由田间和分子表型组成的农艺性状的全基因组关联研究

Q2 Agricultural and Biological Sciences
Yuki Nakano, Yuriko Kobayashi
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引用次数: 5

摘要

全基因组关联研究(GWAS)是鉴定种内表型变异的遗传因素的有力方法。DNA测序技术的最新进展,包括下一代测序,使我们能够轻松地对高密度全基因组snp进行基因分型。此外,近年来广泛收集了许多不同种类的植物资料。这些遗传资源使得GWAS成为一种明显更受欢迎的方法,用于研究在大群体中发生的各种性状的自然变异。除了基因分型技术外,高通量表型分型技术的进步使我们能够获得大量以各种性状为特征的材料的变异数据,不仅包括田间性状(如产量和抗病性),还包括分子性状(如基因表达水平和代谢物含量)。因此,有可能扩大GWAS的应用范围,提高基因组关联的检测能力。本文综述了近年来在田间和分子尺度上对各种农艺性状的GWAS研究进展,重点介绍了GWAS与转录组、离子组和代谢组等高通量表型技术的整合方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genome-wide Association Studies of Agronomic Traits Consisting of Field- and Molecular-based Phenotypes
Genome-wide association study (GWAS) is a powerful approach to identify the genetic factors underlying the intraspecific phenotypic variations. Recent advances in DNA sequencing technology, including next generation sequencing has enabled us to easily genotype high density genome-wide SNPs. In addition, many accessions of various plant species have been widely collected in recent years. These genetic resources have made GWAS a markedly more popular approach for investigation of natural variations occurring in various traits using large populations. In addition to genotyping technology, advances in high-throughput phenotyping technologies have enabled us to acquire variation data on a large number of accessions characterized for various traits, including not only the field traits (e.g., yield and disease resistance) but also molecular traits (e.g., gene expression level and metabolite content). Thus, it is possible to expand the range of application of GWAS and enhance the detection power of genomic association. In this review, we summarize recent GWAS of various agronomic traits at field and molecular scale, following which we highlight the integration approach involving GWAS and high-throughput phenotyping technologies including transcriptome, ionome and metabolome.
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来源期刊
Reviews in Agricultural Science
Reviews in Agricultural Science Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
4.60
自引率
0.00%
发文量
13
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