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作物学报 ›› 2021, Vol. 47 ›› Issue (8): 1491-1510.doi: 10.3724/SP.J.1006.2021.04175

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

整合GWAS和WGCNA筛选鉴定甘蓝型油菜生物产量候选基因

王艳花1,2(), 刘景森1,2, 李加纳1,2,*()   

  1. 1西南大学农学与生物科技学院/油菜工程研究中心, 重庆 400715
    2西南大学现代农业科学研究院, 重庆 400715
  • 收稿日期:2020-07-30 接受日期:2020-12-01 出版日期:2021-08-12 网络出版日期:2021-01-11
  • 通讯作者: 李加纳
  • 作者简介:E-mail: hawer313@163.com
  • 基金资助:
    中国博士后科学基金面上项目(2019M653319);重庆市自然科学基金博士后科学基金项目(cstc2019jcyj-bshXO116);高等学校学科创新引智基地项目(“111”项目)(B12006)

Integrating GWAS and WGCNA to screen and identify candidate genes for biological yield in Brassica napus L.

WANG Yan-Hua1,2(), LIU Jing-Sen1,2, LI Jia-Na1,2,*()   

  1. 1College of Agronomy and Biotechnology, Southwest University/Chongqing Engineering Research Center for Rapeseed, Chongqing 400715, China
    2Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China
  • Received:2020-07-30 Accepted:2020-12-01 Published:2021-08-12 Published online:2021-01-11
  • Contact: LI Jia-Na
  • Supported by:
    Project of China Postdoctoral Science Foundation(2019M653319);Project of Chongqing Natural Science Foundation Postdoctoral Science Foundation(cstc2019jcyj-bshXO116);Project of Intellectual Base for Discipline Innovation in Colleges and Universities (“111” Project)(B12006)

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摘要：

生物产量是作物获得高产的重要基础, 对于甘蓝型油菜(Brassica napus L.)尤其重要。本研究利用588份甘蓝型油菜材料构成的自然群体2年生物产量表型数据的全基因组关联分析, 再结合高生物产量材料‘CQ45’和低生物产量材料‘CQ46’的转录组测序(RNA-seq)结果, 整合了6个甘蓝型油菜材料6个部位(茎秆、叶片、花后30 d主轴与侧枝种子、花后30 d主轴与侧枝角果皮)的转录组数据构建的加权共表达网络分析(WGCNA), 筛选出与生物产量相关的候选基因。通过相关分析发现, 2年间甘蓝型油菜自然群体中生物产量对大多数产量相关性状都具有正向效应; 自然群体2年生物产量分析的最佳模型均为K+PCA模型, 共检测到9个显著位点(P < 1/385691或P < 0.05/385691); 根据CQ45和CQ46共36组转录组数据, 选择MAD值为前5%的基因共计5052个用于构建WGCNA, 通过筛选合并共得到了15个模块, 其中5个基因共表达模块分别与叶片、茎秆和花后30 d种子显著性相关; 整合了WGCNA中关键模块的hub gene、GWAS分析得到的显著SNP位点和极端表型差异基因确定候选基因, 它们的拟南芥同源基因为HCEF1、HOG1、SBPASE、ACT2, 这些基因在光合作用的卡尔文循环、碳同化、物质积累等方面发挥重要作用。

关键词: GWAS, WGCNA, RNA Seq, 甘蓝型油菜

Abstract:

Biomass yield is especially important for Brassica napus, as it is the basis for high yields of crops. In this study, the phenotypic data of the natural populations composed of 588 materials were used for genome-wide association analysis (GWAS). We performed the transcriptome sequencing (RNA-seq) of biomass yield using ‘CQ45’ (high biological yield material) and ‘CQ46’ (low biological yield material). A weighted gene co-expression network analysis (WGCNA) network was constructed by integrating transcriptome data of six tissues of the extreme materials, such as stalks, leaves, 30 day after flowering (DAF) seeds of main inflorescence and lateral branch, 30 DAF pod keratin of main branch and lateral branch. We finally screened the candidate genes related to biomass yield. The main results are as follows: Biomass yields in B. napus had positive effects on most yield-related traits; K + PCA model was the best model for biomass analysis of the natural population, and nine significant loci were detected in the best model (P < 1/385691 or P < 0.05/385691); according to 36 groups of transcriptome data, MAD value of each gene was calculated. A total of 5052 genes with MAD value of the top 5% were selected to construct WGCNA. Fifteen gene modules were obtained, among which, five genes co-expression modules were significantly correlated with leaves, stems, and seeds of 30 DAF. The hub genes of the key modules in WGCNA, the significant SNP loci obtained from GWAS, and the extreme phenotypic differential genes were integrated to identify the candidate genes. Their Arabidopsis homologous genes were HCEF1, HOG1, SBPASE, and ACT2, which played the important roles in the Calvin cycle, carbon assimilation, and material accumulation of photosynthesis.

Key words: GWAS, WGCNA, RNA seq, Brassica napus

表1

自然群体2年各产量相关性状间的相关分析"

性状
Trait		 环境Environment 粒果比SWSI 千粒重TSW 每角粒数SNPS 茎秆干重ST 上部生物产量CBY 籽粒产量SY 收获指数HI 生物产量BY
SWSI
SWSI		 2017CQ 1
2019CQ 1
TSW 2017CQ 0.506** 1
TSW 2019CQ -0.008 1
SNPS 2017CQ 0.513** 0.156** 1
SNPS 2019CQ 0.284 0.056** 1
ST 2017CQ -0.023 0.108* 0.104* 1
ST 2019CQ -0.055** 0.213** 0.198** 1
CBY 2017CQ 0.0247 0.106* 0.072 0.682** 1
CBY 2019CQ -0.037 0.249** 0.171** 0.567** 1
SY 2017CQ 0.204** 0.155** 0.210** 0.524** 0.773** 1
SY 2019CQ 0.179 0.327** 0.409** 0.424** 0.726** 1
HI 2017CQ 0.308** 0.127** 0.232** -0.099* 0.142** 0.687** 1
HI 2019CQ 0.033** 0.292** 0.262** 0.556** 0.972** 0.866** 1
BY 2017CQ 0.007 0.117* 0.089 0.861** 0.957** 0.738** 0.056 1
BY 2019CQ -0.042 0.313** 0.265** 0.752** 0.905** 0.718** 0.901 1

图1

生物产量在各模型下的QQ图"

图2

生物产量的曼哈顿点图"

表2

最佳模型下生物产量显著位点表"

染色体
Chr.		 物理位置
Position		 位点
SNP		 P值
P-value		 贡献率
R2 (%)		 置信区间
Confidence interval
(250 kb up/downstream)
A3 7901059 S3_7901059 9.91E-07 7.25 7651059-8151059
A7 15487057 S7_15487057 1.06E-06 7.70 15237057-15737057
A7 16899362 S7_16899362 1.46E-06 7.43 16649362-17149362
A7 19665408 S7_19665408 1.73E-06 5.99 19415408-19915408
C1 3258674 S11_3258674 1.46E-06 7.65 3008674-3508674
C3 20691876 S13_20691876 1.87E-06 5.64 20441876-20941876
C4 34485253 S14_34485253 1.39E-06 7.51 34174883-34735253
C6 26169577 S16_26169577 2.87E-07 7.98 25919577-26419577
C9 23643765 S19_23643765 1.14E-06 7.61 23393765-23893765

图3

高生物产量材料CQ45和低生物产量材料CQ46转录组分析 A: 极端表型材料各组织差异基因数量; B: 极端表型材料各组织差异基因的GO富集分析; C: 极端表型材料各组织差异基因的KEGG分析。"

图4

样本聚类与基因模块的生成 A: 样本聚类与数据矫正; B和C: 基于规模独立性和均值连通性选择软阈值; D: 已识别模块的树状聚类图; E: 已识别模块的热图。"

图5

基因共表达网络模块与各组织的相关性分析 A: 基因共表达网络模块与组织部位关联热图; B: 各样本中Turquoise模块的所有基因与相应ME的表达水平; C: 不同模块两两之间ME的相关性; D: ME聚类树。"

附图1

各样本中关键模块的所有基因与相应ME的表达水平"

图6

关键模块的基因共表达网络以及核心基因"

表3

生物产量候选基因"

基因
Genes		 染色体
Chromosome		 物理位置
Physical position		 拟南芥同源基因
Homologs in A. thaliana		 功能注释
Functional annotation
BnaA04g04350D A04 3211999-3214328 AT3G54050 高循环电子流
High cyclic electron flow 1 (HCEF1)
BnaA04g06420D A04 5053017-5055052 AT4G13940 同源依赖性的基因沉默
HOMOLOGY-DEPENDENT GENE SILENCING 1 (HOG1)
BnaA07g19320D A07 15496197-15496586 AT3G62410 CP12含域蛋白2
CP12 domain-containing protein 2 (CP12-2)
BnaA09g35380D A09 25808955-25811193 AT3G55800 景天庚酮糖二磷酸酶
Sedoheptulose-bisphosphatase (SBPASE)
BnaC03g33610D C03 20464104-20466211 AT3G04120 甘油醛-3-磷酸脱氢酶C亚基1
Glyceraldehyde-3-phosphate dehydrogenase C subunit 1 (GAPC1)
BnaC03g73810D C03 1811021-1813439 AT3G18780 肌动蛋白2
Actin 2 (ACT2)
BnaC08g48810D C08 3782204-3784813 AT3G54050 高循环电子流
High cyclic electron flow 1 (HCEF1)
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