The Research Progress of Genomic Selection in Breeding of Crops

doi:10.7668/hbnxb.20193366

Abstract

Abstract:

Being effective only to the traits controlled by large-effect QTL,conventional marker-assisted selection(MAS)can hardly improve the quantitative traits that are controlled by many small-effect QTL.Later on, the genomic selection (GS) technology proposed in 2001 solves the problem of improving complex traits controlled by the minor polygenic effects due to its high prediction accuracy through estimating the breeding value of individual with high-density molecular markers. At present,it has been successfully applied to the genetic improvement of quantitative traits in animals and plants such as dairy cattle,pigs,sheep,maize,and wheat in the US,Canada,Australia,Germany,France and so on.As a breaking-through breeding technology and a hot spot in research and application now. In this review,we summarized the factors affecting predictive accuracy of GS and the research progress of GS in breeding maize,wheat,rice and rapeseed at home and abroad,and finally discussed the existing problems in its application. This review will provide some reference for GS of the crops in the future.

Key words: Crop, Genomic selection, Marker-assisted selection, Quantitative trait

LIU Hailan, XIA Chao, LAN Hai. The Research Progress of Genomic Selection in Breeding of Crops[J]. Acta Agriculturae Boreali-Sinica, 2022, 37(S1): 51-58. doi: 10.7668/hbnxb.20193366.

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http://www.hbnxb.net/EN/Y2022/V37/IS1/51

References 98

[1]	黎裕, 王建康, 邱丽娟, 马有志, 李新海, 万建民. 中国作物分子育种现状与发展前景[J]. 作物学报, 2010, 36(9):1425-1430. doi:10.3724/SP.J.1006.2010.01425. doi: 10.3724/SP.J.1006.2010.01425
	Li Y, Wang J K, Qiu L J, Ma Y Z, Li X H, Wan J M. Crop molecular breeding in China:Current status and perspectives[J]. Acta Agronomica Sinica, 2010, 36(9):1425-1430.
[2]	Dudley J W. Molecular markers in plant improvement:manipulation of genes affecting quantitative traits[J]. Crop Science, 1993, 33(4):660-668. doi:10.2135/cropsci1993.0011183X003300040003x. doi: 10.2135/cropsci1993.0011183X003300040003x URL
[3]	Lander E S, Botstein D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps[J]. Genetics, 1989, 121(1):185-199. doi:10.1093/genetics/121.1.185. doi: 10.1093/genetics/121.1.185 pmid: 2563713
[4]	Zeng Z B. Precision mapping of quantitative trait loci[J]. Genetics, 1994, 136(4):1457-1468. doi:10.1093/genetics/136.4.1457. doi: 10.1093/genetics/136.4.1457 pmid: 8013918
[5]	Lande R, Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits[J]. Genetics, 1990, 124(3):743-756. doi:10.1093/genetics/124.3.743. doi: 10.1093/genetics/124.3.743 pmid: 1968875
[6]	Bernardo R. Molecular markers and selection for complex traits in plants:learning from the last 20 years[J]. Crop Science, 2008, 48(5):1649-1664. doi:10.2135/cropsci2008.03.0131. doi: 10.2135/cropsci2008.03.0131 URL
[7]	Ashraf M, Foolad M R. Crop breeding for salt tolerance in the era of molecular markers and marker-assisted selection[J]. Plant Breeding, 2013, 132(1):10-20. doi:10.1111/pbr.12000. doi: 10.1111/pbr.12000 URL
[8]	Morrell P L, Buckler E S, Ross-Ibarra J. Crop genomics:Advances and applications[J]. Nature Reviews Genetics, 2011, 13(2):85-96. doi:10.1038/nrg3097. doi: 10.1038/nrg3097 pmid: 22207165
[9]	Heffner E L, Sorrells M E, Jannink J L. Genomic selection for crop improvement[J]. Crop Science, 2009, 49(1):1-12. doi:10.2135/cropsci2008.08.0512. doi: 10.2135/cropsci2008.08.0512 URL
[10]	Meuwissen T H E, Hayes B J, Goddard M E. 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. doi: 10.1093/genetics/157.4.1819 pmid: 11290733
[11]	Voss-Fels K P, Cooper M, Hayes B J. Accelerating crop genetic gains with genomic selection[J]. Theoretical and Applied Genetics, 2019, 132(3):669-686. doi:10.1007/s00122-018-3270-8. doi: 10.1007/s00122-018-3270-8 pmid: 30569365
[12]	Goddard M E, Hayes B J. Genomic selection[J]. Journal of Animal Breeding and Genetics, 2007, 124(6):323-330. doi:10.1111/j.1439-0388.2007.00702.x. doi: 10.1111/j.1439-0388.2007.00702.x pmid: 18076469
[13]	Schaeffer L R. Strategy for applying genome-wide selection in dairy cattle[J]. Journal of Animal Breeding and Genetics, 2006, 123(4):218-223. doi:10.1111/j.1439-0388.2006.00595.x. doi: 10.1111/j.1439-0388.2006.00595.x pmid: 16882088
[14]	Hayes B J, Bowman P J, Chamberlain A J, Goddard M E. Invited review:Genomic selection in dairy cattle:Progress and challenges[J]. Journal of Dairy Science, 2009, 92(2):433-443. doi:10.3168/jds.2008-1646. doi: 10.3168/jds.2008-1646 pmid: 19164653
[15]	Tribout T, Larzul C, Phocas F. Economic aspects of implementing genomic evaluations in a pig sire line breeding scheme[J]. Genetics,Selection,Evolution, 2013, 45(1):40. doi:10.1186/1297-9686-45-40. doi: 10.1186/1297-9686-45-40
[16]	Raoul J, Swan A A, Elsen J M. Using a very low-density SNP panel for genomic selection in a breeding program for sheep[J]. Genetics,Selection,Evolution, 2017, 49(1):76. doi:10.1186/s12711-017-0351-0. doi: 10.1186/s12711-017-0351-0
[17]	Zhao Y S, Li Z, Liu G Z, Jiang Y, Maurer H P, Würschum T, Mock H P, Matros A, Ebmeyer E, Schachschneider R, Kazman E, Schacht J, Gowda M, Longin C F H, Reif J C. Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding[J]. PNAS, 2015, 112(51):15624-15629. doi:10.1073/pnas.1514547112. doi: 10.1073/pnas.1514547112 pmid: 26663911
[18]	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[J]. Nature Genetics, 2012, 44(2):217-220. doi:10.1038/ng.1033. doi: 10.1038/ng.1033 pmid: 22246502
[19]	Xu S Z, Zhu D, Zhang Q F. Predicting hybrid performance in rice using genomic best linear unbiased prediction[J]. PNAS, 2014, 111(34):12456-12461. doi:10.1073/pnas.1413750111. doi: 10.1073/pnas.1413750111 pmid: 25114224
[20]	Habier D, Fernando R L, Garrick D J. Genomic BLUP decoded:A look into the black box of genomic prediction[J]. Genetics, 2013, 194(3):597-607. doi:10.1534/genetics.113.152207. doi: 10.1534/genetics.113.152207 pmid: 23640517
[21]	Legarra A, Christensen O F, Aguilar I, Misztal I. Single step,a general approach for genomic selection[J]. Livestock Science, 2014, 166:54-65. doi:10.1016/j.livsci.2014.04.029. doi: 10.1016/j.livsci.2014.04.029 URL
[22]	Liu H L, Chen G B. A fast genomic selection approach for large genomic data[J]. Theoretical and Applied Genetics, 2017, 130(6):1277-1284. doi:10.1007/s00122-017-2887-3. doi: 10.1007/s00122-017-2887-3 pmid: 28389770
[23]	Habier D, Fernando R L, Kizilkaya K, Garrick D J. Extension of the bayesian alphabet for genomic selection[J]. BMC Bioinformatics, 2011, 12:186. doi:10.1186/1471-2105-12-186. doi: 10.1186/1471-2105-12-186 pmid: 21605355
[24]	Ogutu J O, Piepho H P, Schulz-Streeck T. A comparison of random forests,boosting and support vector machines for genomic selection[J]. BMC Proceedings, 2011, 5(S3):S11. doi:10.1186/1753-6561-5-S3-S11. doi: 10.1186/1753-6561-5-S3-S11
[25]	Bayer P E, Petereit J, Danilevicz M F, Anderson R, Batley J, Edwards D. The application of pangenomics and machine learning in genomic selection in plants[J]. The Plant Genome, 2021, 14(3):e20112. doi:10.1002/tpg2.20112. doi: 10.1002/tpg2.20112
[26]	Daetwyler H D, Pong-Wong R, Villanueva B, Woolliams J A. The impact of genetic architecture on genome-wide evaluation methods[J]. Genetics, 2010, 185(3):1021-1031. doi:10.1534/genetics.110.116855. doi: 10.1534/genetics.110.116855 pmid: 20407128
[27]	Jiang S Q, Cheng Q, Yan J, Fu R, Wang X F. Genome optimization for improvement of maize breeding[J]. Theoretical and Applied Genetics, 2020, 133(5):1491-1502. doi:10.1007/s00122-019-03493-z. doi: 10.1007/s00122-019-03493-z pmid: 31811314
[28]	Karaman E, Cheng H, Firat M Z, Garrick D J, Fernando R L. An upper bound for accuracy of prediction using GBLUP[J]. PLoS One, 2016, 11(8):e0161054. doi:10.1371/journal.pone.0161054. doi: 10.1371/journal.pone.0161054
[29]	Liu H L, Chen G B. A new genomic prediction method with additive-dominance effects in the least-squares framework[J]. Heredity, 2018, 121(2):196-204. doi:10.1038/s41437-018-0099-5. doi: 10.1038/s41437-018-0099-5 pmid: 29925888
[30]	Norman A, Taylor J, Edwards J, Kuchel H. Optimising genomic selection in wheat:Effect of marker density,population size and population structure on prediction accuracy[J]. G3 Genes\|Genomes\|Genetics, 2018, 8(9):2889-2899. doi:10.1534/g3.118.200311. doi: 10.1534/g3.118.200311
[31]	Zhao Y S, Gowda M, Liu W X, Würschum T, Maurer H P, Longin F H, Ranc N, Reif J C. Accuracy of genomic selection in European maize elite breeding populations[J]. Theoretical and Applied Genetics, 2012, 124(4):769-776. doi:10.1007/s00122-011-1745-y. doi: 10.1007/s00122-011-1745-y pmid: 22075809
[32]	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[J]. The Crop Journal, 2018, 6(4):341-352. doi:10.1016/j.cj.2018.03.005. doi: 10.1016/j.cj.2018.03.005 URL
[33]	Bernardo R, Yu J M. Prospects for genomewide selection for quantitative traits in maize[J]. Crop Science, 2007, 47(3):1082-1090. doi: 10.2135/cropsci2006.11.0690. doi: 10.2135/cropsci2006.11.0690 URL
[34]	Romero Navarro J A, Willcox M, Burgueño J, Romay C, Swarts K, Trachsel S, Preciado E, Terron A, Delgado H V, Vidal V, Ortega A, Banda A E, Montiel N O G, Ortiz-Monasterio I, Vicente F S, Espinoza A G, Atlin G, Wenzl P, Hearne S, Buckler E S. A study of allelic diversity underlying flowering-time adaptation in maize landraces[J]. Nature Genetics, 2017, 49(3):476-480. doi:10.1038/ng.3784. doi: 10.1038/ng.3784 pmid: 28166212
[35]	Atanda S A, Olsen M, Burgue o J, Crossa J, Dzidzienyo D, Beyene Y, Gowda M, Dreher K, Zhang X C, Prasanna B M, Tongoona P, Danquah E Y, Olaoye G, Robbins K R. Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program[J]. Theoretical and Applied Genetics, 2021, 134(1):279-294. doi:10.1007/s00122-020-03696-9. doi: 10.1007/s00122-020-03696-9 pmid: 33037897
[36]	Cooper M, Technow F, Messina C, Gho C, Totir L R. Use of crop growth models with whole-genome prediction:application to a maize multienvironment trial[J]. Crop Science, 2016, 56(5):2141-2156. doi:10.2135/cropsci2015.08.0512. doi: 10.2135/cropsci2015.08.0512 URL
[37]	Jacobson A, Lian L, Zhong S Q, Bernardo R. Minimal loss of genetic diversity after genomewide selection within biparental maize populations[J]. Crop Science, 2015, 55(2):783-789. doi:10.2135/cropsci2014.10.0744. doi: 10.2135/cropsci2014.10.0744 URL
[38]	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[J]. Theoretical and Applied Genetics, 2012, 124(2):261-275. doi:10.1007/s00122-011-1702-9. doi: 10.1007/s00122-011-1702-9 pmid: 21938474
[39]	Zhang X, Pérez-Rodríguez P, Semagn K, Beyene Y, Babu R, López-Cruz M A, San Vicente F, 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[J]. Heredity, 2015, 114(3):291-299. doi:10.1038/hdy.2014.99. doi: 10.1038/hdy.2014.99 pmid: 25407079
[40]	Fritsche-Neto R, Akdemir D, Jannink J L. Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs[J]. Theoretical and Applied Genetics, 2018, 131(5):1153-1162. doi:10.1007/s00122-018-3068-8. doi: 10.1007/s00122-018-3068-8 pmid: 29445844
[41]	Guo T T, Li H H, Yan J B, Tang J H, Li J S, Zhang Z W, Zhang L Y, Wang J K. Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines[J]. Theoretical and Applied Genetics, 2013, 126(1):189-201. doi:10.1007/s00122-012-1973-9. doi: 10.1007/s00122-012-1973-9 pmid: 22972201
[42]	Liu L, Du Y F, Huo D A, Wang M, Shen X M, Yue B, Qiu F Z, Zheng Y L, Yan J B, Zhang Z X. Genetic architecture of maize kernel row number and whole genome prediction[J]. Theoretical and Applied Genetics, 2015, 128(11):2243-2254. doi:10.1007/s00122-015-2581-2. doi: 10.1007/s00122-015-2581-2 pmid: 26188589
[43]	Li G L, Dong Y, Zhao Y S, Tian X K, Würschum T, Xue J Q, Chen S J, Reif J C, Xu S T, Liu W X. Genome-wide prediction in a hybrid maize population adapted to Northwest China[J]. The Crop Journal, 2020, 8(5):830-842. doi:10.1016/j.cj.2020.04.006. doi: 10.1016/j.cj.2020.04.006 URL
[44]	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[J]. Frontiers in Plant Science, 2019, 10:1129. doi:10.3389/fpls.2019.01129. doi: 10.3389/fpls.2019.01129 pmid: 31620155
[45]	Xiao Y J, Jiang S Q, Cheng Q, Wang X Q, Yan J, Zhang R Y, Qiao F, Ma C, Luo J Y, Li W Q, Liu H J, Yang W Y, Song W H, Meng Y J, Warburton M L, Zhao J R, Wang X F, Yan J B. The genetic mechanism of heterosis utilization in maize improvement[J]. Genome Biology, 2021, 22(1):148. doi:10.1186/s13059-021-02370-7. doi: 10.1186/s13059-021-02370-7 pmid: 33971930
[46]	Xu C, Zhang H W, Sun J H, Guo Z F, Zou C, Li W X, Xie C X, Huang C L, Xu R N, Liao H, Wang J X, Xu X J, Wang S H, Xu Y B. Genome-wide association study dissects yield components associated with low-phosphorus stress tolerance in maize[J]. Theoretical and Applied Genetics, 2018, 131(8):1699-1714. doi:10.1007/s00122-018-3108-4. doi: 10.1007/s00122-018-3108-4 pmid: 29754325
[47]	Guo Z F, Zou C, Liu X G, Wang S H, Li W X, Jeffers D, Fan X M, Xu M L, Xu Y B. Complex genetic system involved in Fusarium ear rot resistance in maize as revealed by GWAS,bulked sample analysis,and genomic prediction[J]. Plant Disease, 2020, 104(6):1725-1735. doi:10.1094/PDIS-07-19-1552-RE. doi: 10.1094/PDIS-07-19-1552-RE URL
[48]	Yong H J, Wang N, Yang X J, Zhang F Y, Tang J, Yang Z Y, Zhao X Z, Li Y, Li M S, Zhang D G, Hao Z F, Weng J F, Han J N, Li H H, Li X H. Genomic selection to introgress exotic maize germplasm into elite maize in China to improve kernel dehydration rate[J]. Euphytica, 2021, 217(8): 1-14. doi:10.1007/s10681-021-02899-5. doi: 10.1007/s10681-021-02899-5 URL
[49]	杨建昌, 李超卿, 江贻. 稻米氨基酸含量和组分及其调控[J]. 作物学报, 2022, 48(5):1037-1050. doi:10.3724/SP.J.1006.2022.12062. doi: 10.3724/SP.J.1006.2022.12062
	Yang J C, Li C Q, Jiang Y. Contents and compositions of amino acids in rice grains and their regulation:A review[J]. Acta Agronomica Sinica, 2022, 48(5):1037-1050. doi: 10.3724/SP.J.1006.2022.12062
[50]	Xu S Z. Genetic mapping and genomic selection using recombination breakpoint data[J]. Genetics, 2013, 195(3):1103-1115. doi:10.1534/genetics.113.155309. doi: 10.1534/genetics.113.155309 pmid: 23979575
[51]	Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña 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[J]. PLoS Genetics, 2015, 11(2):e1004982. doi:10.1371/journal.pgen.1004982. doi: 10.1371/journal.pgen.1004982
[52]	Huang M, Balimponya E G, Mgonja E M, McHale L K, Luzi-Kihupi A, Wang G L, Sneller C H. Use of genomic selection in breeding rice(Oryza sativa L.)for resistance to rice blast(Magnaporthe oryzae)[J]. Molecular Breeding, 2019, 39(8): 1-16. doi:10.1007/s11032-019-1023-2. doi: 10.1007/s11032-019-1023-2 URL
[53]	Onogi A, Ideta O, Inoshita Y, Ebana K, Yoshioka T, Yamasaki M, Iwata H. Exploring the areas of applicability of whole-genome prediction methods for Asian rice(Oryza sativa L.)[J]. Theoretical and Applied Genetics, 2015, 128(1):41-53. doi:10.1007/s00122-014-2411-y. doi: 10.1007/s00122-014-2411-y pmid: 25341369
[54]	Grenier C, Cao T V, Ospina Y, Quintero C, Châtel M H, Tohme J, Courtois B, Ahmadi N. Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding[J]. PLoS One, 2015, 10(8):e0136594. doi:10.1371/journal.pone.0136594. doi: 10.1371/journal.pone.0136594
[55]	Bhandari A, Bartholom J, Cao-Hamadoun T V, Kumari N, Frouin J, Kumar A, Ahmadi N. Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice[J]. PLoS One, 2019, 14(5):e0208871. doi:10.1371/journal.pone.0208871. doi: 10.1371/journal.pone.0208871
[56]	Frouin J, Labeyrie A, Boisnard A, Sacchi G A, Ahmadi N. Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains[J]. PLoS One, 2019, 14(6):e0217516. doi:10.1371/journal.pone.0217516. doi: 10.1371/journal.pone.0217516
[57]	Grinberg N F, Orhobor O I, King R D. An evaluation of machine-learning for predicting phenotype:Studies in yeast,rice,and wheat[J]. Machine Learning, 2020, 109(2):251-277. doi:10.1007/s10994-019-05848-5. doi: 10.1007/s10994-019-05848-5 URL
[58]	Júnior O P M, Breseghello F, Duarte J B, Coelho A S G, Borba T C O, Aguiar J T, Neves P C F, Morais O P. Assessing prediction models for different traits in a rice population derived from a recurrent selection program[J]. Crop Science, 2018, 58(6):2347-2359. doi:10.2135/cropsci2018.02.0087. doi: 10.2135/cropsci2018.02.0087 URL
[59]	纪龙, 申红芳, 徐春春, 陈中督, 方福平. 基于非线性主成分分析的绿色超级稻品种综合评价[J]. 作物学报, 2019, 45(7):982-992. doi:10.3724/SP.J.1006.2019.82057. doi: 10.3724/SP.J.1006.2019.82057
	Ji L, Shen H F, Xu C C, Chen Z D, Fang F P. Comprehensive evaluation of green super rice varieties based on nonlinear principal component analysis[J]. Acta Agronomica Sinica, 2019, 45(7):982-992. doi: 10.3724/SP.J.1006.2019.82057
[60]	程慧煌, 易振波, 曾勇军, 郑厚亮, 商庆银. 超级杂交稻抗倒伏能力及其对施肥量的响应[J]. 核农学报, 2018, 32(8):1603-1610. doi:10.11869/j.issn.100-8551.2018.08.1603. doi: 10.11869/j.issn.100-8551.2018.08.1603
	Cheng H H, Yi Z B, Zeng Y J, Zheng H L, Shang Q Y. Lodging resistance of super hybrid rice at different yield levels and its response to fertilization[J]. Journal of Nuclear Agricultural Sciences, 2018, 32(8):1603-1610.
[61]	Cui Y R, Li R D, Li G W, Zhang F, Zhu T T, Zhang Q F, Ali J, Li Z K, Xu S Z. Hybrid breeding of rice via genomic selection[J]. Plant Biotechnology Journal, 2020, 18(1):57-67. doi:10.1111/pbi.13170. doi: 10.1111/pbi.13170 pmid: 31124256
[62]	Xu S Z, Xu Y, Gong L, Zhang Q F. Metabolomic prediction of yield in hybrid rice[J]. The Plant Journal, 2016, 88(2):219-227. doi:10.1111/tpj.13242. doi: 10.1111/tpj.13242 pmid: 27311694
[63]	Xiao N, Pan C H, Li Y H, Wu Y Y, Cai Y, Lu Y, Wang R Y, Yu L, Shi W, Kang H X, Zhu Z B, Huang N S, Zhang X X, Chen Z C, Liu J J, Yang Z F, Ning Y S, Li A H. Genomic insight into balancing high yield,good quality,and blast resistance of japonica rice[J]. Genome Biology, 2021, 22:283. doi:10.1186/s13059-021-02488-8. doi: 10.1186/s13059-021-02488-8 pmid: 34615543
[64]	邓梅, 何员江, 苟璐璐, 姚方杰, 李健, 张雪梅, 龙黎, 马建, 江千涛, 刘亚西, 魏育明, 陈国跃. 小麦骨干亲本繁6产量相关性状关键基因组区段的遗传效应[J]. 作物学报, 2018, 44(5):706-715. doi:10.3724/SP.J.1006.2018.00706. doi: 10.3724/SP.J.1006.2018.00706
	Deng M, He Y J, Gou L L, Yao F J, Li J, Zhang X M, Long L, Ma J, Jiang Q T, Liu Y X, Wei Y M, Chen G Y. Genetic effects of key genomic regions controlling yield-related traits in wheat founder parent Fan 6[J]. Acta Agronomica Sinica, 2018, 44(5):706-715. doi: 10.3724/SP.J.1006.2018.00706 URL
[65]	de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes J M. Predicting quantitative traits with regression models for dense molecular markers and pedigree[J]. Genetics, 2009, 182(1):375-385. doi:10.1534/genetics.109.101501. doi: 10.1534/genetics.109.101501 pmid: 19293140
[66]	Bassi F M, Bentley A R, Charmet G, Ortiz R, Crossa J. Breeding schemes for the implementation of genomic selection in wheat(Triticum spp.)[J]. Plant Science, 2016, 242:23-36. doi:10.1016/j.plantsci.2015.08.021. doi: 10.1016/j.plantsci.2015.08.021 URL
[67]	Heffner E L, Jannink J L, Iwata H, Souza E, Sorrells M E. Genomic selection accuracy for grain quality traits in biparental wheat populations[J]. Crop Science, 2011, 51(6):2597-2606. doi:10.2135/cropsci2011.05.0253. doi: 10.2135/cropsci2011.05.0253 URL
[68]	Michel S, Löschenberger F, Ametz C, Pachler B, Sparry E, Bürstmayr H. Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding[J]. Theoretical and Applied Genetics, 2019, 132(6):1745-1760. doi:10.1007/s00122-019-03312-5. doi: 10.1007/s00122-019-03312-5 pmid: 30810763
[69]	Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink J L, Singh R P, Autrique E, de los Campos G. Increased prediction accuracy in wheat breeding trials using a marker×environment interaction genomic selection model[J]. G3 Genes\|Genomes\|Genetics, 2015, 5(4):569-582. doi:10.1534/g3.114.016097. doi: 10.1534/g3.114.016097
[70]	Sandhu K, Patil S S, Pumphrey M, Carter A. Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program[J]. The Plant Genome, 2021, 14(3):e20119. doi:10.1002/tpg2.20119. doi: 10.1002/tpg2.20119
[71]	Arruda M P, Lipka A E, Brown P J, Krill A M, Thurber C, Brown-Guedira G, Dong Y, Foresman B J, Kolb F L. Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat(Triticum aestivum L.)[J]. Molecular Breeding, 2016, 36(7): 1-11. doi:10.1007/s11032-016-0508-5. doi: 10.1007/s11032-016-0508-5 URL
[72]	Joukhadar R, Thistlethwaite R, Trethowan R M, Hayden M J, Stangoulis J, Cu S, Daetwyler H D. Genomic selection can accelerate the biofortification of spring wheat[J]. Theoretical and Applied Genetics, 2021, 134(10):3339-3350. doi:10.1007/s00122-021-03900-4. doi: 10.1007/s00122-021-03900-4 pmid: 34254178
[73]	Bentley A R, Scutari M, Gosman N, Faure S, Bedford F, Howell P, Cockram J, Rose G A, Barber T, Irigoyen J, Horsnell R, Pumfrey C, Winnie E, Schacht J, Beauchêne K, Praud S, Greenland A, Balding D, Mackay I J. Applying association mapping and genomic selection to the dissection of key traits in elite European wheat[J]. Theoretical and Applied Genetics, 2014, 127(12):2619-2633. doi:10.1007/s00122-014-2403-y. doi: 10.1007/s00122-014-2403-y pmid: 25273129
[74]	Juliana P, Poland J, Huerta-Espino J, Shrestha S, Crossa J, Crespo-Herrera L, Toledo F H, Govindan V, Mondal S, Kumar U, Bhavani S, Singh P K, Randhawa M S, He X Y, Guzman C, Dreisigacker S, Rouse M N, Jin Y, Pérez-Rodríguez P, Montesinos-López O A, Singh D, Mokhlesur Rahman M, Marza F, Singh R P. Improving grain yield,stress resilience and quality of bread wheat using large-scale genomics[J]. Nature Genetics, 2019, 51(10):1530-1539. doi:10.1038/s41588-019-0496-6. doi: 10.1038/s41588-019-0496-6 URL
[75]	Yao J, Zhao H D, Chen X M, Zhang Y, Wang J K. Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat(Triticum aestivum L.)[J]. The Crop Journal, 2018, 6(4):353-365. doi:10.1016/j.cj.2018.05.003. doi: 10.1016/j.cj.2018.05.003 URL
[76]	Ali M, Zhang Y, Rasheed A, Wang J K, Zhang L Y. Genomic prediction for grain yield and yield-related traits in Chinese winter wheat[J]. International Journal of Molecular Sciences, 2020, 21(4):1342. doi:10.3390/ijms21041342. doi: 10.3390/ijms21041342 URL
[77]	张春, 赵小珍, 庞承珂, 彭门路, 王晓东, 陈锋, 张维, 陈松, 彭琦, 易斌, 孙程明, 张洁夫, 傅廷栋. 甘蓝型油菜千粒质量全基因组关联分析[J]. 作物学报, 2021, 47(4):650-659. doi:10.3724/SP.J.1006.2021.04136. doi: 10.3724/SP.J.1006.2021.04136
	Zhang C, Zhao X Z, Pang C K, Peng M L, Wang X D, Chen F, Zhang W, Chen S, Peng Q, Yi B, Sun C M, Zhang J F, Fu T D. Genome-wide association study of 1000-seed weight in rapeseed(Brassica napus L.)[J]. Acta Agronomica Sinica, 2021, 47(4):650-659.
[78]	吕伟生, 肖富良, 张绍文, 郑伟, 黄天宝, 肖小军, 李亚贞, 吴艳, 韩德鹏, 肖国滨, 张学昆. 种肥播施方式对红壤旱地油菜产量及肥料利用率的影响[J]. 作物学报, 2020, 46(11):1790-1800. doi:10.3724/SP.J.1006.2020.94203. doi: 10.3724/SP.J.1006.2020.94203
	Lü W S, Xiao F L, Zhang S W, Zheng W, Huang T B, Xiao X J, Li Y Z, Wu Y, Han D P, Xiao G B, Zhang X K. Effects of sowing and fertilizing methods on yield and fertilizer use efficiency in red-soil dryland rapeseed(Brassica napus L.)[J]. Acta Agronomica Sinica, 2020, 46(11):1790-1800.
[79]	秦璐, 韩配配, 常海滨, 顾炽明, 黄威, 李银水, 廖祥生, 谢立华, 廖星. 甘蓝型油菜耐低氮种质筛选及绿肥应用潜力评价[J]. 作物学报, 2022, 48(6):1488-1501. doi:10.3724/SP.J.1006.2022.14087. doi: 10.3724/SP.J.1006.2022.14087
	Qin L, Han P P, Chang H B, Gu C M, Huang W, Li Y S, Liao X S, Xie L H, Liao X. Screening of rapeseed germplasms with low nitrogen tolerance and the evaluation of its potential application as green manure[J]. Acta Agronomica Sinica, 2022, 48(6):1488-1501. doi: 10.3724/SP.J.1006.2022.14087
[80]	Würschum T, Abel S, Zhao Y S. Potential of genomic selection in rapeseed(Brassica napus L.)breeding[J]. Plant Breeding, 2014, 133(1):45-51. doi:10.1111/pbr.12137. doi: 10.1111/pbr.12137 URL
[81]	Knoch D, Werner C R, Meyer R C, Riewe D, Abbadi A, L cke S, Snowdon R J, Altmann T. Multi-omics-based prediction of hybrid performance in canola[J]. Theoretical and Applied Genetics, 2021, 134(4):1147-1165. doi:10.1007/s00122-020-03759-x. doi: 10.1007/s00122-020-03759-x pmid: 33523261
[82]	Jan H U, Abbadi A, Lücke S, Nichols R A, Snowdon R J. Genomic prediction of testcross performance in canola(Brassica napus)[J]. PLoS One, 2016, 11(1):e0147769. doi:10.1371/journal.pone.0147769. doi: 10.1371/journal.pone.0147769
[83]	Werner C R, Qian L W, Voss-Fels K P, Abbadi A, Leckband G, Frisch M, Snowdon R J. Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture[J]. Theoretical and Applied Genetics, 2018, 131(2):299-317. doi:10.1007/s00122-017-3002-5. doi: 10.1007/s00122-017-3002-5 pmid: 29080901
[84]	Koscielny C B, Gardner S W, Technow F, Duncan R W. Linkage mapping and whole-genome predictions in canola(Brassica napus)subjected to differing temperature treatments[J]. Crop & Pasture Science, 2020, 71(3):229-238. doi:10.1071/CP19387. doi: 10.1071/CP19387
[85]	Roy J, Shaikh T M, Mendoza L D R, Hosain S, Chapara V, Rahman M. Genome-wide association mapping and genomic prediction for adult stage sclerotinia stem rot resistance in Brassica napus (L.)under field environments[J]. Scientific Reports, 2021, 11:21773. doi:10.1038/s41598-021-01272-9. doi: 10.1038/s41598-021-01272-9 URL
[86]	Fikere M, Barbulescu D M, Malmberg M M, Shi F, Koh J C O, Slater A T, MacLeod I M, Bowman P J, Salisbury P A, Spangenberg G C, Cogan N O I, Daetwyler H D. Genomic prediction using prior quantitative trait loci information reveals a large reservoir of underutilised blackleg resistance in diverse canola(Brassica napus L.)lines[J]. The Plant Genome, 2018, 11(2):170100. doi:10.3835/plantgenome2017.11.0100. doi: 10.3835/plantgenome2017.11.0100
[87]	Derbyshire M C, Khentry Y, Severn-Ellis A, Mwape V, Saad N S M, Newman T E, Taiwo A, Regmi R, Buchwaldt L, Denton-Giles M, Batley J, Kamphuis L G. Modeling first order additive×additive epistasis improves accuracy of genomic prediction for Sclerotinia stem rot resistance in canola[J]. The Plant Genome, 2021, 14(2):e20088. doi:10.1002/tpg2.20088. doi: 10.1002/tpg2.20088
[88]	Fikere M, Barbulescu D M, Malmberg M M, Maharjan P, Salisbury P A, Kant S, Panozzo J, Norton S, Spangenberg G C, Cogan N O I, Daetwyler H D. Genomic prediction and genetic correlation of agronomic,blackleg disease,and seed quality traits in canola(Brassica napus L.)[J]. Plants, 2020, 9(6):719. doi:10.3390/plants9060719. doi: 10.3390/plants9060719 URL
[89]	Li L, Long Y, Zhang L B, Dalton-Morgan J, Batley J, Yu L J, Meng J L, Li M T. Genome wide analysis of flowering time trait in multiple environments via high-throughput genotyping technique in Brassica napus L.[J]. PLoS One, 2015, 10(3):e0119425. doi:10.1371/journal.pone.0119425. doi: 10.1371/journal.pone.0119425
[90]	Zou J, Zhao Y S, Liu P F, Shi L, Wang X H, Wang M, Meng J L, Reif J C. Seed quality traits can be predicted with high accuracy in Brassica napus using genomic data[J]. PLoS One, 2016, 11(11):e0166624. doi:10.1371/journal.pone.0166624. doi: 10.1371/journal.pone.0166624
[91]	Liu P F, Zhao Y S, Liu G Z, Wang M, Hu D D, Hu J, Meng J L, Reif J C, Zou J. Hybrid performance of an immortalized F₂ rapeseed population is driven by additive,dominance,and epistatic effects[J]. Frontiers in Plant Science, 2017, 8:815. doi:10.3389/fpls.2017.00815. doi: 10.3389/fpls.2017.00815 URL
[92]	Wang Q, Yan T, Long Z B, Huang L Y, Zhu Y, Xu Y, Chen X Y, Pak H, Li J Q, Wu D Z, Xu Y, Hua S J, Jiang L X. Prediction of heterosis in the recent rapeseed(Brassica napus)polyploid by pairing parental nucleotide sequences[J]. PLoS Genetics, 2021, 17(11):e1009879. doi:10.1371/journal.pgen.1009879. doi: 10.1371/journal.pgen.1009879
[93]	Luo Z L, Wang M, Long Y, Huang Y J, Shi L, Zhang C Y, Liu X, Fitt B D L, Xiang J X, Mason A S, Snowdon R J, Liu P F, Meng J L, Zou J. Incorporating pleiotropic quantitative trait loci in dissection of complex traits:Seed yield in rapeseed as an example[J]. Theoretical and Applied Genetics, 2017, 130(8):1569-1585. doi:10.1007/s00122-017-2911-7. doi: 10.1007/s00122-017-2911-7 URL
[94]	Hu D D, Zhao Y S, Shen J X, He X X, Zhang Y K, Jiang Y, Snowdon R, Meng J L, Reif J C, Zou J. Genome-wide prediction for hybrids between parents with distinguished difference on exotic introgressions in Brassica napus[J]. The Crop Journal, 2021, 9(5):1169-1178. doi:10.1016/j.cj.2020.11.002. doi: 10.1016/j.cj.2020.11.002 URL
[95]	Werner C R, Voss-Fels K P, Miller C N, Qian W, Hua W, Guan C Y, Snowdon R J, Qian L. Effective genomic selection in a narrow-genepool crop with low-density markers:Asian rapeseed as an example[J]. The Plant Genome, 2018, 11(2):170084. doi:10.3835/plantgenome2017.09.0084. doi: 10.3835/plantgenome2017.09.0084
[96]	Pocrnic I, Lourenco D A L, Masuda Y, Misztal I. Dimensionality of genomic information and performance of the algorithm for proven and young for different livestock species[J]. Genetics,Selection,Evolution, 2016, 48(1):82. doi:10.1186/s12711-016-0261-6. doi: 10.1186/s12711-016-0261-6
[97]	Jenko J, Gorjanc G, Cleveland M A, Varshney R K, Whitelaw C B A, Woolliams J A, Hickey J M. Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs[J]. Genetics,Selection,Evolution, 2015, 47(1):55. doi:10.1186/s12711-015-0135-3. doi: 10.1186/s12711-015-0135-3
[98]	Jighly A, Lin Z B, Pembleton L W, Cogan N O I, Spangenberg G C, Hayes B J, Daetwyler H D. Boosting genetic gain in allogamous crops via speed breeding and genomic selection[J]. Frontiers in Plant Science, 2019, 10:1364. doi:10.3389/fpls.2019.01364. doi: 10.3389/fpls.2019.01364 URL

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