全基因组关联分析(GWAS)鉴定水稻氮素利用效率候选基因

doi:10.16819/j.1001-7216.2024.231010

中国水稻科学 ›› 2024, Vol. 38 ›› Issue (5): 516-524.DOI: 10.16819/j.1001-7216.2024.231010

全基因组关联分析(GWAS)鉴定水稻氮素利用效率候选基因

吕阳¹^,³, 刘聪聪³, 杨龙波³, 曹兴岚³, 王月影², 童毅², Mohamed Hazman⁴^,⁵, 钱前¹^,²^,³, 商连光³^,^*(), 郭龙彪²^,⁶^,^*()

¹沈阳农业大学水稻研究所, 沈阳 110161
²中国水稻研究所水稻生物育种全国重点实验室, 杭州 311401
³中国农业科学院农业基因组研究所,深圳 518120
⁴埃及农业研究中心农业遗传工程研究所, 埃及吉萨 12588
⁵尼罗河大学生物技术学院, 埃及谢赫·扎耶德 12588
⁶（深圳）生物育种技术有限公司, 深圳 518120

收稿日期:2023-10-26 修回日期:2023-12-08 出版日期:2024-09-10 发布日期:2024-09-10
通讯作者: *email: shanglianguang@caas.cn; guolongbiao@caas.cn
基金资助:
国家重点研发计划资助项目(2022YFE0139400);国家自然科学基金青年科学基金资助项目(32101718);深圳市科技计划资助项目(GJHZ20190821163601707);埃及科学、技术和创新基金管理局项目[the Science, Technology, and Innovation Funding Authority (STDF), Egypt under the grant: Chinese-Egyptian Research Fund(CERF,5^th call)Project ID at the Egyptian side: 46512]

Identification of Candidate Genes for Rice Nitrogen Use Efficiency by Genome-wide Association Analysis

LÜ Yang¹^,³, LIU Congcong³, YANG Longbo³, CAO Xinglan³, WANG Yueying², TONG Yi², Mohamed Hazman⁴^,⁵, QIAN Qian¹^,²^,³, SHANG Lianguang³^,^*(), GUO Longbiao²^,⁶^,^*()

¹Rice Research Institute, Shenyang Agricultural University, Shenyang 110161, China
²State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou 311401, China
³Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
⁴Agricultural Genetic Engineering Research Institute, Agricultural Research Center, Giza 12619, Egypt
⁵School of Biotechnology, Nile University (NU), Juhayna Square, 26th of July Corridor, El Sheikh Zayed 12588, Egypt
⁶CAAS Channel (Shenzhen) Biological Breeding Technology Co., Ltd., Shenzhen 518120, China

Received:2023-10-26 Revised:2023-12-08 Online:2024-09-10 Published:2024-09-10
Contact: *email: shanglianguang@caas.cn; guolongbiao@caas.cn

摘要/Abstract

摘要：

【目的】挖掘水稻氮高效的种质和基因资源，揭示其分子机制和遗传效应，是当前水稻氮素利用效率（NUE）研究的重要内容和目标。【方法】为了鉴定与水稻NUE相关的变异位点和候选基因，我们收集了190份亚洲稻为关联群体，通过质量过滤和群体频率过滤筛选出3,934,195个高质量的单核苷酸多态性（SNPs）位点，在大田条件下设置低氮（N₁，90 kg/hm²）和常规氮（N₂，180 kg/hm²）水平，成熟期调查水稻剑叶叶宽在低氮和常规氮处理下的表型数据，结合FarmCPU和MLM模型进行全基因组关联分析（GWAS）。【结果】通过植株在不同氮水平下的叶宽表型数据，计算该群体在低氮和常规氮水平下的剑叶宽表型比值Q（N₁／N₂），Q值呈现正态分布的特征。对Q值进行全基因组关联分析，在12条染色体上共鉴定了100个显著位点，确定了39个候选QTLs，包括已克隆NUE相关基因OsNR1.2和OsNAC42。进一步鉴定了候选基因OsNR1.2和OsNAC42的优异单倍型和潜在的优势单倍型组合，为水稻NUE的改良提供了有价值的资源和信息。【结论】利用GWAS和单倍型分析，揭示了水稻剑叶叶宽在不同氮处理下的遗传基础，鉴定了与NUE相关的候选QTLs和基因，包括OsNR1.2和OsNAC42。通过组合单倍型分析，鉴定了两个基因的优势单倍型组合，为水稻NUE的改良提供了有价值的资源和信息。

关键词: 水稻, 氮素利用效率, 叶宽, 全基因组关联分析

Abstract:

【Objective】 The exploration of germplasm and gene resources in rice for high nitrogen efficiency, along with the elucidation of their molecular mechanisms and genetic effects, represents a significant focus and goal within current research efforts on rice nitrogen use efficiency (NUE).【Method】 To identify the variant loci and candidate genes associated with rice NUE, we collected 190 Asian rice accessions as an association population. After thorough filtering and screening, we obtained 3,934,195 high-quality single nucleotide polymorphisms (SNPs). Under field conditions, two nitrogen treatment levels were established: low nitrogen (N₁, 90 kg/hm²) and normal nitrogen (N₂, 180 kg/hm²). We investigated the phenotypic data of rice leaf width in response to both low and normal nitrogen treatments at the maturity stage. Genome-wide association analysis (GWAS) was conducted by integrating the FarmCPU and MLM models. 【Result】By calculating the phenotypic ratio Q (N₁/N₂) of leaf width under low and normal nitrogen levels, we found that the Q value exhibited a normal distribution. A total of 100 significant loci were identified on 12 chromosomes through GWAS for the Q value, leading to the determination of 39 candidate QTLs. This included the cloned NUE-related genes OsNR1.2 and OsNAC42. Additionally, we identified superior haplotypes and potential advantageous haplotype combinations of the candidate genes OsNR1.2 and OsNAC42, which provide valuable resources and information for enhancing rice NUE. 【Conclusion】This study elucidated the genetic basis of rice leaf width under varying nitrogen treatments using GWAS and haplotype analysis. We identified candidate QTLs and genes associated with NUE, including OsNR1.2 and OsNAC42. Through haplotype analysis, we recognized advantageous haplotype combinations of these two genes, offering valuable resources and insights for the improvement of rice NUE.

Key words: rice, nitrogen use efficiency, leaf width, genome-wide association analysis

吕阳, 刘聪聪, 杨龙波, 曹兴岚, 王月影, 童毅, Mohamed Hazman, 钱前, 商连光, 郭龙彪. 全基因组关联分析(GWAS)鉴定水稻氮素利用效率候选基因[J]. 中国水稻科学, 2024, 38(5): 516-524.

LÜ Yang, LIU Congcong, YANG Longbo, CAO Xinglan, WANG Yueying, TONG Yi, Mohamed Hazman, QIAN Qian, SHANG Lianguang, GUO Longbiao. Identification of Candidate Genes for Rice Nitrogen Use Efficiency by Genome-wide Association Analysis[J]. Chinese Journal OF Rice Science, 2024, 38(5): 516-524.

图/表 6

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染色体 Chromosome	SNP覆盖距离 Coverage distance of SNP (bp)	SNP覆盖率 Coverage rate of SNP (%)	SNP数 SNP numbers
1	43,268,496	99.99	434,147
2	35,935,088	99.99	365,162
3	36,404,657	99.97	346,548
4	35,500,209	99.99	329,291
5	29,953,155	99.98	274,538
6	31,245,818	99.99	334,152
7	29,680,533	99.94	326,003
8	28,440,061	99.99	312,782
9	22,902,397	99.52	249,843
10	23,201,787	99.98	286,539
11	29,016,815	99.99	370,492
12	27,529,160	99.99	304,698
合计All	373,078,176	99.94	3,934,195

染色体 Chromosome	SNP覆盖距离 Coverage distance of SNP (bp)	SNP覆盖率 Coverage rate of SNP (%)	SNP数 SNP numbers
1	43,268,496	99.99	434,147
2	35,935,088	99.99	365,162
3	36,404,657	99.97	346,548
4	35,500,209	99.99	329,291
5	29,953,155	99.98	274,538
6	31,245,818	99.99	334,152
7	29,680,533	99.94	326,003
8	28,440,061	99.99	312,782
9	22,902,397	99.52	249,843
10	23,201,787	99.98	286,539
11	29,016,815	99.99	370,492
12	27,529,160	99.99	304,698
合计All	373,078,176	99.94	3,934,195

序号 Num.	QTLs	染色体 Chr.	物理区间 Position(bp)	模型 Model
1	qN1.1	1	16,711,341−17,842,922	FarmCPU
2	qN1.3	1	29,450,332−29,850,332	FarmCPU
3	qN2.1	2	5,202,109−5,634,866	FarmCPU
4	qN2.2	2	15,469,192−16,276,088	FarmCPU
5	qN2.3	2	21,668,095−22,329,457	FarmCPU
6	qN2.4	2	23,110,299−23,510,309	FarmCPU
7	qN3.1	3	19,114,795−19,514,795	FarmCPU
8	qN4.1	4	12,844,649−13,244,649	FarmCPU, MLM
9	qN4.2	4	17,659,810−18,662,950	FarmCPU
10	qN4.3	4	23,336,187−23,790,593	FarmCPU
11	qN4.4	4	24,729,681−25,129,681	FarmCPU
12	qN4.5	4	26,074,465−26,474,465	FarmCPU
13	qN4.6	4	30,147,389−30,547,389	FarmCPU
14	qN5.1	5	2,757,529−3,157,612	FarmCPU, MLM
15	qN5.2	5	16,539,268−16,939,268	FarmCPU
16	qN6.1	6	1,047,084−1,447,087	FarmCPU
17	qN6.2	6	7,235,155−7,635,155	FarmCPU
18	qN6.3	6	10,994,434−12,207,057	FarmCPU
19	qN6.4	6	14,631,833−15,031,833	FarmCPU
20	qN6.5	6	22,830,458−23,230,458	FarmCPU
21	qN7.1	7	10,738,028−11,138,028	FarmCPU
22	qN7.2	7	21,982,893−22,382,893	FarmCPU
23	qN7.3	7	24,335,116−24,735,851	FarmCPU, MLM
24	qN7.4	7	26,393,603−27,493,824	FarmCPU, MLM
25	qN8.1	8	832,784−1,288,754	FarmCPU
26	qN8.2	8	5,296,217−5,696,240	FarmCPU
27	qN8.3	8	11,161,050−11,771,793	FarmCPU, MLM
28	qN8.4	8	12,948,612−13,348,782	FarmCPU
29	qN8.5	8	18,290,616−18,717,167	FarmCPU
30	qN8.6	8	22,921,397−23,321,397	FarmCPU
31	qN9.1	9	18,943,064−19,344,087	FarmCPU, MLM
32	qN10.1	10	502,390−902,390	FarmCPU, MLM
33	qN10.2	10	6,859,536−7,259,536	FarmCPU
34	qN10.3	10	21,015,655−21,415,655	FarmCPU
35	qN11.1	11	5,033,674−5,433,674	FarmCPU
36	qN11.2	11	18,939,513−19,339,513	FarmCPU
37	qN11.3	11	26,603,335−27,005,127	FarmCPU
38	qN12.1	12	14,101,046−14,501,052	FarmCPU
39	qN12.2	12	16,046,475−16,446,475	FarmCPU

序号 Num.	QTLs	染色体 Chr.	物理区间 Position(bp)	模型 Model
1	qN1.1	1	16,711,341−17,842,922	FarmCPU
2	qN1.3	1	29,450,332−29,850,332	FarmCPU
3	qN2.1	2	5,202,109−5,634,866	FarmCPU
4	qN2.2	2	15,469,192−16,276,088	FarmCPU
5	qN2.3	2	21,668,095−22,329,457	FarmCPU
6	qN2.4	2	23,110,299−23,510,309	FarmCPU
7	qN3.1	3	19,114,795−19,514,795	FarmCPU
8	qN4.1	4	12,844,649−13,244,649	FarmCPU, MLM
9	qN4.2	4	17,659,810−18,662,950	FarmCPU
10	qN4.3	4	23,336,187−23,790,593	FarmCPU
11	qN4.4	4	24,729,681−25,129,681	FarmCPU
12	qN4.5	4	26,074,465−26,474,465	FarmCPU
13	qN4.6	4	30,147,389−30,547,389	FarmCPU
14	qN5.1	5	2,757,529−3,157,612	FarmCPU, MLM
15	qN5.2	5	16,539,268−16,939,268	FarmCPU
16	qN6.1	6	1,047,084−1,447,087	FarmCPU
17	qN6.2	6	7,235,155−7,635,155	FarmCPU
18	qN6.3	6	10,994,434−12,207,057	FarmCPU
19	qN6.4	6	14,631,833−15,031,833	FarmCPU
20	qN6.5	6	22,830,458−23,230,458	FarmCPU
21	qN7.1	7	10,738,028−11,138,028	FarmCPU
22	qN7.2	7	21,982,893−22,382,893	FarmCPU
23	qN7.3	7	24,335,116−24,735,851	FarmCPU, MLM
24	qN7.4	7	26,393,603−27,493,824	FarmCPU, MLM
25	qN8.1	8	832,784−1,288,754	FarmCPU
26	qN8.2	8	5,296,217−5,696,240	FarmCPU
27	qN8.3	8	11,161,050−11,771,793	FarmCPU, MLM
28	qN8.4	8	12,948,612−13,348,782	FarmCPU
29	qN8.5	8	18,290,616−18,717,167	FarmCPU
30	qN8.6	8	22,921,397−23,321,397	FarmCPU
31	qN9.1	9	18,943,064−19,344,087	FarmCPU, MLM
32	qN10.1	10	502,390−902,390	FarmCPU, MLM
33	qN10.2	10	6,859,536−7,259,536	FarmCPU
34	qN10.3	10	21,015,655−21,415,655	FarmCPU
35	qN11.1	11	5,033,674−5,433,674	FarmCPU
36	qN11.2	11	18,939,513−19,339,513	FarmCPU
37	qN11.3	11	26,603,335−27,005,127	FarmCPU
38	qN12.1	12	14,101,046−14,501,052	FarmCPU
39	qN12.2	12	16,046,475−16,446,475	FarmCPU

全基因组关联分析(GWAS)鉴定水稻氮素利用效率候选基因

Identification of Candidate Genes for Rice Nitrogen Use Efficiency by Genome-wide Association Analysis

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