数据资源: 科信所期刊全文

林木全基因组选择研究现状和应用



编号 lyqk009524

中文标题 林木全基因组选择研究现状和应用

作者 张苗苗  王军辉  卢楠  麻文俊  王楠  吴夏明 

作者单位 1. 中国林业科学研究院林业研究所,北京 100091;
2. 林木遗传育种国家重点实验室,北京 100091;
3. 瑞典农业大学森林遗传和植物生理系于默亚植物科学中心,瑞典于默亚 SE-90183

期刊名称 世界林业研究 

年份 2021 

卷号 34

期号 4

栏目名称 专题论述 

中文摘要 全基因组选择(GS)是利用覆盖全基因组的高密度遗传标记对复杂数量性状进行预测的育种方法。在林木种苗阶段根据基因组估计育种值(GEBV)可以利用GS进行个体选择,相比常规育种能增强遗传增益、加快选育进程。该方法无需定位与性状相关的数量性状位点(QTL),相比分子标记辅助育种能极大地提高对微效位点的捕获功效,是具有巨大潜力的林木育种策略。文中系统介绍了GS的概念和优势,及其在林木中的研究现状和应用。我国林木GS研究处于初期阶段,可优先在常规育种较成熟的树种中开展研究,建立林木GS程序为其他树种提供范式。该综述有助于系统了解全基因组选择育种策略和研究进展,并为全基因组选择在林木育种中的应用提供理论和技术信息。

关键词 林木育种  全基因组选择  预测模型  基因组估计育种值  遗传增益 

基金项目 国家重点研发专项课题“楸树良种选育与高效培育技术研究”(2017YFD0600604);国家自然基金青年项目“基于动态QTL模型的楸树生长和表型可塑性对氮素响应的遗传解析”(32001337)

英文标题 Research Progress and Application of Whole Genome Selection in Forest Tree Breeding

作者英文名 Zhang Miaomiao, Wang Junhui, Lu Nan, Ma Wenjun, Wang Nan, Wu Harry

单位英文名 1. Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China;
2. State Key Laboratory of Tree Genetics and Breeding, Beijing 100091, China;
3. Ume? Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Ume? SE-90183, Sweden

英文摘要 As a modern breeding approach with great potential for trees, whole genomic selection is used to predict quantitative traits by using genome-wide marker data. It can be used for early individual selection based on the genomic estimated breeding value (GEBV) at seed and seedling stages, which can enhance genetic gain and accelerate the breeding process compared to conventional breeding. Meanwhile, it captures all genetic variance for the trait explained by the large numbers of small effects without QTLs identification, which can greatly improve the capture efficiency of polygenic traits compared to the molecular marker-assisted breeding. This study systematically introduces the conception and superiority of whole genomic selection and its research and application in tree breeding. Considering that the study on whole genomic selection in tree breeding is still at its infancy in China, it could start with the major tree species cultivated in conventional breeding, whose well-developed breeding program can provide a paradigm for other tree species. This review helps systematically understand the whole genomic selection breeding strategies and their research progress, and provides theoretical information and technical support for its application to tree breeding.

英文关键词 tree breeding;whole genomic selection;prediction model;genomic estimated breeding value;genetic gain

起始页码 26

截止页码 32

投稿时间 2020/10/28

最后修改时间 2021/1/7

作者简介 张苗苗,女,博士,助理研究员,研究方向为林木统计遗传学,E-mail:mmzhang@caf.ac.cn

通讯作者介绍 王军辉,男,研究员,博士生导师,研究方向为珍贵树种遗传改良,E-mail:wangjh@caf.ac.cn

E-mail mmzhang@caf.ac.cn;wangjh@caf.ac.cn

分类号 S722.3

DOI 10.13348/j.cnki.sjlyyj.2021.0001.y

参考文献 [1] RESENDE M F Jr, MUNOZ P, ACOSTA J J, et al. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments[J]. New Phytologist, 2012, 193(3):617-624.
[2] CROSSA J, PEREZ-RODRIGUEZ P, CUEVAS J, et al. Genomic selection in plant breeding: methods, models, and perspectives[J]. Trends in Plant Science, 2017, 22(11):961-975.
[3] GRATTAPAGLIA D, SILVA-JUNIOR O B, RESENDE R T, et al. Quantitative genetics and genomics converge to accelerate forest tree breeding[J]. Frontiers in Plant Science, 2018, 9:1693. DOI:10.3389/fpls.2018.01693
[4] WU H X, ELDRIDGE K G, MATHESON A C, et al. Achievement in forest tree improvement in Australia and New Zealand: 8. successful introduction and breeding of radiata pine to Australia[J]. Australian Forestry, 2007, 70:215-225.
[5] ISIK F, MCKEAND S E. Fourth-cycle breeding and testing strategy for Pinus taeda in the NC state university cooperative tree improvement program[J]. Tree Genetics and Genome, 2019, 15:70. DOI:10.1007/s11295-019-1377-y
[6] 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.
[7] XU Y, LIU X, FU J, et al. Enhancing genetic gain through genomic selection: from livestock to plants[J]. Plant Communications, 2020, 1(1):100005. DOI:10.1016/j.xplc.2019.100005
[8] HICKEY J M, CHIURUGWI T, MACKAY I, et al. Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery[J]. Nature Genetics, 2017, 49(9):1297-1303.
[9] RESENDE M D, RESENDE M F Jr, SANSALONI C P, et al. Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees[J]. New Phytologist, 2012, 194(1):116-128.
[10] RESENDE R T, RESENDE M D V, SILVA F F, et al. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model[J]. Heredity, 2017, 119(4):245-255.
[11] LI Y, DUNGEY H S. Expected benefit of genomic selection over forward selection in conifer breeding and deployment[J]. Plos One, 2018, 13(12):e0208232. DOI:10.1371/journal.pone.0208232
[12] WONG C K, BERNARDO R. Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations[J]. Theoretical and Applied Genetics, 2008, 116(6):815-824.
[13] RESENDE M F Jr, MUNOZ P, RESENDE M D, et al. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.)[J]. Genetics, 2012, 190(4):1503-1510.
[14] ZAPATA-VALENZUELA J, WHETTEN R W, NEALE D, et al. Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine[J]. G3 (Bethesda), 2013, 3(5):909-916.
[15] THISTLETHWAITE F R, RATCLIFFE B, KLAPSTE J, et al. Genomic selection of juvenile height across a single-generational gap in Douglas-fir[J]. Heredity, 2019, 122(6):848-863.
[16] IWATA H, HAYASHI T, TSUMURA Y. Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica[J]. Tree Genetics & Genomes, 2011, 7(4):747-758.
[17] GRATTAPAGLIA D, RESENDE M D V. Genomic selection in forest tree breeding[J]. Tree Genetics & Genomes, 2011, 7(2):241-255.
[18] SUONTAMA M, KLAPSTE J, TELFER E, et al. Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories[J]. Heredity, 2019, 122(3):370-379.
[19] LENZ P R N, NADEAU S, AZAIEZ A, et al. Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce[J]. Heredity, 2020, 124(4):562-578.
[20] CHEN Z Q, BAISON J, PAN J, et al. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce[J]. BMC Genomics, 2018, 19(1):946. DOI:10.1186/s12864-018-5256-y
[21] LENZ P R N, BEAULIEU J, MANSFIELD S D, et al. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana)[J]. BMC Genomics, 2017, 18(1):335. DOI:10.1186/s12864-017-3715-5
[22] VARSHNEY R K, ROORKIWAL M, SORRELLS M E. Genomic selection for crop improvement-new molecular breeding stategies improvement[M]. Switzerland: Springer Nature, 2017: 199-249.
[23] 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.
[24] BEAULIEU J, DOERKSEN T, CLEMENT S, et al. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce[J]. Heredity, 2014, 113(4):343-352.
[25] DENIS M, BOUVET J M. Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding[J]. Tree Genetics & Genomes, 2013, 9(1):37-51.
[26] TAN B, GRATTAPAGLIA D, MARTINS G S, et al. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids[J]. BMC Plant Biology, 2017, 17(1):110. DOI:10.1186/s12870-017-1059-6
[27] KAINER D, STONE E A, PADOVAN A, et al. Accuracy of genomic prediction for foliar terpene traits in Eucalyptus polybractea[J]. G3 (Bethesda), 2018, 8(8):2573-2583.
[28] EL-DIEN O G, RATCLIFFE B, KLAPSTE J, et al. Multienvironment genomic variance decomposition analysis of open-pollinated Interior spruce (Picea glauca × engelmannii)[J]. Molecular Breeding, 2018, 38(3):26. DOI:10.1007/s11032-018-0784-3
[29] SILVA-JUNIOR O B, FARIA D A, GRATTAPAGLIA D. A ?exible multi-species genome-wide 60K SNP chip developed from pooled resequencing 240 Eucalyptus tree genomes across 12 species[J]. New Phytologist, 2015, 206:1527-1540.
[30] GUO Z F, WANG H W, TAO J J, et al. Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize[J]. Molecular Breeding, 2019, 39(3):37. DOI:10.1007/s11032-019-0940-4
[31] LIMA B M. Bridging genomics and quantitative genetics of Eucalyptus: genome-wide prediction and genetic parameter estimation for growth and wood properties using high-density SNP data[D]. Lorena, Brazil: Universidade de S?o Paulo, 2014.
[32] DUNGEY H S, DASH J P, PONT D, et al. Phenotyping whole forests will help to track genetic performance[J]. Trends in Plant Science, 2018, 23:854-864.
[33] DE ALMEIDA FILHO J E, GUIMARAES J F R, FONSCECA E S F, et al. Genomic prediction of additive and non-additive effects using genetic markers and pedigrees[J]. G3 (Bethesda), 2019, 9(8):2739-2748.
[34] CAPPA E P, EL-KASSABY Y A, MUNOZ F, et al. Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers[J]. Plant Science, 2018, 271:27-33.
[35] LENZ P R N, NADEAU S, MOTTET M J, et al. Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce[J]. Evolutionary Applications, 2020, 13(1):76-94.

发布日期 2021-01-18

PDF全文 浏览全文

相关图谱

扫描二维码