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基于代谢组的植物全基因组关联研究:进展、挑战与展望。

-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives.

机构信息

Département de Phytologie, Université Laval, Quebec City, QC G1V 0A6, Canada.

Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC G1V 0A6, Canada.

出版信息

Genes (Basel). 2023 Jul 13;14(7):1439. doi: 10.3390/genes14071439.

Abstract

Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, -mer-based GWAS approaches have recently been developed. -mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, -mer-based analyses can be used in species that lack a reference genome. However, the use of -mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for -mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant -mers to sequence variation.

摘要

基因组关联研究(GWAS)在过去几十年中允许在作物中发现标记-性状关联。然而,它们的功能受到许多限制的阻碍,其中关键的限制之一是过度依赖单核苷酸多态性(SNP)作为分子标记。事实上,SNP 仅代表一种类型的遗传变异,通常来自于与单个基因组组装的比对,而该基因组组装可能不能很好地代表研究中的群体。为了克服这一点,最近开发了基于 -mer 的 GWAS 方法。基于 -mer 的 GWAS 提供了一种通用的方法来评估由于 SNP、插入/缺失和结构变异引起的变异,而无需专门检测和基因分型这些变体。此外,基于 -mer 的分析可用于缺乏参考基因组的物种。然而,使用 -mer 进行 GWAS 存在一些挑战,例如数据大小和复杂性、缺乏标准工具以及可能检测到虚假关联。尽管如此,正在努力克服这些挑战,并且已经开始出现通用的分析工作流程。我们确定了未来几年基于 -mer 的 GWAS 的优先事项,特别是在开发用于分析的用户友好型程序以及将显著的 -mer 与序列变异联系起来的方法方面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e05/10379394/9c1be9b1527d/genes-14-01439-g002.jpg

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