief Bioinform, 2023, 24(2): bbad043.
doi: 10.1093/bib/bbad043 URL |
[62] |
Lopez-Cruz M, Crossa J, Bonnett D, et al. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model[J]. G3, 2015, 5(4): 569-582.
doi: 10.1534/g3.114.016097 URL |
[63] | Cuevas J, Crossa J, Soberanis V, et al. Genomic prediction of genotype × environment interaction kernel regression models[J]. Plant Genome, 2016, 9(3): 1-20. |
[64] |
Crossa J, de los Campos G, Maccaferri M, et al. Extending the marker × environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat[J]. Crop Sci, 2016, 56(5): 2193-2209.
doi: 10.2135/cropsci2015.04.0260 URL |
[65] |
Cuevas J, Crossa J, Montesinos-López OA, et al. Bayesian genomic prediction with genotype × environment interaction kernel models[J]. G3, 2017, 7(1): 41-53.
doi: 10.1534/g3.116.035584 URL |
[66] | Rogers AR, Holland JB. Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data[J]. G3, 2022, 12(2): jkab440. |
[67] |
Yan WK, Nilsen KT, Beattie A. Mega-environment analysis and breeding for specific adaptation[J]. Crop Sci, 2023, 63(2): 480-494.
doi: 10.1002/csc2.v63.2 URL |
[68] |
Ritchie MD, Holzinger ER, Li RW, et al. Methods of integrating data to uncover genotype-phenotype interactions[J]. Nat Rev Genet, 2015, 16(2): 85-97.
doi: 10.1038/nrg3868 pmid: 25582081 |
[69] |
Westhues M, Schrag TA, Heuer C, et al. Omics-based hybrid prediction in maize[J]. Theor Appl Genet, 2017, 130(9): 1927-1939.
doi: 10.1007/s00122-017-2934-0 pmid: 28647896 |
[70] |
Frisch M, Thiemann A, Fu JJ, et al. Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize[J]. Theor Appl Genet, 2010, 120(2): 441-450.
doi: 10.1007/s00122-009-1204-1 pmid: 19911157 |
[71] |
Fu JJ, Falke KC, Thiemann A, et al. Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data[J]. Theor Appl Genet, 2012, 124(5): 825-833.
doi: 10.1007/s00122-011-1747-9 pmid: 22101908 |
[72] |
Zenke-Philippi C, Frisch M, Thiemann A, et al. Transcriptome-based prediction of hybrid performance with unbalanced data from a maize breeding programme[J]. Plant Breed, 2017, 136(3): 331-337.
doi: 10.1111/pbr.2017.136.issue-3 URL |
[73] |
Riedelsheimer C, Czedik-Eysenberg A, Grieder C, et al. Genomic and metabolic prediction of complex heterotic traits in hybrid maize[J]. Nat Genet, 2012, 44(2): 217-220.
doi: 10.1038/ng.1033 pmid: 22246502 |
[74] |
Xu SZ, Xu Y, Gong L, et al. Metabolomic prediction of yield in hybrid rice[J]. Plant J, 2016, 88(2): 219-227.
doi: 10.1111/tpj.2016.88.issue-2 URL |
[75] |
Guo ZG, Magwire MM, Basten CJ, et al. Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize[J]. Theor Appl Genet, 2016, 129(12): 2413-2427.
pmid: 27586153 |
[76] |
Schrag TA, Westhues M, Schipprack W, et al. Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize[J]. Genetics, 2018, 208(4): 1373-1385.
doi: 10.1534/genetics.117.300374 URL |
[77] |
Wang SB, Wei JL, Li RD, et al. Identification of optimal prediction models using multi-omic data for selecting hybrid rice[J]. Heredity, 2019, 123(3): 395-406.
doi: 10.1038/s41437-019-0210-6 pmid: 30911139 |
[78] |
Wu PY, Stich B, Weisweiler M, et al. Improvement of prediction ability by integrating multi-omic datasets in barley[J]. BMC Genomics, 2022, 23(1): 200.
doi: 10.1186/s12864-022-08337-7 |
[79] |
Rasheed A, Hao YF, Xia XC, et al. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives[J]. Mol Plant, 2017, 10(8): 1047-1064.
doi: S1674-2052(17)30174-0 pmid: 28669791 |
[80] |
Guo ZF, Yang QN, Huang FF, et al. Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip[J]. Plant Commun, 2021, 2(6): 100230.
doi: 10.1016/j.xplc.2021.100230 URL |
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