玉米复杂性状的全基因组关联研究发现提高籽粒产量和品质的基因组区域和基因

Yheni Dwiningsih, S.Si., M.Si., Ph.D
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引用次数: 1

摘要

本文综述了玉米主要作物全基因组关联研究(GWAS)的现状,重点介绍了关联定位作为关联遗传性状和复杂性状的新方法,以及利用表型和基因型数据分析鉴定群体结构和连锁不平衡的最新策略。GWAS在粮食安全方面具有重要作用,因为这种方法确定了世界上最商业化作物(如玉米)重要性状的许多关键基因组区域。这些复杂的性状包括产量、粮食品质、生物强化、生物和非生物抗性。GWAS具有降低基因分型成本和研究时间、提高定位分辨率和增加等位基因数量等优点。同时,GWAS在群体大小和标记数量方面存在两个主要限制。GWAS中有许多用于数据分析的软件包。GWAS中最常用的软件是TASSEL,因为它经常更新。近年来,许多研究论文集中在玉米的GWAS上。GWAS分析加速了玉米遗传区域的鉴定,候选基因及其代谢组学分析与玉米的复杂性状相关,以提高籽粒产量和品质,满足市场需求。
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 Study of Complex Traits in Maize Detects Genomic Regions and Genes for Increasing Grain Yield and Grain Quality
This review describes the current status of genome-wide association study (GWAS) of the major crops in maize (Zea mays L.) concentrate on performing association mapping as a novel method in associating genetic and complex traits, current strategy in analyzing of phenotype and genotype data to identify population structure and linkage disequilibrium. GWAS has an important role in food security because this method identified many crucial genomic regions of important traits in the most commercialize crops of the world, such as maize. These complex traits including yield, grain quality, biofortification, biotic and abiotic resistance. GWAS has many advantages correlated with reducing genotyping cost and research time, increasing mapping resolution and larger allele number. Meanwhile, GWAS has two main limitations related to population size and the number of markers. There are many software packages for data analysis in GWAS. The most commonly software that was used in GWAS especially in this crop is TASSEL because frequently updated. Recently, many research papers concentrated on GWAS in maize. GWAS analysis accelerated identification of genetic regions, candidate genes within these genomic regions and their metabolomic analysis correlated to the complex traits in maize for increasing grain yield and grain quality to fulfill the market demands.
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