Association mapping in plants in the post-GWAS genomics era.

4区 生物学 Q2 Biochemistry, Genetics and Molecular Biology
Advances in Genetics Pub Date : 2019-01-01 Epub Date: 2019-01-22 DOI:10.1016/bs.adgen.2018.12.001
Pushpendra K Gupta, Pawan L Kulwal, Vandana Jaiswal
{"title":"Association mapping in plants in the post-GWAS genomics era.","authors":"Pushpendra K Gupta,&nbsp;Pawan L Kulwal,&nbsp;Vandana Jaiswal","doi":"10.1016/bs.adgen.2018.12.001","DOIUrl":null,"url":null,"abstract":"<p><p>With the availability of DNA-based molecular markers during early 1980s and that of sophisticated statistical tools in late 1980s and later, it became possible to identify genomic regions that control a quantitative trait. The two methods used for this purpose included quantitative trait loci (QTL) interval mapping and genome-wide association mapping/studies (GWAS). Both these methods have their own merits and demerits, so that newer approaches were developed in order to deal with the demerits. We have now entered a post-GWAS era, where either the original data on individual genotypes are being used again keeping in view the results of GWAS or else summary statistics obtained through GWAS is subjected to further analysis. The first half of this review briefly deals with the approaches that were used for GWAS, the GWAS results obtained in some major crops (maize, wheat, rice, sorghum and soybean), their utilization for crop improvement and the improvements made to address the limitations of original GWA studies (computational demand, multiple testing and false discovery, rare marker alleles, etc.). These improvements included the development of multi-locus and multi-trait analysis, joint linkage association mapping, etc. Since originally GWA studies were used for mere identification of marker-trait association for marker-assisted selection, the second half of the review is devoted to activities in post-GWAS era, which include different methods that are being used for identification of causal variants and their prioritization (meta-analysis, pathway-based analysis, methylation QTL), functional characterization of candidate signals, gene- and gene-set based association mapping, GWAS using high dimensional data through machine learning, etc. The last section deals with popular resources available for GWAS in plants in the post-GWAS era and the implications of the results of post-GWAS for crop improvement.</p>","PeriodicalId":50949,"journal":{"name":"Advances in Genetics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/bs.adgen.2018.12.001","citationCount":"80","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/bs.adgen.2018.12.001","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/1/22 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 80

Abstract

With the availability of DNA-based molecular markers during early 1980s and that of sophisticated statistical tools in late 1980s and later, it became possible to identify genomic regions that control a quantitative trait. The two methods used for this purpose included quantitative trait loci (QTL) interval mapping and genome-wide association mapping/studies (GWAS). Both these methods have their own merits and demerits, so that newer approaches were developed in order to deal with the demerits. We have now entered a post-GWAS era, where either the original data on individual genotypes are being used again keeping in view the results of GWAS or else summary statistics obtained through GWAS is subjected to further analysis. The first half of this review briefly deals with the approaches that were used for GWAS, the GWAS results obtained in some major crops (maize, wheat, rice, sorghum and soybean), their utilization for crop improvement and the improvements made to address the limitations of original GWA studies (computational demand, multiple testing and false discovery, rare marker alleles, etc.). These improvements included the development of multi-locus and multi-trait analysis, joint linkage association mapping, etc. Since originally GWA studies were used for mere identification of marker-trait association for marker-assisted selection, the second half of the review is devoted to activities in post-GWAS era, which include different methods that are being used for identification of causal variants and their prioritization (meta-analysis, pathway-based analysis, methylation QTL), functional characterization of candidate signals, gene- and gene-set based association mapping, GWAS using high dimensional data through machine learning, etc. The last section deals with popular resources available for GWAS in plants in the post-GWAS era and the implications of the results of post-GWAS for crop improvement.

后gwas基因组时代的植物关联图谱。
随着20世纪80年代初基于dna的分子标记的出现和80年代末及以后复杂的统计工具的出现,鉴定控制数量性状的基因组区域成为可能。用于此目的的两种方法包括数量性状位点(QTL)区间定位和全基因组关联定位/研究(GWAS)。这两种方法都有各自的优点和缺点,为了克服它们的缺点,需要开发新的方法。我们现在已经进入了后GWAS时代,在GWAS结果的基础上再次使用单个基因型的原始数据,或者通过GWAS获得的汇总统计数据进行进一步分析。本综述的前半部分简要介绍了用于GWAS的方法,在一些主要作物(玉米、小麦、水稻、高粱和大豆)上获得的GWAS结果,它们在作物改良中的应用,以及为解决原始GWA研究的局限性(计算需求、多重测试和错误发现、罕见标记等位基因等)所做的改进。这些改进包括多位点和多性状分析、联合连锁关联图谱的发展等。由于最初的GWA研究仅用于标记辅助选择的标记-性状关联鉴定,因此该综述的第二部分致力于后gwas时代的活动,其中包括用于鉴定因果变异及其优先级的不同方法(荟萃分析,基于途径的分析,甲基化QTL),候选信号的功能表征,基于基因和基因集的关联作图,GWAS通过机器学习等方式使用高维数据。最后一节讨论了后GWAS时代植物中GWAS可用的流行资源以及后GWAS结果对作物改良的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Advances in Genetics
Advances in Genetics 生物-遗传学
CiteScore
5.70
自引率
0.00%
发文量
1
审稿时长
1 months
期刊介绍: Advances in Genetics presents an eclectic mix of articles of use to all human and molecular geneticists. They are written and edited by recognized leaders in the field and make this an essential series of books for anyone in the genetics field.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信