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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (8): 1491-1510.doi: 10.3724/SP.J.1006.2021.04175


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 Online:2021-08-12 Published:2021-01-11
  • Contact: LI Jia-Na E-mail:hawer313@163.com;ljn1950@swu.edu.cn
  • 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)


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

Table 1

Correlation coefficients of two-years yield related traits in natural population"

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
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

Fig. 1

Quantile-quantile plot of biomass yield in six models"

Fig. 2

Manhattan plots of biomass yield using the optimal model"

Table 2

Significantly SNPs for biomass yield using the best model"

R2 (%)
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

Fig. 3

Transcriptome profile of extreme phenotypic materials CQ45 and CQ46 A: the number of differentially expressed genes in tissues of extreme phenotypic materials; B: GO enrichment analysis of differentially expressed genes in tissues of extreme phenotypic materials; C: KEGG analysis of differentially expressed genes in tissues of extreme phenotype materials."

Fig. 4

Sample clustering and gene module generation A: sample clustering to detect outliers; B and C: the selection of the soft threshold based on scale independence and mean connectivity; D: cluster dendrogram of the identified modules; E: the heatmap of identified modules."

Fig. 5

Correlation analysis between gene co-expression network modules and tissues A: association analysis of gene co-expression network modules with tissues; B: expression levels of all genes and corresponding ME in turquoise module of each sample; C: ME correlation between different modules; D: ME cluster tree."

Fig. S1

Expression levels of all genes and corresponding ME in key modules of each sample"

Fig. 6

Co-expression network of key modules and hub genes"

Table 3

Candidate genes of biomass yield"

Physical position
Homologs in A. thaliana
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
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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