玉米籽粒氮含量遗传位点挖掘及候选基因预测
作者:
作者单位:

1.河南省农业科学院农作物种质资源研究所,郑州 450002;2.河南大学生命科学学院,开封 475001

作者简介:

研究方向为玉米种质资源和遗传育种,E-mail: wanglifeng625@126.com

通讯作者:

李会勇,研究方向为玉米种质资源,E-mail : lihuiyong1977@126.com

中图分类号:

基金项目:

国家重点研发计划(2021YFD1200703);河南省科技攻关计划(222102110471,232102111103);河南省农业科学院科技创新团队项目(2024TD19);河南省农业科学院自主创新项目(2023ZC009)


Exploration of Elite Genetic Loci for Grain Nitrogen Content and Prediction of Candidate Genes in Maize
Author:
Affiliation:

1.Crop Germplasm Research Institute, Henan Academy of Agricultural Sciences, Zhengzhou 450002;2.School of Life Sciences, Henan University, Kaifeng 475001

Fund Project:

Foundation projects: National Key Research and Development Program of China(2021YFD1200703); Henan Province Science and Technology Research Program Project(222102110471,232102111103); Technology Innovation Team Project of Henan Academy of Agricultural Sciences (2024TD19); Independent Innovation Project of Henan Academy of Agricultural Sciences (2023ZC009)

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    摘要:

    玉米籽粒氮含量与品质紧密相关,其遗传机制的解析对玉米品质育种具有重要意义。本研究以252份玉米自交系为关联群体,利用贝叶斯信息与连锁不平衡迭代嵌套式模型(BLINK,bayesian-information and linkage-disequilibrium iteratively nested keyway)、固定随机循环概率模型(FarmCPU,fixed and random model circulating probability unification)、一般线性模型(GLM,general linear model)、混合线性模型(MLM,mixed linear model)、多位点混合线性模型(MLMM,multiple loci mixed model)和逐步排它性混合线性模型(SUPER,settlement of MLM under progressively exclusive relationship)等方法分别对其籽粒氮含量进行全基因组关联分析。共鉴定到13个与籽粒氮含量显著关联的SNP(P<3.64E-07)。BLINK、FarmCPU、GLM、MLM、MLMM和SUPER方法分别检测到6个、3个、7个、4个、2个和4个SNP位点。其中,S3_8879213在5种方法中均能检测到,S9_146173702在4种方法中均能检测到,S5_114774030和S7_182217338在3种方法中均能检测到,S1_10906326和S1_177528813 在2种方法中均能检测到。共挖掘25个相关候选基因,其中Zm00001eb275080Zm00001eb330700可能是影响玉米籽粒氮含量的重要候选基因。

    Abstract:

    Nitrogen content in maize grains is closely related to maize quality, and the analysis of its genetic mechanism is great significance for maize quality breeding. In this study, we used 252 maize inbred lines as an association population, and used bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK), fixed and random model circulating probability unification (FarmCPU), general linear model (GLM), mixed linear model (MLM), multiple loci mixed model (MLMM), and settlement of MLMs under progressively exclusive relationship (SUPER) to conduct genome-wide association analysis for grain nitrogen content. A total of thirteen SNPS (P<3.64E-07) were identified. Six, three, seven, four, two and four SNPs were detected by BLINK, FarmCPU, GLM, MLM, MLMM and SUPER methods, respectively. S3_ 8879213 can be detected in five methods, S9_146173702 can be detected in four methods, S5_114774030 and S7_ 182217338 can be detected in three methods, S1_10906326, and S1_177528813 can be detected in two methods. A total of twenty-five candidate genes were identified, among which Zm00001eb275080 and Zm00001eb330700 may be the important candidate genes affecting maize grain nitrogen content.

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引用本文

王利锋,王森,蔡齐宗,等.玉米籽粒氮含量遗传位点挖掘及候选基因预测[J].植物遗传资源学报,2024,25(9):1540-1551.

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  • 收稿日期:2023-12-27
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  • 在线发布日期: 2024-09-02
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