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利用关联作图解析植物复杂性状的遗传基础。

Using association mapping to dissect the genetic basis of complex traits in plants.

机构信息

Department of Ecology and Environmental Science, Umeå Plant Science Centre, Umeå University, Sweden.

出版信息

Brief Funct Genomics. 2010 Mar;9(2):157-65. doi: 10.1093/bfgp/elp048. Epub 2010 Jan 6.

Abstract

Association or linkage disequilibrium mapping has become a very popular method for dissecting the genetic basis of complex traits in plants. The benefits of association mapping, compared with traditional quantitative trait locus mapping, is, for example, a relatively detailed mapping resolution and that it is far less time consuming since no mapping populations need to be generated. The surge of interest in association mapping has been fueled by recent developments in genomics that allows for rapid identification and scoring of genetic markers which has traditionally limited mapping experiments. With the decreasing cost of genotyping future emphasis will likely focus on phenotyping, which can be both costly and time consuming but which is crucial for obtaining reliable results in association mapping studies. In addition, association mapping studies are prone to the identification of false positives, especially if the experimental design is not rigorously controlled. For example, population structure has long been known to induce many false positives and accounting for population structure has become one of the main issues when implementing association mapping in plants. Also, with increasing numbers of genetic markers used, the problem becomes separating true from false positive and this highlights the need for independent validation of identified association. With these caveats in mind, association mapping nevertheless shows great promise for helping us understand the genetic basis of complex traits of both economic and ecological importance.

摘要

关联或连锁不平衡作图已成为解析植物复杂性状遗传基础的一种非常流行的方法。与传统的数量性状位点作图相比,关联作图的优势在于,例如,相对较详细的作图分辨率,并且由于不需要生成作图群体,因此耗时较少。由于基因组学的快速发展,允许快速识别和评分遗传标记,这在传统上限制了作图实验,关联作图的兴趣激增。随着基因分型成本的降低,未来的重点可能将放在表型分析上,表型分析既昂贵又耗时,但对于获得关联作图研究中的可靠结果至关重要。此外,关联作图研究容易识别假阳性,尤其是如果实验设计没有严格控制的话。例如,种群结构长期以来一直被认为会导致许多假阳性,并且在植物中实施关联作图时,考虑种群结构已成为主要问题之一。此外,随着越来越多的遗传标记被使用,问题变成了如何将真阳性与假阳性区分开来,这凸显了对所识别的关联进行独立验证的必要性。考虑到这些警告,关联作图仍然显示出很大的希望,可以帮助我们了解具有经济和生态重要性的复杂性状的遗传基础。

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