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用于作物改良的基因分型平台的革命

Revolution in Genotyping Platforms for Crop Improvement.

作者信息

Scheben Armin, Batley Jacqueline, Edwards David

机构信息

School of Biological Sciences, University of Western Australia, Crawley, WA, Australia.

Institute of Agriculture, University of Western Australia, Crawley, WA, Australia.

出版信息

Adv Biochem Eng Biotechnol. 2018;164:37-52. doi: 10.1007/10_2017_47.

Abstract

In the past decade, the application of high-throughput sequencing to crop genotyping has given rise to novel platforms capable of genotyping tens of thousands of genome-wide DNA markers. Coupled with the decreasing costs of sequencing, this rapid increase in markers allows accelerated and highly accurate genotyping of entire crop populations and diversity sets using single nucleotide polymorphisms (SNPs). These revolutionary advances accelerate crop improvement by facilitating a more precise connection of phenotype to genotype through association studies, linkage mapping and diversity analysis. The platforms driving the advances in genotyping are array technologies and genotyping by sequencing (GBS) methods, which include both low-coverage whole genome resequencing (skim sequencing) and reduced representation sequencing (RRS) approaches. Here, we outline and compare these genotyping platforms and provide a perspective on the promising future of crop genotyping. While SNP arrays provide high quality, simple handling, and unchallenging analysis, the lower cost of RRS and the greater data volume produced by skim sequencing suggest that use of GBS will become more prevalent in crop genomics as sequencing costs decrease and data analysis becomes more streamlined. Graphical Abstract.

摘要

在过去十年中,高通量测序技术在作物基因分型中的应用催生了能够对数万个全基因组DNA标记进行基因分型的新型平台。随着测序成本的不断降低,标记数量的迅速增加使得利用单核苷酸多态性(SNP)对整个作物群体和多样性集进行加速且高度准确的基因分型成为可能。这些革命性的进展通过关联研究、连锁图谱构建和多样性分析,促进了表型与基因型之间更精确的联系,从而加速了作物改良。推动基因分型进展的平台是阵列技术和测序基因分型(GBS)方法,其中包括低覆盖度全基因组重测序(快速测序)和简化基因组测序(RRS)方法。在此,我们概述并比较这些基因分型平台,并对作物基因分型充满希望的未来提供一个观点。虽然SNP阵列提供了高质量、易于操作且分析简单的特点,但RRS的低成本以及快速测序产生的更大数据量表明,随着测序成本的降低和数据分析变得更加简化,GBS在作物基因组学中的应用将变得更加普遍。图形摘要。

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