作物全基因组关联研究展望

Shreesha Uprety
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摘要

全基因组关联研究(GWAS)测试了许多基因组中的数十万个遗传变异,以发现与特定性状或疾病相关的统计数据。对生物抗性、非生物耐受性、产量相关特性和代谢组成的研究进行了全面综述。全基因组SNP图谱揭示了与地理起源和形态类型有关的种群结构特征,并确定了古代作物向不同农业气候区扩散的模式。为了更好地了解作物多样化的基因组模式,对整个基因组的核苷酸多样性变异、连锁不平衡和重组率进行了量化。GWAS的结果可用于一系列应用。GWAS方法已被证明非常适合于鉴定常见的基于snp的变异,对表型有中等到显著的影响。然而,其中一些关联背后的遗传因素已经被确定。绝大多数仍未得到解释。下一代测序和生物信息学工具的发展已经得到了极大的改善,目前正在用于破译目标性状的遗传多样性。重大的缺点是需要庞大的人口规模,准备DNA样本的成本,以及对质量风险的了解较少。为了克服这一缺陷,研究人员对统计方法进行了升级,对基因型进行了适当的插入,并采用了嵌套关联作图和候选基因关联作图等先进方法。主要的好处是在不同的环境条件下进行一次基因分型和重复表型分型。j:。科学。Biotechnol。Vol 11(2): 54-59。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Prospect for Genome Wide Association Studies in Crops
Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. A Comprehensive review of studies on biotic resistance, abiotic tolerance, yield-associated characteristics, and metabolic composition is provided. Genome-wide SNP maps have characterized population structure concerning the geographic origin and morphological type and identified patterns of ancient crop diffusion to diverse agroclimatic regions. To better understand the genomic patterns of crop diversification, nucleotide diversity variation, linkage disequilibrium, and recombination rates across the genome are quantified. Results from GWAS can be used for a range of applications. The GWAS approach has proven highly suitable for identifying common SNP-based variants with moderate to significant effects on phenotype. However, the genetic factors underlying some of these associations have been characterized. The vast majority remain unexplained. The development of next-generation sequencing and bioinformatics tools has dramatically improved and is currently being implemented to decipher the genetic diversity of targeted traits. The significant drawbacks are the need for large population size, the cost of preparing DNA samples, and less knowledge about the risk of the quality. To overcome this drawback, researchers have upgraded the statistical approaches, proper imputation of genotypes, and advanced approaches such as nested association mapping and candidate gene association mapping. The primary benefit is one-time genotyping and repeated phenotyping in different environmental conditions. Int. J. Appl. Sci. Biotechnol. Vol 11(2): 54-59.
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