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利用全基因组关联图谱鉴定稻米中矿质元素的遗传基础。

Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping.

机构信息

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.

Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA.

出版信息

Genes (Basel). 2022 Dec 10;13(12):2330. doi: 10.3390/genes13122330.

Abstract

Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements (Cu, Fe, K, Mg, Mn, and Zn) by using three models for single-locus and six models for multi-locus analysis of a genome-wide association study (GWAS) using 174 diverse rice accessions and 6565 SNP markers. To declare a SNP as significant, -log10(P) ≥ 3.0 and 15% FDR significance cut-off values were used for single-locus models, while LOD ≥ 3.0 was used for multi-locus models. Using these criteria, 147 SNPs were detected by one or two GWAS methods at -log10(P) ≥ 3.0, 48 of which met the 15% FDR significance cut-off value. Single-locus models outperformed multi-locus models before applying multi-test correction, but once applied, multi-locus models performed better. While 14 (~29%) of the identified quantitative trait loci (QTLs) after multiple test correction co-located with previously reported genes/QTLs and marker associations, another 34 trait-associated SNPs were novel. After mining genes within 250 kb of the 48 significant SNP loci, in silico and gene enrichment analyses were conducted to predict their potential functions. These shortlisted genes with their functions could guide future experimental validation, helping us to understand the complex molecular mechanisms controlling rice grain mineral elements.

摘要

矿物质营养不良是许多稻米消费国的一个主要问题。了解稻米中矿物质元素积累的遗传机制对于解决这个问题至关重要。本研究通过使用全基因组关联研究(GWAS)的三种单基因座模型和六种多基因座模型,对 174 种不同的水稻品种和 6565 个 SNP 标记进行分析,以确定六种矿物质元素(Cu、Fe、K、Mg、Mn 和 Zn)的遗传基础。为了宣布一个 SNP 是显著的,单基因座模型使用 -log10(P) ≥ 3.0 和 15% FDR 显著性截止值,而多基因座模型使用 LOD ≥ 3.0。使用这些标准,通过一种或两种 GWAS 方法在 -log10(P) ≥ 3.0 处检测到 147 个 SNP,其中 48 个符合 15% FDR 显著性截止值。在应用多测试校正之前,单基因座模型优于多基因座模型,但一旦应用,多基因座模型表现更好。在经过多重测试校正后,14 个(约 29%)鉴定的数量性状基因座(QTL)与先前报道的基因/QTL 和标记关联重合,另外 34 个与性状相关的 SNP 是新的。在挖掘 48 个显著 SNP 位点 250 kb 范围内的基因后,进行了计算机和基因富集分析,以预测它们的潜在功能。这些经过筛选的基因及其功能可以指导未来的实验验证,帮助我们理解控制水稻谷物矿物质元素的复杂分子机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68cc/9777918/4c45c84963d5/genes-13-02330-g001.jpg

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