• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

植物育种中的基因组选择

Genomic selection in plant breeding.

作者信息

Newell Mark A, Jannink Jean-Luc

机构信息

The Samuel Roberts Noble Foundation, Ardmore, OK, USA.

出版信息

Methods Mol Biol. 2014;1145:117-30. doi: 10.1007/978-1-4939-0446-4_10.

DOI:10.1007/978-1-4939-0446-4_10
PMID:24816664
Abstract

Genomic selection (GS) is a method to predict the genetic value of selection candidates based on the genomic estimated breeding value (GEBV) predicted from high-density markers positioned throughout the genome. Unlike marker-assisted selection, the GEBV is based on all markers including both minor and major marker effects. Thus, the GEBV may capture more of the genetic variation for the particular trait under selection.

摘要

基因组选择(GS)是一种基于从遍布整个基因组定位的高密度标记预测的基因组估计育种值(GEBV)来预测选择候选个体遗传值的方法。与标记辅助选择不同,GEBV基于所有标记,包括微效和主效标记效应。因此,GEBV可能会捕获更多所选特定性状的遗传变异。

相似文献

1
Genomic selection in plant breeding.植物育种中的基因组选择
Methods Mol Biol. 2014;1145:117-30. doi: 10.1007/978-1-4939-0446-4_10.
2
Invited review: Genomic selection in dairy cattle: progress and challenges.特邀综述:奶牛的基因组选择:进展与挑战
J Dairy Sci. 2009 Feb;92(2):433-43. doi: 10.3168/jds.2008-1646.
3
Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters.在不同性状和基因组参数下,基因组估计育种值与传统最佳线性无偏预测(BLUP)估计育种值的准确性及选择反应的比较。
J Anim Breed Genet. 2007 Dec;124(6):342-55. doi: 10.1111/j.1439-0388.2007.00700.x.
4
Genomic selection: genome-wide prediction in plant improvement.基因组选择:植物改良中的全基因组预测。
Trends Plant Sci. 2014 Sep;19(9):592-601. doi: 10.1016/j.tplants.2014.05.006. Epub 2014 Jun 23.
5
The efficiency of genome-wide selection for genetic improvement of net merit.全基因组选择在遗传改良净效益方面的效率。
J Anim Sci. 2011 Oct;89(10):2972-80. doi: 10.2527/jas.2009-2606. Epub 2011 Apr 21.
6
Genomic selection of seed weight based on low-density SCAR markers in soybean.基于低密度SCAR标记的大豆种子重量基因组选择
Genet Mol Res. 2013 Jul 3;12(3):2178-88. doi: 10.4238/2013.July.3.2.
7
Long-term selection strategies for complex traits using high-density genetic markers.利用高密度遗传标记进行复杂性状的长期选择策略。
J Dairy Sci. 2012 Aug;95(8):4646-56. doi: 10.3168/jds.2011-5289.
8
Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations.低密度芯片标记信息导入对荷斯坦牛群体基因组育种值可靠性的影响。
J Dairy Sci. 2011 Jul;94(7):3679-86. doi: 10.3168/jds.2011-4299.
9
Practical Workflow from High-Throughput Genotyping to Genomic Estimated Breeding Values (GEBVs).高通量基因分型到基因组估计育种值(GEBVs)的实用工作流程。
Methods Mol Biol. 2021;2264:119-135. doi: 10.1007/978-1-0716-1201-9_9.
10
Does genomic selection have a future in plant breeding?基因组选择在植物育种中有未来吗?
Trends Biotechnol. 2013 Sep;31(9):497-504. doi: 10.1016/j.tibtech.2013.06.003. Epub 2013 Jul 16.

引用本文的文献

1
Machine learning solutions for integrating partially overlapping genetic datasets and modelling host-endophyte effects in ryegrass () dry matter yield estimation.用于整合部分重叠遗传数据集并模拟黑麦草宿主-内生菌效应以估计干物质产量的机器学习解决方案。
Front Plant Sci. 2025 May 6;16:1543956. doi: 10.3389/fpls.2025.1543956. eCollection 2025.
2
Micronutrient Biofortification in Wheat: QTLs, Candidate Genes and Molecular Mechanism.小麦中的微量营养素生物强化:数量性状基因座、候选基因与分子机制
Int J Mol Sci. 2025 Feb 28;26(5):2178. doi: 10.3390/ijms26052178.
3
Agricultural landscape genomics to increase crop resilience.
农业景观基因组学以增强作物适应力。
Plant Commun. 2025 Feb 10;6(2):101260. doi: 10.1016/j.xplc.2025.101260. Epub 2025 Jan 22.
4
Polyploidization and genomic selection integration for grapevine breeding: a perspective.葡萄育种中的多倍体化与基因组选择整合:展望
Front Plant Sci. 2023 Nov 15;14:1248978. doi: 10.3389/fpls.2023.1248978. eCollection 2023.
5
Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery.利用无人机多光谱影像对湿地松进行多时期表型选择。
Front Plant Sci. 2023 Aug 21;14:1156430. doi: 10.3389/fpls.2023.1156430. eCollection 2023.
6
Genome-Wide Association Studies Using 3VmrMLM Model Provide New Insights into Branched-Chain Amino Acid Contents in Rice Grains.使用3VmrMLM模型的全基因组关联研究为水稻籽粒中支链氨基酸含量提供了新见解。
Plants (Basel). 2023 Aug 17;12(16):2970. doi: 10.3390/plants12162970.
7
Breeding techniques to dispense higher genetic gains.能带来更高遗传增益的育种技术。
Front Plant Sci. 2023 Jan 19;13:1076094. doi: 10.3389/fpls.2022.1076094. eCollection 2022.
8
The era of cultivating smart rice with high light efficiency and heat tolerance has come of age.培育高光效、耐热智能水稻的时代已经到来。
Front Plant Sci. 2022 Oct 7;13:1021203. doi: 10.3389/fpls.2022.1021203. eCollection 2022.
9
Breeding and Genomics Interventions for Developing Ascochyta Blight Resistant Grain Legumes.培育和基因组学干预措施在开发抗壳二孢叶斑病的豆科作物中的应用。
Int J Mol Sci. 2022 Feb 17;23(4):2217. doi: 10.3390/ijms23042217.
10
Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize.基于基因组学辅助的小粒谷物和玉米数量抗病性育种
Int J Mol Sci. 2020 Dec 19;21(24):9717. doi: 10.3390/ijms21249717.