Suppr超能文献

玉米全基因组关联研究:赞美与展望。

Genome-wide Association Studies in Maize: Praise and Stargaze.

机构信息

National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.

Synergetic Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China.

出版信息

Mol Plant. 2017 Mar 6;10(3):359-374. doi: 10.1016/j.molp.2016.12.008. Epub 2016 Dec 27.

Abstract

Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype-phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.

摘要

全基因组关联研究(GWAS)得益于新一代测序(NGS)技术的进步,已成为解码许多物种基因型-表型关联的广泛接受的策略。玉米是 GWAS 的理想作物,在过去十年中取得了重大进展。本综述总结了玉米功能基因组学研究中当前的 GWAS 工作,并讨论了组学时代的未来前景。GWAS 的总体目标是使用给定群体中最合适的统计模型将基因型变异与表型的相应差异联系起来。本综述还提出了优化 GWAS 设计和分析的观点。讨论了基于 RNA、蛋白质和代谢物的组学研究数据的 GWAS 分析,以及新的模型和新的群体设计,这些将识别迄今为止隐藏的表型变异的原因。整个社区的共同和持续努力将增强我们对玉米数量性状的理解,并推动作物分子育种设计。

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