Deciphering the Genetic Basis of Root and Biomass Traits in Rapeseed (Brassica napus L.) through the Integration of GWAS and RNA-Seq under Nitrogen Stress

Abstract

An excellent root system is responsible for crops with high nitrogen-use efficiency (NUE). The current study evaluated the natural variations in 13 root- and biomass-related traits under a low nitrogen (LN) treatment in a rapeseed association panel. The studied traits exhibited significant phenotypic differences with heritabilities ranging from 0.53 to 0.66, and most of the traits showed significant correlations with each other. The genome-wide association study (GWAS) found 51 significant and 30 suggestive trait–SNP associations that integrated into 14 valid quantitative trait loci (QTL) clusters and explained 5.7–21.2% phenotypic variance. In addition, RNA sequencing was performed at two time points to examine the differential expression of genes (DEGs) between high and low NUE lines. In total, 245, 540, and 399 DEGs were identified as LN stress-specific, high nitrogen (HN) condition-specific, and HNLN common DEGs, respectively. An integrated analysis of GWAS, weighted gene co-expression network, and DEGs revealed 16 genes involved in rapeseed root development under LN stress. Previous studies have reported that the homologs of seven out of sixteen potential genes control root growth and NUE. These findings revealed the genetic basis underlying nitrogen stress and provided worthwhile SNPs/genes information for the genetic improvement of NUE in rapeseed.

Genome-wide transcriptome analysis unravels genetic variants associated with root and biomass-related traits under low phosphorus conditions in Rapeseed (Brassica napus L.)

“…Crops with enhanced PUE lead to higher growth for the same amount of phosphorus taken up at a time [51]. Several studies have explored the genetic basis and function of roots in rapeseed under low phosphorus, and several loci have been reported across various populations and environments [52,53]. Based on 20,131 high-quality SNPs in a 327-rapeseed association panel, 70 SNPs (39 signi cant and 31 suggestive) were linked with root and shoot biomass parameters that were further integrated into 11 reliable QTL clusters (Table 3, Additional le 2; Table S1).…”

“…Over the past few years, RNA-Seq has been extensively used to investigate the transcriptome of a wide range of economically important crop plants under P de cit conditions [58,59]. Comparative transcriptomics has been utilized successfully to investigate the fundamental genetic mechanisms in rapeseed [28,53]. RNA-seq analysis was performed to investigate the molecular mechanisms involved in rapeseed seedling response to low phosphorus stress.…”

“…The integration of GWAS, WGCNA, and differential gene expression analysis is a promising strategy and has been utilized in many crops to explore potential candidate genes [53]. We identi ed ve hub genes by integrating GWAS and WGCNA data.…”

“…For example, BnaA04g23490D (AtSTR7), a major hub gene located in the region of QTL cluster RT-A04-1, encodes phosphatase superfamily protein and is involved in primary root elongation in A.thaliana [65]. BnaA09g08440D (ATNPY2), another hub gene in the QTL cluster RT-A09-2, has been involved in root growth and development [53]. Four potential candidate genes out of seven were identi ed through the integration of GWAS and differential gene expression analysis that has been reported to function as central regulators in root development and phosphorus stress.…”

Insights on Phytohormonal Crosstalk in Plant Response to Nitrogen Stress: A Focus on Plant Root Growth and Development

“…In the past four decades, agricultural food production has doubled worldwide, and nitrogen (N) fertilizer use has increased seven-fold. Plants require N as a major macronutrient, and its availability has been recognized for years as a critical factor in crop production and food security [ 1 , 2 ]. The overuse of N fertilizers results in a 25–50% decrease in plant N uptake efficiency and a 60–70% decrease in plant nitrogen use efficiency (NUE) [ 3 , 4 ].…”